Python_Aula_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jpscard/I2A2_AI_Industry_Desafios/blob/main/Python_Aula_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Python - Aula 1"
      ],
      "metadata": {
        "id": "DEBb3Vs90gan"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Montando o Drive do google para acessar nossos arquivos "
      ],
      "metadata": {
        "id": "p-F46-qe3jb5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "from google.colab import drive\n",
        "from google.colab import auth\n",
        "from IPython.display import Image\n",
        "\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_xj1_zCeWGV",
        "outputId": "39bc5433-3cc7-4bd7-d497-5c2ca11b6d2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lista os elementos dentro do diretório. \n",
        "!ls drive/MyDrive/i2a2/dataset/\n",
        "\n",
        "# Aqui temos 3 elementos:\n",
        "\n",
        "# ! -> Informa ao notebook que estamos rodando um comando.\n",
        "# ls -> comando que lista os itens (arquivos/diretorios) em uma pasta.\n",
        "# 'caminho' -> pasta que queremos listar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ttwf21lEemFr",
        "outputId": "3d03b06d-992c-4b86-c1d1-cdf32da5b511"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "image_2_insert_listas.png  indice_lista.png  indice_string.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upload de arquivos"
      ],
      "metadata": {
        "id": "9tol7-RVpzPj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "dM0GOFnSp19m",
        "outputId": "b4064329-a2b5-4831-d1c3-56633953005b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-488cd4c3-77fd-43a3-b112-2a255613a2d5\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-488cd4c3-77fd-43a3-b112-2a255613a2d5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Hotel Reservations.csv to Hotel Reservations.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Variáveis"
      ],
      "metadata": {
        "id": "DhOMHzOynDJJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Variáveis são elementos que armazenam uma informação para que possamos utiliza-la em nosso código posteriormente.\n",
        "\n",
        "Tipos de variáveis no Python:\n",
        "\n",
        "* Inteiro (int)\n",
        "* Ponto Flutuante ou Decimal (float)\n",
        "* Tipo Complexo (complex)\n",
        "* String (str)\n",
        "* Boolean (bool)\n",
        "* List (list)\n",
        "* Tuple\n",
        "* Dictionary (dic)"
      ],
      "metadata": {
        "id": "cDdQ5U8Z5FLo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definindo uma variável."
      ],
      "metadata": {
        "id": "o6XDjXcV5kRJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aluno = 'Diego'"
      ],
      "metadata": {
        "id": "k-IBYGhM5olb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(aluno)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2gNbWhn6ggR",
        "outputId": "a685cf57-477e-4e2b-f56e-6ba6dc1cf85c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diego\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idade = 15"
      ],
      "metadata": {
        "id": "6gg6yMzO6r-a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(idade)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBUh3zmk608P",
        "outputId": "07f22c43-def7-489f-b07e-a7e7ca458397"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'O nome do aluno é {aluno} e ele tem {idade} anos')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_0fOwW362st",
        "outputId": "7706cc98-1b71-436a-ebe2-70952375bd21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O nome do aluno é Diego e ele tem 15 anos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No Python as variaveis são dinamicamente tipadas, ou seja, podemos mudar o tipo do dado contido em uma variável durante a execução do código."
      ],
      "metadata": {
        "id": "b6H9sOgf7rW3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aluno = 'Diego'\n",
        "print(type(aluno))\n",
        "aluno = 15\n",
        "print(type(aluno))\n",
        "aluno = 7.4\n",
        "print(type(aluno))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCbjXz2R8Cly",
        "outputId": "9d64f627-27d2-4ede-d886-643ed3398636"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'str'>\n",
            "<class 'int'>\n",
            "<class 'float'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Booleano* (bool)\n",
        "\n",
        "A variável é um operador boleano (verdadeiro/falso)(1/0)"
      ],
      "metadata": {
        "id": "Stkpl4cTAbFZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matriculado = True"
      ],
      "metadata": {
        "id": "UFisSyGl8GTo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(matriculado))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBfvPr3MAh0L",
        "outputId": "ab5e3f29-9d2f-48b1-e9ed-a8fc2bbd12a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'bool'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lista (list)\n"
      ],
      "metadata": {
        "id": "tnkiQehCAob-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "No caso das listas, seu nome ja entrega sua funcionalidade. São objetos que armazenam informações na forma de lista. <br />\n",
        "Por mais que esses objetos permitam que você armazene as informações de tipos diferentes em um mesmo objeto, é comum separarmos as informações que colocamos em cada uma."
      ],
      "metadata": {
        "id": "v_e_VFC4kqrt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista_de_alunos = ['Pedro', 'Diego', 'João', 'Celso']"
      ],
      "metadata": {
        "id": "SPuEHB_FAryX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(lista_de_alunos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jl7Ul9G8D4Ho",
        "outputId": "6fd65980-fbff-4839-eb76-5bffec643ae4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Pedro', 'Diego', 'João', 'Celso']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(lista_de_alunos))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dy1KLkPLBftU",
        "outputId": "26ac8d2a-a519-401e-a300-87b04744eb06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Não é o ideal mas funciona.\n",
        "lista_teste = [\"Celso\", 45, \"Av. Independência\"]"
      ],
      "metadata": {
        "id": "P0HKOWef8eLf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lista_teste"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmW29t6w8m-G",
        "outputId": "6880e5da-9c01-4fe8-d114-210e46f155ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Celso', 45, 'Av. Independência']"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Acessando os item em uma lista:"
      ],
      "metadata": {
        "id": "PDXNSl2yBk_9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quando criamos uma lista, os itens são posicionados seguindo um indice que é criado pelo proprio Python. Esse índice sempre irá seguir a ordem em que os itens foram inseridos."
      ],
      "metadata": {
        "id": "Z2bmyxa-T_-r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Image('drive/MyDrive/i2a2/imagens/indice_lista.png', width=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "VMdF9qkhyA0n",
        "outputId": "016d27d5-85aa-43be-fe4b-5733b53d174c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 500
            }
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos utilizar esses indices para acessarmos os valores armazenados na lista."
      ],
      "metadata": {
        "id": "v90RWdYNUgGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista_de_alunos[0] "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "XTHZy-5vBoFL",
        "outputId": "a8859993-f3c3-4e84-a5b1-1a2d3c0fc8a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Pedro'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adicionando um item na nossa lista"
      ],
      "metadata": {
        "id": "NSD2fH3FEnOS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista_de_alunos.append('Marcos')"
      ],
      "metadata": {
        "id": "EA8zz6BJEqa9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(lista_de_alunos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIRpUunrEzK0",
        "outputId": "cf0c962b-d512-4c61-8418-012065d0f672"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Pedro', 'Diego', 'João', 'Celso', 'Marcos']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lista_de_alunos.insert(1, 'Maria')"
      ],
      "metadata": {
        "id": "0jruyDvZE9Nx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(lista_de_alunos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UeU2HJr-FP8U",
        "outputId": "1149d234-12f0-4eac-d096-6ee0d08c0723"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Pedro', 'Maria', 'Diego', 'João', 'Celso', 'Marcos']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Image('drive/MyDrive/i2a2/imagens/image_2_insert_listas.png', width=600)"
      ],
      "metadata": {
        "id": "WzOkOFdmZZ3P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "72343b0d-3801-4301-bef8-7fc6973ddd1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 600
            }
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lista_de_alunos[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "U81O0p_P9ySR",
        "outputId": "ebf89d50-3715-40b5-a9d6-cde32bf749fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Diego'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Strings"
      ],
      "metadata": {
        "id": "c6FNbzZfy2Bp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aluno = 'Diego.pdf'"
      ],
      "metadata": {
        "id": "W8741zEry3ds"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(aluno)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsJNSYe_y5re",
        "outputId": "94e9e8d5-699e-419a-97b4-dc0ecf9588fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diego.pdf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Image('drive/MyDrive/i2a2/imagens/indice_string.png', width=420)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "cV2ZXoHdzN2G",
        "outputId": "327413a6-c2fd-4bc0-e974-e01d58ff57f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAACUCAYAAAAtSgahAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAACodSURBVHhe7Z0JvFVV9cc3IIphiZhEggQqASoqgiOIgkFlIIOKgoEgoBlJiIQDglIqagKJYQyOTApOGAgqToiSIyiZYChoDmiKOFRkied/vqu73/9wue9x3+MN+1x/389nf959d3jv7HvOWWuvtddQLYpxQgghRMBUz/wUQgghgkXKSgghRPBIWQkhhAgeKSshhBDBI2UlhBAieKSshBBCBI+UlRBCiOCRshJCCBE8UlZCCCGCR8pKCCFE8EhZCSGECB4pKyGEEMEjZSWEECJ4pKyEEEIEj5SVEEKI4JGyEkIIETxSVkIIIYJHnYLFDvHxxx+7999/33311VeZZ4QQIjfVqlVz9evXd3Xr1rXHpUHKSpQZFNS9997rpk2b5jZv3px5tjDgtvC3BjdVaW+sENGc0oFf+BXKfMCfp1133dWdc8457uSTT3bVq5fOsSdlJcrMli1b3I033mjKatCgQW7PPffMvJJ+3nrrLXfLLbe4d99915166qmuU6dOpb65QmPlypXu9ttvt8d9+/Z1rVu3tsdpBaH+6KOPurvvvtv961//svn069fPVu1p5YsvvrAF4IMPPuhatWqV+vl4Xn75Zbuf/vGPf7if/vSnbsqUKa5mzZqZV/NDykqUGa+s5s6d62bOnOkaNmyYeSX9INh//vOfu9dee8395je/cT/72c9cjRo1Mq+mk4ULF7qhQ4fa40mTJrkuXbrY47TC9YfQGz16tAnB7t27u4kTJ7oGDRpk3pE+/vnPf7qxY8e6G264wXXt2jX18/EsWrTI7qcPPvjArru77rqr1MpKARaiXNhpp53s4iuk4V0wKKlcr6dtMA/vWiqkOXmwfHO9J02D+8hb8IUwHz+Y1466NKWshBBCBI+UlRBCiOCRshJCCBE8UlZCCCGCR8pKCCFE8EhZCSGECB4pKyGEEMEjZSWEECJ4pKyEEEIEj5SVEEKI4JGyEkIIETxSVkIIIYJHykoED40BqEb9l7/8xT3yyCNu8eLF7tlnn3UfffRRQTR9ZH6ff/65+9vf/mbjnXfesd/TAN//p59+ai0gaNexZMkS9+KLL7pNmzYV9ZlKKxw/86PBKFX4mR+tO55++mn39ttvu//+97+Zd6YX5sf19tJLL9kcGXQa+M9//pN5RzhIWYmgQWg/8MADbvDgwa5Pnz7WqoNWAwMGDHC9e/d2v/vd76xTcVoF45dffmmC/pe//KU75ZRTrCkdPYwWLFiQeUeY0Hfp1VdfdVdffbXr1auX9SiiqR7n58wzz7Tnrr32WusLlrZzw/HScuRPf/qTu/jii+280P+L+XHtDRw40J7j8WOPPWa9tNIKSnfkyJE2P647xqhRo2whGBzxiRGiTMSCNpo0aVLUtm3bKLYIMs+WH/GKPYqFYdSkSZOoZs2aSLytRvXq1aO6detG/fv3j9asWRPFq8TMJ3ecFStWRG3atIl22203myNzLW9igRjdf//9Ubt27aJddtmlaF516tSJJk6cmHlX+cH/2meffWzwuKzEgiyaOnVq1KpVq+ib3/xmVK1ata3OC4Pndt9996h79+5RbGlFW7ZsyXy6/PDXH+eI/9mzZ88othIyr5YNrqHVq1dHw4cPjxo3bhx94xvf2GZufnDOmjZtGsULpii2JDN/YceIF2f2v2vUqFEu8ykJrr9YGdv3t9NOO0U777yzzeuQQw6J4kVG5l3lw8KFC6MGDRrYvLp16xbFllvmlfyRZSWCBDfEvHnz3O9//3u3fv36nC4X76Kh+SNN6v7+979nXgmfjRs3WufUCy64wC1fvtwslTSAywhr9tJLLzWLEMs3liOZV/8fehdhneCyHTdunHVcTgO4mi+88EJrKopVWJzV5HszvfHGG+63v/2tdffFSk4L3Du4bO+88047T7Vr13bxwiPobthSViJI8JvTgh0hl7yBEBL87oUFbN682VqbP/zww8ELDITE2rVrrfsw4/XXX7fn0gBKCbfRrFmz3IcffrjVcfvz4s8Vv++55572mL2e0N2awPxYNLz55pv2M6mEk/Pz115sdbn69etb99vZs2fboiotcA3SZZk9UmDfkQVUyNeilJUIDqyqp556yq1YscI6we6yyy4mIJo2berGjBljq96TTjrJuo96uNHmz58ftHWF8HvmmWfciBEj3PTp003gpwnOQfPmzW3PhlU4cH6aNGnizj33XDdt2jRrx37cccfZXBl169Y164vz+cknn9hnQoX5HXTQQW7YsGFFihaF1LJlSzd8+HCbH+Pss892u+++uwX97L333nYdvvLKK2aVpQGCX+bMmeOefPJJt2XLlsyz4SNlJYID1x6b21hMderUMWsJoYFAPP/88y244pJLLnFt2rQxAeNbZhOlFfKGPq5MgkWwNHK5l8qj9XdFs8cee7hBgwa5/v37m0D/4Q9/6G6++WZ35ZVX2ib9WWed5S666CK31157mVDk/GGNvPfee6lw07Iw6tatm11j+++/v/vFL35hrjIWScyPQdAFiyWuSwbnjcUSQQmhXnseLCeU1K233mr3V5qQshLBgZBjPwR23nlnEwBYVcccc4ytaHmuVatW7qijjnI1a9a09+26666m5FatWhVk2C1wrKeffropWe8uq1WrlmvYsKEpKV73z4cMrq+hQ4eaG/O6665z7du3N6XEeWE+jRs3tvewavcW1r///W8baQBrkKhGBDqLohYtWrhvfetbNj8GrzM/5oXA59zxOA1WCu4/rN9ce4ihL5SkrERwfPbZZ2YhIbi9Mtpnn31co0aN7DHw/IEHHmhCBFcUyopVI+HUoea/IAxwo7GBj/LFMiH8GQXGHFihp0FZAVYHAh1BzrEnYb+HcwgIcwQ55wdFlhb23Xdf165dO1sceSHOPLjG2Ld7/vnn7TmCE7wHgPMZssDHbck+FS7ZXHtTKOKQrz8pKxEU3ES4VLwA8KtzlBKCIwn7BbgHucG40XgfSi7kZE2U7PHHH+9+/etfu2uuucaiAbGsIPSVbTa5jpfvHut2w4YNdl78pv13v/tds0bSBMKdubzwwgs2EPJEcJKXRAQn1xuuTa5RlHazZs0ynwwPjnHhwoUWtZiMPOXa4z7jXIa+WJKyEkHBTYU7D0HATYRLD2HHypz9hCTsn/jVIDcan0lDVQvm0r17d3fGGWeYS6mQWLdunbvttttMIHIeEPgsNI444oiioIy0QKTmkCFDbH+KQaIziooqFl7go5y//e1vWzJ3qMqK+2L16tUWHELqgYf7C+uYhQRwH4W8YJKyEkHBjeVdRygglBU3EQI+292EW4nnuMH8ipDAhdCVFaBk0+QWywcWGeTFERzD+WMAQpxAjLRZjigiLCesRAaVUthPTe5NcY2yZ0c1Fc5piBCWPnPmTLMG/b3BcaNgu3TpUnTcoZ8fKSsRFAg478bjMTcXiiiXIEiuBL2yCtkFWMiwd0Ne3Lx582yx4cF1SwTd97///cwz6SaXQCdXiZSEEHP8OCbqaZIblwxwOfjggy0FwVtVaUDKSgQFwiCpmLCcWMkm/ewenverd/+TPaG0reDTDtYseyFUEUnmjuG2pWbgaaedto1VnAZYAFHZwQ+se67N5FxQBhTuveKKK8yiDI2//vWvbtKkSZY64CGtgPNC/liuxR0LRDwajJAWf1JWIihQNAgGfqKgvOJitZ50vwArRf+cV1YEXEhZVR4Is0WLFlmeFfsh/jzgZurQoYMVf2VPJ40QgUrO2IQJE9z48eNtjuT5nXjiiWaR+OuMBRJVy6nS4aMgQwD33+TJk4siF4Fj3m233WwvccaMGeYezM4Pw51LbtlNN93k7rnnnq0s5apEykoEBatWQoBZ1XLTIPS4wbhhsvN0eN0HYLDC5X0IxjSu4tMICwWCDYhsJBjBCzy+/8MOO8ySZ4mSSys+cIJEZ1xm5513nhs7dqy5Owm04HUUVYMGDewaxLIKqVo5x8P5Sd43nCPKQpEjR1WOyy+/fCurC8jB8q+jqHHxhoCUlQgKlBRCgL0ObhK/D0CpnuxyPazkWSFyAyI4UVaE4qLgRMXCd7506VJzf1FmyG/ccw7IJRs9erQlcadx4eCVLnPh+Lme/MDSJwq1Y8eObr/99rPrlWhH3ksQBtdjKFCzsDhFwyIPzwXDzzeJdwMyQkHKSgQHiooNeW4ibjYEIflTFBj1sFokARi3C+/jpkJwkCicKxhDlB8sDHAtoaio35hUVCTTYlF17tw5lYsGBDxlu7iu/LxygZD3gpzrDrCy/GNR/uibFcGBGxA3EsLPr/xoxfDEE0+Y6w+XIJ2CcXMgUAgBR7iwwmXTGKEhKg6KtuImIkk2GQHH94+77PDDD7eQbyzf5MBFlr3vGBJcZyyIcPdRE5AoOiL9sOi5DpkriyTcZLyG6xOFzPN8liR1gjBCgfuCYIp69eqVOLIXFfzOPchr3iUfAlJWIjgQekcffbS5A70wREhMnTrVVvP0U2KfhPqBCAkvSPgMm+IoOVExUGqIDsAPPfSQLRSS3zWLBPpX0fWYyuTZg2r55CmFDMqUOVKVnKRtivYSWEGZIgr2cu0RZOH3cvAC4P7DXUjiM8I9FKhBedlll1n9xuIGe1bsuSXPI0qXbsH06WLfKphk7vhmF6JMxAqiwjoFxwIj6tGjh3UD5jItbsQ3mY099tgjmj59ehQrrsxf2DEqo1MwxFaidZqNV7NRrKStI3KdQDsFx4I8euCBB+y75nvhvPM413nJNegQy3ktL/z1x7Hw93e0s26sfKOnn346ihXONseea3Bt8r+5/po1axbF1tYOd6uuzE7BEFvA1hmYOfjrT52ChSgFrO4oBXPAAQeUaCnF17C5Xrp27WojzftVWI8hu8nAu2UhGQBTKODyytfthWVJQAXX6uDBg82yL+laDZ3Qrz8pKxEkCAya+NE+nf0r8qey4T24Ck899VTLh8HHnmbYsEcApgGENFGAxUWbpRXq5XG95drLSYJSwj1GqxquUfbqcl2jaSL060/KSgQLFRB69Ohhex1YWYQLH3roodbNlf0BXmOjn+rl1J9L46oWhfu9733Poueon8dgnvSEChE27Dk+jveEE07Y6ri3NxDs2cWIQ4Lrh+uIyupcU1R5YK6tW7e2KFNyxihThAVFIWKiHul5RaNGlFwaQSEzn+R5IuUgpEARTzV8gZnHQpQKXAYokrlz57o77rjDghsqCiol+G6zPCYIg41hhERFKKmVK1daUMCaNWvcVVddZX2nKiJniNuP6EZav3uYD1UGynul/sc//tGi3ICCs1QSLy24iny6QGlBUWGNlNf36K8/GiRi4fXs2dNKC3FdlAdYGoSyE8XoCyTjZvbXHnMp72uPeRAUcf3111vH4vKcTy6YE0EvSXcuCoxcsnzdoflAh2yqmVAMmOK5d911V6mjdmVZiVTAhY0FQlg0Kz+sK26oNFpTSTh+lNJ3vvOdooELKlSXEuHQHF/yePMdLCwqQuFXFCgmFmBYhG3btnXHHnusO/LII4s6B6f92gMUEhGMyfMUUrh6koJSVuRyEM6cHKxUZDwKIUS6KShlhZ+ZFRArbwYdWclGT8umtRBCiNwUlLJiLwPfsh9ELBVaaK0QQnwdKcg9K/zqaY3OEUIIsS0FqazYxC1tpIkQQohwCV5Z4dqj+yj1uqi8Tegj4bMhgIuRwqocG8UtfbuKfKASAGGxzImQbEKXFQgihBC5CTLPioAIIvuosk1EH8IcRcDzhPTSpZPkUJISk4VLSRydNm2avYdBtWTabTdp0sS6Yib3r4466ih3yimnFH0WZUHsP1n5HsJTKWSJgpw3b17R50mYa9SokSkb2kbTkRNrrn79+pZA+KMf/Wib4pDAV43ifeyxx9xzzz1nc2RvjRBZKjEQjk1SHn1ySsqeT0IuyJ///OeidgWlhTnSjqMslmhl5llVNpWVZ1WZlEeeVUhUdJ5VVVDZeVaVRXnkWSFAgyI+WVGsGKIOHTpE9erVs8KHHGZyVK9e3V7r3Llz9NBDD0Wx9WWfjb8MK8hYu3btaK+99rKijPENGi1evNgeJ//GwIEDrTCnZ8OGDVF88271nr333juKlUo0e/bsrT7PMVHsMlZaW73fHxeFGpcuXbpVUUsKrD7++ONWnDVWatvMi+OuU6dOdPTRR0dTpkyxgpb5EFt0Ufv27aPmzZuXerRo0SLq379/FFuHmb9WOmLlXWGFbKuayipkW5nsaCHb0PDXX3kVsg2Byi5kW1kUXCFbLBcsGFZKdCGlWgGrp1xgzcTC3zqS0oQvnkvmleKJv6i8Vsfb0/gc08aNG63ygIckOiwkLEBWEazGvZWGK3PJkiVuxIgRbsGCBba6yJ4Xx48l+Mwzz1gJocmTJ1ulgO2BRfXaa6+ZBVCWQb+efL47IYSoSoJSVghd+sasW7cu80xuKNuCGw4XFo3gUHDFKbUklEehjE22ey4J7jcy9Et6Ty5wUfK3qRLOT5Ttww8/bPtrzIscMFxL28v5Qumx/4V7g95AKDohhPi6E5SyevDBB02woyhQSPyk/AdVteneyf4RhSQR4FgUlAXh5/Lly/MW6vkooXwVFUoN/yvHx74PwRZYTRQh5bjoZMt+G9YUFhN7XX5eKDT23IYMGWKfZx8OUFYoYYI2brvtNmvstj34ezsyhBAidIJRVgQ4ECjAT6wPhD3uOFxndLQcM2aMDTpbUhMONxwRdbj1cJcRqFCZoJDoVstGNd1DaROAtYfrEnCtoWgYBHlg+dFVlONFUfXv39/94Q9/2KrzLZ/nc7wPlyLKju+kJKvR/y2UXlkG1aNDrLAshBBJgokGfPPNNy0KZtmyZaZ42JNCKY0fP96sK17HPbhq1SprqY0lhTVCxWCsGqwX2k9nRwPec889pvSIfKpdu7b9L/72WWedZe/FkgEsIqJVFi1aZFF9uOL4+/Pnz3dr1641hYDS4OtiEHmEy5KWCbB69WqLHsOCatiwoYWkYwWiENiDQ/ny/4kGbNmypZs9e7YVxPQQBUS7bKwp3JVYVxwDCvqCCy6w+eSCY2LvyUcqlhaOiWjJfPbysuF/466cOXOmtcDmeysU2Aflu+e6GzZsmOvTp0+QxT1LA1Go7IcCc+vQoYM9TissarmPJk6caDKDdiXca2m+DtnzvuGGGyx6+Qc/+EHq5+Mhshv5TgV7rjsW6MhlFv0szPMhGGWFsEVhoBjuu+8+UzK41RCmvoMlFyc/GUCoNO/Zd9997TMIzIpUVnyem4JjPffccy2s1H9+/fr1pqw4KXyOsPT999/fLJepU6cWKRtO1sknn2wXY/ZJ4n1cnPx9juH11193ffv2tf+D8soFx01YNd9DacEFSAoA1irWXGnxygoBSLfUkHsVlRbOM+eUIBoWHyyY0u4y5X5A+XLLIyRYDKYZ5oEnA5c5soGqNaURfiHCPJA9eGQKYT4eZC73E0aGT5fBo4N3iX3+fAgqzwoBcfPNN5vQzifCr7KVFblQ/E2UydChQ8096UEIoKyIUPSf57hYRdx5553mrgM+Tz4Yx5qdS8Uc2JvDFYpwfOONN6xVO9ZWceWj+L/kjJHzVVoQvhwfOQ9169bNPJs/XlnxPQ4YMMD2EAsFLGO+d/YcycdjfzHtltVLL73kbr/9dpsHjQVpfZFmkA+PPvqo3eMsKshxZHGX5lJreGDYNiA4i/OT9vl4yJflfkL2Mi8W+ygrrEf2/vMCZRUCseCL7rjjjqhJkyaWc8Tg8LJHjUR+EvkisQKKWrZsGcVWyHbzrOKTboP3kGcVK53Mf4+iWChFsWKIYgUSNWrUyN6TnWcVKyHLreLxiBEjMp/8H/GqIerUqZN9Pl6J2+f3228/+z+xorLPMmrVqhWdf/75RblhSeKbzj4brzyieOVheVvkJGzatCnzjm3h/8aKbavvKN/BMXbs2DHauHFj5q+VDr4/5VmlB+VZhY/yrIonmKUilg0h6OT94J7wbi9cS+wL0XiPCg+0YOY59lj46Ve78VzsZy5ioVzkwkGb8xmsI4aHYA0G78MULw/4W7jXkityrBH2rXL9D1yE2aWkWFUlPy+EEF9HgpGC7M8QpIBrjHJDbDTimurXr5/lKLGJT2g7UXe48xgoNBQCiqo4BcPrVGHnpw9y4LFXTh4UBQOfKq6fkpRfvvB/CF7ADcn/QunwdwmI4H8kwQXqoyH5DpgPn0dJZ7sLk+DPbt++vW0ul3ZQ2umII44oCJ+4EKKwCUZZIaQZWEwoKYQ1AQpEwrFf1K5dO1M2RP/wPl5HAfBze8oKa4qB1cJeFkqDTVmsOD7HJi3RhChMKO5vlRb+d9OmTW3/ifB6/i+KgXqC7M0Rfs+xc1woYvzUWF4oYY6J9+LfLSlwgb9N+DwBG6Ud+JBHjhxZtJcnhBChEoyyQplgAQFCHqVFYAPh6FhWF110kQVFsPmI9UOoN65DHiPgs91nSfjbCHXALcf/YbOZKLozzzzTnXHGGRYokE95o9KCG4/EYRQVwRX85NgJeycogTwyAi5QGihLb4WhkFFUzZs3LzGsnNfYoGR+ZRm4XPm+hRAiZIJRVoRoYoVgPaGkqDRMDhX7WOPGjTPr4cknnzT3oAfXmVdU7AN5si0tovhwdyGUsahwraEQaHlPxXDyT3ABbg9vxW2P5P/GkqNCBfttHC+D1/l/RB4SuknFciL/eJ7jwqpCyRGxxfcihBBfd4JSVj169LDwZ8KxyTNAuSC8fYWKpBIArCov+Kn2wHsBhZdUagRokNtEhQiUFX/f7wv5v5EPWDvJoIxc8LcIz/RKjTkccsghZkGxF5cERUvILcfr4fMkBQ8cONC+D29tCiHE15lglBXusV69eplQx6pCgGdbMQh+9rOOOeYYy1tC2PMeX+mcwAXgeRSXd2/xkzwZgjOoGoGCyHYborzYI+K9fmTDZ/jbuV4Dnud4UKzJv4FrD8WDO7Njx45FAR9J+B2XHhYYLk/26sjXEkII4VyNyylfEAgI8QMPPNCS+6gawO9s/pOcS4kiBP7w4cNd7969TZCjeHDvMbBaSBI+7LDDip5joBzYmyFIgaxpFB1WnE8cRjHyvsGDB1t0HK7Iww8/3J4j2fbYY4+196HI/PMMIvDYT/KgxLDSSAT27yHMvm3btmYpobAIGOFvYmnhmvQRjTzP+zgGko3Z40pDdQEU8/PPP2+tUEicLUsVjFBhP5SgG6x2FjqcexZUaYYi0bie4cQTT3TNmjWzx2nFX38kBrO4RR78+Mc/Lkp7SSPMgyo4zz77rMmXtM/Hw9bOwoULbb8eOYxhUtJefE7iEx4cX331VfTZZ59FscCI3n77bRsbNmyIPv30U0sE5HWaNJIsW9L45JNPtkk+47OxJRZ98MEHlnDH4HFsDdngM/7z/v/FSmibv8t7k8TKaptjYg58PhuO6eOPP7ZEZOb27rvvRh9++GEUW3xbNWwMHeampOD0oKTg8PlcScHFEuRSEZcY1ggWERYWA0uKFQbamNexSghCKGmw0seiScJnsZSIoMOqYvDYh7fzGf95//9wEWb/Xd6bhFV39jExh1yrB44JywmLkblRVw9LK5d7UAghREB7VkIIIURxSFkJIUQJsBdNEBdRyQwe81wU/S+hn4AtP77cTrSwKDtSViJoqKpPBft33nlnm8HzBEJQCYSNW8L+0wgpEb7cGNXz0zqPQgPFw3khD5LALoICCCSiYjidIXwlcfo0jR492n4uWbJkq1QUUX5IWYlgIcLylltuMeFA+5XsQeUPGlaOGjXKTZgwwVqdvPDCCyb80wLHSvUU+p4RoUfaAlF7rNpF1YHFRAk0rjEaqHIdooiIPJwzZ46llpC0T4fv66+/3nrO0TSRfE9ZVxWDlJUIFiwMivsiNBYvXrzNIBSWXmGsclnVUo6L8H8ECB2bUXYhg0J66qmnrGsq/dtIVqeiCnUjQz/2fGB+2SMN4OJ75JFH3IUXXmjVbbDaUUBcjwzODdYTKRsUL+Axn2HwWojzTON5yEbKSqQCIi0ZJUEyNjUfWeXSxHLWrFm2jxAqCD4S2bGuyLU7+OCDTfC9+OKLqVZWzAEXLXlqJMJffPHFtpi49dZb3SuvvGLnKWSBuW7dOmuVz7khQtd3PSDil8e58u1CjOLl+mKPjQUfTTfHjh1rCpjO3vyO2znk+yMbKSuRCvJRVoDQQBCuXLnS9hGwvEK+IUlfIGGdDgAkTgKpDGlNQKauJfs4lDcbNGiQu+qqq8w9hpv2V7/6le35XHHFFdZ5IESFjKLF3Yc7D0VFmgvXE2koRx55pOvWrZs7/vjjtzpHpNiQehISftFDgWxczLgtx48fbzVW6VLOHhzPUxMCCzENrkspK5EKUEIMBMShhx7qfvKTn9geD22xqS5BHh6v8R7y4MiRo2fYdddd55577jlbZYYGOXi0vunUqVNRTh4CkU4AfjWfJgh28R0SqCxBcWmsRiwpIugIlmE/Drcn70FIhmZh4fLDImTPipqilG2jeg2dGXDRYq3TRh8lTEFslBnVbKhKEwocMwqXrhJYs1iKfPfMjYUbP/mdBQNeiGHDhlnFjNCteSkrkSqoCclNOGPGDBMcuDNuuukmE4CnnXaaCQ8KCVNDkkE0F5XtQwy6QFAjxCkVRpNOBuW6eD5E5VoSKCSCEBCOmzZtKvH4EZjsN7LST3ZLCAEsQ1yVvoccApwybvTU4yfWFgn/lH6jFJu3YPgZAnzvq1atMpcfwUbbOy6U8uOPP277vCivkJGyEqkCywmFhMCgCgjuGPZ6aMNy9dVXu3POOccEDTX9cLGxKsalQzuY0EApsW9AF2wsjrfeeqsocCQ0i6MkOFYsKeaBovJQqYVzwPlBEXtrkXOIEsCCWbp0aTAuKAT9+vXrTZlinXNcCHuuMWqWJsFyp7YiyotrDasyBFiUsWjADe7dlHzfVNzhXLA3isWYrOzDvJctW2Yh+lhdoSJlJQoCbkxawBDmjpuQm5YblH0uVsvLly8P0lohgoxjRUgweMxqN01gHRLSjRWLYARcmhSGxnVGR4T777/f9enTxxYSCErcnmz+o7D4fCh4q5DjRwkzH8qxeUsrCftUPI9Co7FqVcPxJosVs6hjHizqSPGgEznnifNB5CxeCr/445rjHLFgChUpK1FQsAo+6aSTTHkheHAFskLGxRGCQCkOBAcjjeDK8/lFCD6+e5QRHRAQ5CgxrA8sESxilBWWC+CqwoUYAgh7X5nCgzDPpaiA51EGKLcQ3IAcB6kQJMlj+eFVANyV7EthHbKgY4+XVkx0oOBcMQ+uPRQdln6owRZSVqKg4MZr06aN/aTRJq4PhCMClZs4VBAajDTCHiGCDsHO4oB5YC1NmzbNmoiy3zNgwACLRGNjH6GK+wzLl3OSbFZalXgrg+PnePwozjWGYmMuvD+EgBiOlf02FCfXPIPv+bjjjrPzwvw8vpUR7+H9NL3FNUuYewiKNxdSVqKgQHD4ivdERSFEuElZvYeygi802OPBckLweYGIIGcfB7eSHyTQ8jwrd39uEPa4A0OAYyeqFGuJ4/NKi+POPkaex73M+1gYhdB/zh8TSodjZ3BOUExJRQW8RsAIbnIecy74POcs1KhAKStRcCBsWFEmQUAiKEX5grJBWSHg+M4RfNuD80BaAQoAARmK2wmBTjg6ix2sPQQ4iog6lAQgeCHulQIJ6FiQ7GlhwVc1HBd7T/xkLgzOBwopW1kBVqS3CHkfn+Ncck5DRMpKFBwIP29FcQMyEDoMUb4gBL1FhRJCAPKY/RICEEiYzTVwOxGVhqD3eyshwF4PHb6Ba4iFD5bV5MmT3fz58y2qlFJMJDkTyYiwx6oKwTrke/c9+LgHGF4B8TMbv8gAXufzuGbzWXBUBVJWoqBgVci+iI8G9DcqQjSfChiidCDg+F4RkrjE/Kq8cePGlvhL9Ypcg2RUBukGudxUVQVz6dmzp1lXBOhw7TCIoqOoLXtvDJQV1hX7OytWrLC9ohBA+aOs/HGjsKg5yeMk/E5JLBQyj73SYiHB50NEykoUFAgPnwxJ5BPuJlb83MShlcQpFIjsa9q0qQk9FBZgaSD4EPzkwCUHZX4Ia8flhqJC0YUCViJVRUgwB4Q5Cpi5sS9HSSyEfNJ1yWvenVaVYBG1aNGiKGiC6x634BNPPGGLNw/Hi2sTNybnC8sWxcwcDjjggKAs3SRSVqKgIKkTdw03JOG43KTspXATE3Qhyh9cekSWISyxanG3YnVgQd13330Wuk6ABWV/iDajlQvV8cm7Yi8oKfhDgEUNhZBRrD4XKRf+eSwR72LmuaqyEvn+27Zta5F/7KWhqLCY+I6nT59epGgp0Etlf54H7hX26FjcNW/ePAjFmwspK1EQcMP5atk0xcONw83KypgIL3JKQnE1FRp81507dzZhx+IAAcn5wMJFKfXu3dvqHSL8qefIcygxivd6SywkuE7ICaP+H8V3sbSYm685STAFOWQHHXSQWSG837vOqtIq4TioUMG5AO+WJW2DckosDiguTJ1Damb6BGisYI6/a9euNs9QkbISqQNB6Ac3JCtF6gRSSZqSMcDmPWG4uEPYMKdArKg4WAz42ox+nxBBSCI2SotgBPZ2cD+xgAjNmkqCVUgfKywUWmpQe5IcMepPMrAYySGjAHHSCkFZEGxRlXs+eA9YDLRs2dLOgYdcMc4DveEoxYTl5e8hFhetWrVyp59+ehAh+MUhZSVSBYKQZot9+/a1YqKs1FkxkpG/YMECe50VMMmmuKTwwVMvUMEVFQvfOaWuKOODwMvHisUKCXEzH1cZLTWYD8Idy7Fjx46mjLES2W9jkURkID9REFjx7BVRA5GfVQXfJ+XGaI/Dz+1F9nEOku/P57xVFVJWIlVgKVHah30PlBMrYFaKrIZZJSIo8L+zoselQUQa+ykh34SFAkmmNFocM2aMfecEWBCRiQDl++cnv7MnhJsNtyAWWWgb+j6C7u6773Znn322taNhwXPppZdaLy7q7PE8liL7oViTKCtccERBVnXoN98nx4wVSP8wAllQqFiBnAfuEdyZPI81RVt+rMSqVLL5IGUlUgEWE+4jwG2BQPFh0h6UFUKD59lTGDdunOvSpUtwwjAXzCdk11g+IAjZHxwyZIibM2eOtQAZOnSo7VchFPv162e/E64+b948y13KZ/VfVbAwwpVMCxCaeOL+o3EhbWleffVVuw5RViyUuMaIckRhhwDHQ03AKVOmmCcCzwPngf0q9q0uueQSa5KJQiMoIw05iFJWIhWwCZwMv80GQcnKEV89RTsRLHSrTUtxWBSxV8ZphxU6IekIRhQTkWcoL1pX8Dv1Amkbgms2RDdgthXu93UIBfch4X6/B1czr9E9GIXsC/SGAIsAXLK4LbF22XujjcuNN95oe3EcM6+HuljIRspKBAtCA6GHldS6dettBq6mDh06mFIiaosbkYH/nddCDcFNwjGiUHElMXicBkuwkCEU3xd/xeLwbswkCHjOE+/t1auXKWHCvkXFIWUlggVBjtuINuL02ske9957r63Y6QQ8atQo88/TXjwt1hQCEKXKSnfGjBk2sD6wPEK0OL4usJeD+4xra+TIkeZKppI/LTbI18N6R5kRgME5w92JpZgWCyWt6NsVQYObokGDBsUO9kgIU2fDOA2WVBKUFf23iGikBxcDwRh6VFahg9Ihl4qgA4IqSIeg3BKVIPhJGD7dnLGmyGkikETnq+KRshKiCkHIYUUlh1boYeBdfUQwEk1HFCNKjAUULlvvIhSVg+4KIYQQwSNlJYQQInikrIQQQgSPlJUQQojgkbISQggRPFJWQgghgkfKSgghRPBIWQkhhAgeKSshhBDBI2UlhBAieKSshBBCBI+UlRBCiOCpFtFBTIgyQNM52lvQcfTaa6+1CuiFwpo1a9zll1/u1q9f78477zzXu3fv1BctpWr4lVdeaZ2UaanSsWPHzCvphOtv7ty51u2WxpUnnHCCtdVP83XIPOigPGvWLGuOmPb5eJYtW2b300cffWTdBThvpW2jL2UlygzCAkV1zTXXWA+pQqpA/cUXX7gNGzbYz3r16lml7bRDy/9NmzaZsqKxINXE0w7zoa081evr1KljzRDTfB0ijumKzeD8cN0Vwn21efNm995779l5ooP0hAkTSt3SR8pKlBkunbVr17qXX37ZFFchgWB///33rYU5rSEQhOpZFBZcfygrVuu066CvVK1atTKvipDw9xMLpbZt27r27duX+n6SshJCCBE8CrAQQggRPFJWQgghgkfKSgghRPBIWQkhhAgeKSshhBDBI2UlhBAieKSshBBCBI+UlRBCiOCRshJCCBE8UlZCCCGCR8pKCCFE8EhZCSGECB4pKyGEEMEjZSWEECJ4pKyEEEIEj5SVEEKIwHHu/wCoPZ+8o4dI+QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420
            }
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aluno[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "uzG2_1fAzfpS",
        "outputId": "df6d553b-87c1-4165-a4a5-fd7d83ecc918"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Diego'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tupla"
      ],
      "metadata": {
        "id": "bWSL434oEUOO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tuplas são objetos que possuem uma estrutura semelhante ao de uma lista, porém, possui caracteristicas proprias.<br />\n",
        "<br />\n",
        "\n",
        "O principal ponto que difere as duas é o fato de que as tuplas, após serem criadas, não podem ser alteradas."
      ],
      "metadata": {
        "id": "6v1G6RQk4Pr-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(tupla_aluno)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rdjm3hf95FB0",
        "outputId": "b9b1c8b8-507b-4657-d2e7-5fdd0c93a73c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tupla_aluno[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KO_Zl_iR5JqY",
        "outputId": "cf22e852-1416-4e57-c6cf-ab3af7118cea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "João\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tupla_aluno[0] = \"Pedro\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "3gxL-Cwk5R4V",
        "outputId": "e771d460-4d5c-49da-8f8e-136935d8a8d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-c3d438b99c65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtupla_aluno\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Pedro\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tuplas nomeadas"
      ],
      "metadata": {
        "id": "hYZjWuai5ipl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "O significado do conteúdo de uma tupla depende da posição de cada item na sua estrutura. Devido a isso, nem sempre fica claro o que cada item representa uma vez que o objeto é criado e está em uso.<br />\n",
        "<br />\n",
        "\n",
        "Por exemplo:<br />\n",
        "<br />\n",
        "\n",
        "aluno = (nome, nota) --> ('João', 10) <br />\n",
        "aluno = (nome, idade) --> ('João', 10) <br />\n",
        "<br />\n",
        "\n",
        "Consegue ver o problema?<br />"
      ],
      "metadata": {
        "id": "IfiYGFdG6TpN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importamos a biblioteca collections \n",
        "from collections import namedtuple"
      ],
      "metadata": {
        "id": "bwgOYLAs5nOs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# criando nossa tupla nomeada\n",
        "Aluno = namedtuple('Aluno', ['nome', 'idade'])"
      ],
      "metadata": {
        "id": "dWBLxL-D5uAp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "novo_aluno = Aluno(nome='Fábio', idade=16)"
      ],
      "metadata": {
        "id": "LvOZkcO66CHR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "novo_aluno"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlKmHSWG6KbC",
        "outputId": "c47dc6b7-2830-46af-cc27-7b5fb1b7c4e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Aluno(nome='Fábio', idade=16)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dicionários"
      ],
      "metadata": {
        "id": "NZxhiWcKBnBL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dicionários são estruturas que armazenam informações no formato {key: value}"
      ],
      "metadata": {
        "id": "z-iuqy31CSl9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aluno = {\n",
        "    \"nome\": \"Diego\",\n",
        "    \"idade\": 35,\n",
        "    \"nota\": 10\n",
        "}"
      ],
      "metadata": {
        "id": "B-s1yLHBBo4l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aluno[\"idade\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S4SkLviNAOn-",
        "outputId": "c0fe78b7-1c8a-4000-9e67-6a2a634776ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idade_alunos_dict = {\n",
        "    \"diego\": 35,\n",
        "    \"joao\": 27,\n",
        "    \"maria\": 28,\n",
        "    \"pedro\": 19,\n",
        "    \"Godofredo\": 26\n",
        "}"
      ],
      "metadata": {
        "id": "q3kLLLNQrkkD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "idade_alunos_dict[\"diego\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pQ2sFmgrzic",
        "outputId": "03d84de1-e918-4819-9402-42e9486bd3af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(aluno)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNmRMb39orH-",
        "outputId": "febda991-c046-42c0-a769-f95500a7aaf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# uma lista de dicionários\n",
        "alunos = [\n",
        "    {\n",
        "    \"nome\": \"Diego\",\n",
        "    \"idade\": 35,\n",
        "    \"nota\": 10\n",
        "    },\n",
        "    {\n",
        "    \"nome\": \"João\",\n",
        "    \"idade\": 25,\n",
        "    \"nota\": 9.5\n",
        "    },\n",
        "    {\n",
        "    \"nome\": \"Maria\",\n",
        "    \"idade\": 23,\n",
        "    \"nota\": 10\n",
        "    },\n",
        "]"
      ],
      "metadata": {
        "id": "Iv5k1xpODC_s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(alunos))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZcvOwK3FNtU",
        "outputId": "1dfe9a30-15ed-4fca-8004-977ed5ee27dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Input"
      ],
      "metadata": {
        "id": "wNJeFwjknIh_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "O input é uma forma de capturamos um dado fornecido pelo usuário durante a execução do nosso código"
      ],
      "metadata": {
        "id": "210vzFVNAI8A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nome = input()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zJmXOvpAVZY",
        "outputId": "281c277f-915e-479e-b785-3cbee612b35b"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Diego\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(nome)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acekK5XYAH26",
        "outputId": "ef22047c-7da6-4108-dac9-52a8551e7853"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diego\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Laços"
      ],
      "metadata": {
        "id": "lr4R0aurnZeE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Laços são uma maneira de iterarmos sobre os elementos de nossos objetos. Vamos ver alguns casos: <br />\n",
        "<br />"
      ],
      "metadata": {
        "id": "CvG_5WMrGWzW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Iterando sobre a nossa lista de alunos\n",
        "for aluno in alunos:\n",
        "  print(aluno)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JIhCx-NGSRf",
        "outputId": "4e214645-1aea-4a04-8fa4-7d166fab3180"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'nome': 'Diego', 'idade': 35, 'nota': 10}\n",
            "{'nome': 'João', 'idade': 25, 'nota': 9.5}\n",
            "{'nome': 'Maria', 'idade': 23, 'nota': 10}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando a média das notas dos alunos\n",
        "soma_notas = 0\n",
        "for aluno in alunos:\n",
        "  soma_notas += aluno[\"nota\"]\n",
        "\n",
        "# média = soma(notas)/alunos\n",
        "media = soma_notas/len(alunos)\n",
        "media"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDWt0k0hAu1g",
        "outputId": "2e858570-5ffe-41d4-a06d-1b903bd935e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.833333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While"
      ],
      "metadata": {
        "id": "lLLqCbUgtZN1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numero = len(alunos)\n",
        "count = 0\n",
        "while count < numero:\n",
        "  print(alunos[count])\n",
        "  count += 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_uDoRwDtVoJ",
        "outputId": "a6b6a687-219d-4c4e-efce-e8e9af19b9cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'nome': 'Diego', 'idade': 35, 'nota': 10}\n",
            "{'nome': 'João', 'idade': 25, 'nota': 9.5}\n",
            "{'nome': 'Maria', 'idade': 23, 'nota': 10}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Condicionais"
      ],
      "metadata": {
        "id": "zp9AWhF9nWbh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "São operações utilizadas para determinar se uma condição está sendo atendida. <br />\n",
        "<br />\n",
        "\n",
        "Operadores:<br />\n",
        "\n",
        "Operador |\tTipo\t| Valor\n",
        "--------- | ------ | --------\n",
        "==\t| Igualdade |\tVerifica a igualdade entre dois valores.\n",
        "!=  |\tIgualdade  |\tVerifica a diferença entre dois valores.\n",
        "> |\tComparação |\tVerificar se o valor A é maior que o valor B.\n",
        "< |\tComparação\t| Verifica se o valor A é menor que o valor B.\n",
        ">=|\tComparação\t| Verifica se o valor A é maior ou igual ao valor B.\n",
        "<= |\tComparação\t| Verifica se o valor A é menor ou igual ao valor B.\n",
        "In |\tSeqüência\t| Verifica se o valor A está contido em um conjunto.\n"
      ],
      "metadata": {
        "id": "pHdvx8d1F7IM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exemplos:\n",
        "<br />\n",
        "1 - Dada uma lista de alunos, vamos verificar se o aluno **João** está entre eles"
      ],
      "metadata": {
        "id": "b0aqzWq-RncC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alunos = ['Mario', 'João', 'Marcelo', 'Jair', 'Jaques', 'Diego', 'Jairo', 'Beatriz']"
      ],
      "metadata": {
        "id": "RzyDJmhsRpuB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Método 1\n",
        "for aluno in alunos:\n",
        "  if aluno == 'João':\n",
        "    print(f'{aluno} está matriculado.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PImIpF9iSPNP",
        "outputId": "ed6a6e3e-7d01-41e0-e795-d2e3d78c9bb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "João está matriculado.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Método 2\n",
        "estudante = 'João'\n",
        "if estudante in alunos:\n",
        "  print(f\"Aluno {estudante} encontrado.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfXXcVZoS8Ck",
        "outputId": "02a848f7-133b-4743-f8b3-5fe885795fcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aluno João encontrado.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Funções"
      ],
      "metadata": {
        "id": "aa8VFl221Uit"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Uma função é um bloco de código nomeado que executa uma tarefa ou um conjunto de tarefas pré definidas.<br />\n",
        "<br />\n",
        "O uso de funções nos trazem algumas vantagens:\n",
        "* Reaproveitamento de código\n",
        "* Melhor organização\n",
        "* Código mais bem estruturado e de fácil compreensão\n",
        "* Facilidade de manutenção\n"
      ],
      "metadata": {
        "id": "LmiCicjPULqp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos considerar nossa lista de alunos:"
      ],
      "metadata": {
        "id": "F7mKyGXYbMJo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alunos_lista = [\n",
        "    {\n",
        "    \"nome\": \"Diego\",\n",
        "    \"idade\": 35,\n",
        "    \"nota\": 10\n",
        "    },\n",
        "    {\n",
        "    \"nome\": \"João\",\n",
        "    \"idade\": 25,\n",
        "    \"nota\": 9.5\n",
        "    },\n",
        "    {\n",
        "    \"nome\": \"Maria\",\n",
        "    \"idade\": 23,\n",
        "    \"nota\": 10\n",
        "    },\n",
        "]"
      ],
      "metadata": {
        "id": "-Mak-JV-bcui"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "URxvunAFmDeE"
      },
      "outputs": [],
      "source": [
        "def valida_aluno(aluno_nome):\n",
        "  for aluno in alunos_lista:\n",
        "    if aluno_nome == aluno['nome']:\n",
        "      print(f'Aluno {aluno_nome} encontrado.')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valida_aluno(aluno_nome='Maria')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Jv1oTZ3ViR1",
        "outputId": "de81a763-a664-49dd-f61f-ba6b40f3aea0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aluno Maria encontrado.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No exemplo acima vimos um exemplo de como podemos criar e utilizar uma função. Vamos a mais um exemplo."
      ],
      "metadata": {
        "id": "1m-Hy5Hnatmd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cria_aluno(aluno_nome, aluno_idade):\n",
        "  alunos_lista.append({'nome': aluno_nome,\n",
        "                       'idade': aluno_idade})"
      ],
      "metadata": {
        "id": "u28uMZd7a8e_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cria_aluno(aluno_nome='Giovanni', aluno_idade=29)"
      ],
      "metadata": {
        "id": "bocFxxnEdhf1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cria_aluno(aluno_nome='Marcelo', aluno_idade=18)"
      ],
      "metadata": {
        "id": "0h75PlXId3z1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alunos_lista"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btfrn6SvdRFw",
        "outputId": "4dc69554-d16b-4e43-f03a-e8827ea5b7c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'nome': 'Diego', 'idade': 35, 'nota': 10},\n",
              " {'nome': 'João', 'idade': 25, 'nota': 9.5},\n",
              " {'nome': 'Maria', 'idade': 23, 'nota': 10},\n",
              " {'nome': 'Giovanni', 'idade': 29},\n",
              " {'nome': 'Marcelo', 'idade': 18}]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def atualiza_nota_aluno(aluno_nome, aluno_idade, aluno_nota):\n",
        "  for aluno in alunos_lista:\n",
        "    if aluno['nome']==aluno_nome and aluno['idade']==aluno_idade:\n",
        "      aluno.update({'nota': aluno_nota})\n",
        "      print(f'Nota do aluno {aluno_nome} atualizada')"
      ],
      "metadata": {
        "id": "YAi_27Lmd6ed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "atualiza_nota_aluno('Giovanni', 29, 8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miMCXfNXe9bZ",
        "outputId": "be082b40-5e75-4a49-953d-cc10d9a9bd8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nota do aluno {aluno_nome} atualizada\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alunos_lista"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMllhhU6fF4G",
        "outputId": "13ff940c-d74e-4709-a9ab-e7424f17910c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'nome': 'Diego', 'idade': 35, 'nota': 10},\n",
              " {'nome': 'João', 'idade': 25, 'nota': 9.5},\n",
              " {'nome': 'Maria', 'idade': 23, 'nota': 10},\n",
              " {'nome': 'Giovanni', 'idade': 29, 'nota': 8},\n",
              " {'nome': 'Marcelo', 'idade': 18}]"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classes"
      ],
      "metadata": {
        "id": "zypZ6Enht8wD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Aqui apenas um exemplo de como criamos uima classe no Python. No próximo encontro\n",
        "# iremos adicionar mais detalhes.\n",
        "\n",
        "class Alunos():\n",
        "  def __init__(self):\n",
        "    pass\n",
        "\n",
        "  def valida_aluno(self, aluno_nome):\n",
        "    for aluno in alunos_lista:\n",
        "      if aluno_nome == aluno['nome']:\n",
        "        print(f'Aluno {aluno_nome} encontrado.')\n",
        "\n",
        "  def cria_aluno(self, aluno_nome, aluno_idade):\n",
        "    alunos_lista.append({'nome': aluno_nome,\n",
        "                        'idade': aluno_idade})\n",
        "    \n",
        "  def atualiza_nota_aluno(self, aluno_nome, aluno_idade, aluno_nota):\n",
        "    for aluno in alunos_lista:\n",
        "      if aluno['nome']==aluno_nome and aluno['idade']==aluno_idade:\n",
        "        aluno.update({'nota': aluno_nota})\n",
        "        print(f'Nota do aluno {aluno_nome} atualizada')"
      ],
      "metadata": {
        "id": "vN09fEo6t-xL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criamos uma instancia da nossa classe\n",
        "ca = Alunos()"
      ],
      "metadata": {
        "id": "2CLJKGDRfdpV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# E podemos utilizá-la\n",
        "ca.cria_aluno('Matheus', 28)"
      ],
      "metadata": {
        "id": "X3vgPkStuUSm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ca.cria_aluno('Mariana', 35)"
      ],
      "metadata": {
        "id": "K71Zg_OdHVaS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alunos_lista"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1mA1SQIHBfW",
        "outputId": "05a0602f-9ec1-4b80-98b6-6e4c41149dc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'nome': 'Diego', 'idade': 35, 'nota': 10},\n",
              " {'nome': 'João', 'idade': 25, 'nota': 9.5},\n",
              " {'nome': 'Maria', 'idade': 23, 'nota': 10},\n",
              " {'nome': 'Giovanni', 'idade': 29, 'nota': 8},\n",
              " {'nome': 'Marcelo', 'idade': 18},\n",
              " {'nome': 'Matheus', 'idade': 28},\n",
              " {'nome': 'Mariana', 'idade': 35}]"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yYuzePwPHYB3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}