Python_para_ML_04.ipynb

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  "metadata": {
    "colab": {
      "name": "Python para ML - 04.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "14qg52pC1fXYL9XD-gaRmihwNh6G2-Xv_",
      "authorship_tag": "ABX9TyNO+FRf4jkYaD1gh2SGdWkO",
      "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/ricardorocha86/Python-para-Machine-Learning/blob/master/Python_para_ML_04.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6l4GKt4xjst",
        "colab_type": "text"
      },
      "source": [
        "![texto alternativo](https://raw.githubusercontent.com/ricardorocha86/Python-para-Machine-Learning/master/Imagens/capa%20-%20python%20para%20ml%2004.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BNsCsDcRxq5K",
        "colab_type": "text"
      },
      "source": [
        "# **Python para Machine Learning** - Parte 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5ci8BYxxrLn",
        "colab_type": "text"
      },
      "source": [
        "Nesse notebook vamos ver:\n",
        "- Dicionários\n",
        "- A Biblioteca Pandas\n",
        "- Projeto: Pegando dados da WEB (*webscrapping*)\n",
        "- Exercício\n",
        "- Links Úteis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GjMyuwrxrQh",
        "colab_type": "text"
      },
      "source": [
        "## **Dicionários**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXH65lvlx0gl",
        "colab_type": "text"
      },
      "source": [
        "Dicionários são objetos no python similares a conjuntos, mas permitem que sejam colocados chaves de referência para cada item. São definidos utilizando chaves **{ }** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqYC1RV9yMt1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dicionario = {'nome': 'Ricardo', 'idade': 33, 'magro': False, 'função_favorita': len}"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kk9ZP8usygY5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "a28f16f5-aa2e-4f89-e50b-2050624a5abb"
      },
      "source": [
        "dicionario.items()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('nome', 'Ricardo'), ('idade', 33), ('magro', False), ('função_favorita', <built-in function len>)])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyelWW_8ylgu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "ae14ff6b-205f-4409-e22b-facb419a322f"
      },
      "source": [
        "dicionario.keys()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['nome', 'idade', 'magro', 'função_favorita'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlCZXYpEDt5j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "e569e2b0-33f5-441c-8dad-2e718388e3f3"
      },
      "source": [
        "dicionario.values()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values(['Ricardo', 33, False, <built-in function len>])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Kc8Ixhfyv4L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "c9260047-63f3-406d-c7c0-d72cc927dc47"
      },
      "source": [
        "dicionario.get('nome')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Ricardo'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sd4byHojyynH",
        "colab_type": "text"
      },
      "source": [
        "Adicionando novos itens a um dicionário:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7epUiPRzrfE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dicionario['musica_favorita'] = 'Fairy Tale - Shaman'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYmiS-A5z07c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "outputId": "7c945bb7-e5bd-4212-a965-b740dbbaec99"
      },
      "source": [
        "print(dicionario)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'nome': 'Ricardo', 'idade': 33, 'magro': False, 'função_favorita': <built-in function len>, 'musica_favorita': 'Fairy Tale - Shaman'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kocKWQbzxLf",
        "colab_type": "text"
      },
      "source": [
        "Visualizando todas as keys (chaves):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrI23nkC0DTb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "088d436f-7e89-40de-9e74-e77672017310"
      },
      "source": [
        "for i in dicionario:\n",
        "    print(i)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nome\n",
            "idade\n",
            "magro\n",
            "função_favorita\n",
            "musica_favorita\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvDKhOUt0GGE",
        "colab_type": "text"
      },
      "source": [
        "Visualizando todas os values (valores):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qg4gMLgf0L2E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "96abad56-ead8-4f9d-940f-123dd480e925"
      },
      "source": [
        "for i in dicionario:\n",
        "    print(dicionario[i])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ricardo\n",
            "33\n",
            "False\n",
            "<built-in function len>\n",
            "Fairy Tale - Shaman\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMG3o7de0Qi8",
        "colab_type": "text"
      },
      "source": [
        "Dicionários são úteis em diversas ocasiões em Python, como por exemplo, para passar uma lista de argumentos para uma função:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XuuB_nms0b4W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "a1e6370d-27e1-441c-bf64-392eec2ab45c"
      },
      "source": [
        "parametros = {'loc': 2, 'scale': 1, 'size': 12}\n",
        "print(parametros)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'loc': 2, 'scale': 1, 'size': 12}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVpAmawNytuc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6102604a-13ff-475c-b249-cee56f5cb4a7"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.random.normal(**parametros)\n",
        "print(x)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.74889965 0.21859105 1.38775864 3.97551208 2.36528028 2.34072986\n",
            " 3.16100015 1.13124302 2.98907357 3.21460391 1.52536989 3.01640311]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHR-KVDv26rz",
        "colab_type": "text"
      },
      "source": [
        "Existe uma solução similar para quando se quer passar listas como argumentos de função:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eu8WolK22EbW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "41feb312-d904-468b-cb7c-0688dbd114db"
      },
      "source": [
        "x = np.random.normal(*[2, 1, 12])\n",
        "print(x)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.87783395 1.901883   0.98516956 2.33666672 2.06751484 3.89585858\n",
            " 2.25744675 3.30778925 3.25457413 1.97127765 5.08657147 2.80088823]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp5a6xSKysGA",
        "colab_type": "text"
      },
      "source": [
        "## **A Biblioteca Pandas**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twMKZpCZyqe2",
        "colab_type": "text"
      },
      "source": [
        "Pandas é a principal biblioteca em Python para se trabalhar com conjunto de dados. Ela oferece diversos objetos e muitas funções que facilitam a importação, seleção, alteração e filtragem de conjuntos de dados. Vejamos alguns a seguir."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zr0v5FAbfgCg",
        "colab_type": "text"
      },
      "source": [
        "Os dois principais objetos da biblioteca pandas são o **Series** e o **DataFrame**. O segundo é a representação usual que temos de um conjunto de dados, o primeiro é um objeto similar a um array. Para todos os fins, um objeto Series é como se fosse um DataFrame de apenas uma coluna.\n",
        "\n",
        "Vejamos como criar alguns DataFrames:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzQknJ8Nf7Ho",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Givie7Abf9pJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "7b1bb592-6d78-4963-f3d2-275705afa844"
      },
      "source": [
        "valores = np.random.uniform(0, 10, 20).reshape(5, 4).round(1)\n",
        "valores"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.8, 2.5, 7.7, 4.7],\n",
              "       [0.6, 2.7, 2.3, 2.3],\n",
              "       [7.2, 7.9, 1.1, 7.6],\n",
              "       [2.8, 7.9, 4.3, 6.1],\n",
              "       [3.6, 6.7, 5.1, 8.6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-ECB_E7gF2j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nomes = ['Alice', 'Bruno', 'Carol', 'David', 'Emily']\n",
        "testes = ['P1', 'P2', 'P3', 'P4']"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQN4m--XfeCK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "263d2e9c-1c41-4ca6-f4a7-ab037e6e98c3"
      },
      "source": [
        "provas = pd.DataFrame(valores, columns = testes, index = nomes)\n",
        "provas"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.7</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>0.6</td>\n",
              "      <td>2.7</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>7.2</td>\n",
              "      <td>7.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>2.8</td>\n",
              "      <td>7.9</td>\n",
              "      <td>4.3</td>\n",
              "      <td>6.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>3.6</td>\n",
              "      <td>6.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>8.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4\n",
              "Alice  5.8  2.5  7.7  4.7\n",
              "Bruno  0.6  2.7  2.3  2.3\n",
              "Carol  7.2  7.9  1.1  7.6\n",
              "David  2.8  7.9  4.3  6.1\n",
              "Emily  3.6  6.7  5.1  8.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PykG8Me-feVr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "cfe35950-1947-4ab6-c483-35d7ee6b20e0"
      },
      "source": [
        "provas.transpose()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Alice</th>\n",
              "      <th>Bruno</th>\n",
              "      <th>Carol</th>\n",
              "      <th>David</th>\n",
              "      <th>Emily</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>P1</th>\n",
              "      <td>5.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>7.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>3.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P2</th>\n",
              "      <td>2.5</td>\n",
              "      <td>2.7</td>\n",
              "      <td>7.9</td>\n",
              "      <td>7.9</td>\n",
              "      <td>6.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1.1</td>\n",
              "      <td>4.3</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>4.7</td>\n",
              "      <td>2.3</td>\n",
              "      <td>7.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>8.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Alice  Bruno  Carol  David  Emily\n",
              "P1    5.8    0.6    7.2    2.8    3.6\n",
              "P2    2.5    2.7    7.9    7.9    6.7\n",
              "P3    7.7    2.3    1.1    4.3    5.1\n",
              "P4    4.7    2.3    7.6    6.1    8.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPyXCnLafeeW",
        "colab_type": "text"
      },
      "source": [
        "Gerando DataFrames através de Dicionários:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-Rhr0l7iFeJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dicionario = {'Alice': np.random.uniform(5, 10, 4).round(1),\n",
        "              'Bruno': np.random.uniform(0, 7, 4).round(1),\n",
        "              'Carol': np.random.uniform(3, 7, 4).round(1),\n",
        "              'David': np.random.uniform(0, 10, 4).round(1),\n",
        "              'Emily': np.random.uniform(8, 10, 4).round(1)}"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woHJJLeGil0K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas = pd.DataFrame(dicionario)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPVV70HdiPbK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas.index = ['P1', 'P2', 'P3', 'P4']"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUwBWh1Ziw8z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas = provas.transpose()"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFLZHExYEnT4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5448b96a-645a-4d65-d9c8-a9bcf4a49d73"
      },
      "source": [
        "provas"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4\n",
              "Alice  8.9  8.3  8.3  6.2\n",
              "Bruno  5.6  4.1  3.6  6.2\n",
              "Carol  5.2  5.5  3.5  5.2\n",
              "David  8.5  9.2  2.0  4.2\n",
              "Emily  9.1  9.7  9.3  8.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQO-ODtJE8_t",
        "colab_type": "text"
      },
      "source": [
        "Alguns métodos básicos importantes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6a5CbNsixqa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "0e5a6f46-25e8-45f7-b6fe-6197abd23c8f"
      },
      "source": [
        "provas.head(2)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4\n",
              "Alice  8.9  8.3  8.3  6.2\n",
              "Bruno  5.6  4.1  3.6  6.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFu4k955jXpE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "ce2092bc-6ad4-4ef3-b6ca-37d584d43bc3"
      },
      "source": [
        "provas.tail(2)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4\n",
              "David  8.5  9.2  2.0  4.2\n",
              "Emily  9.1  9.7  9.3  8.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2cKr7zfiCbr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "6aba7ffe-58d6-4351-9f0b-d18a40556b07"
      },
      "source": [
        "provas.info()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 5 entries, Alice to Emily\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   P1      5 non-null      float64\n",
            " 1   P2      5 non-null      float64\n",
            " 2   P3      5 non-null      float64\n",
            " 3   P4      5 non-null      float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 200.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPBjktoMjbXS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "a3f65ab8-0567-491f-8eae-829ad5e36b62"
      },
      "source": [
        "provas.describe()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.460000</td>\n",
              "      <td>7.360000</td>\n",
              "      <td>5.340000</td>\n",
              "      <td>6.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.898157</td>\n",
              "      <td>2.440901</td>\n",
              "      <td>3.240833</td>\n",
              "      <td>1.520526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.200000</td>\n",
              "      <td>4.100000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.600000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>5.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.500000</td>\n",
              "      <td>8.300000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>6.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.900000</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>8.300000</td>\n",
              "      <td>6.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.100000</td>\n",
              "      <td>9.700000</td>\n",
              "      <td>9.300000</td>\n",
              "      <td>8.300000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             P1        P2        P3        P4\n",
              "count  5.000000  5.000000  5.000000  5.000000\n",
              "mean   7.460000  7.360000  5.340000  6.020000\n",
              "std    1.898157  2.440901  3.240833  1.520526\n",
              "min    5.200000  4.100000  2.000000  4.200000\n",
              "25%    5.600000  5.500000  3.500000  5.200000\n",
              "50%    8.500000  8.300000  3.600000  6.200000\n",
              "75%    8.900000  9.200000  8.300000  6.200000\n",
              "max    9.100000  9.700000  9.300000  8.300000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGmZPXz6jkOB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "fad5ef64-e76e-41b2-8b4c-b38cd5eeb4ce"
      },
      "source": [
        "provas.transpose().describe()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Alice</th>\n",
              "      <th>Bruno</th>\n",
              "      <th>Carol</th>\n",
              "      <th>David</th>\n",
              "      <th>Emily</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.925000</td>\n",
              "      <td>4.875000</td>\n",
              "      <td>4.850000</td>\n",
              "      <td>5.975000</td>\n",
              "      <td>9.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.184272</td>\n",
              "      <td>1.225765</td>\n",
              "      <td>0.911043</td>\n",
              "      <td>3.450966</td>\n",
              "      <td>0.588784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.200000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.775000</td>\n",
              "      <td>3.975000</td>\n",
              "      <td>4.775000</td>\n",
              "      <td>3.650000</td>\n",
              "      <td>8.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.300000</td>\n",
              "      <td>4.850000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>6.350000</td>\n",
              "      <td>9.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.450000</td>\n",
              "      <td>5.750000</td>\n",
              "      <td>5.275000</td>\n",
              "      <td>8.675000</td>\n",
              "      <td>9.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.900000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>9.700000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Alice     Bruno     Carol     David     Emily\n",
              "count  4.000000  4.000000  4.000000  4.000000  4.000000\n",
              "mean   7.925000  4.875000  4.850000  5.975000  9.100000\n",
              "std    1.184272  1.225765  0.911043  3.450966  0.588784\n",
              "min    6.200000  3.600000  3.500000  2.000000  8.300000\n",
              "25%    7.775000  3.975000  4.775000  3.650000  8.900000\n",
              "50%    8.300000  4.850000  5.200000  6.350000  9.200000\n",
              "75%    8.450000  5.750000  5.275000  8.675000  9.400000\n",
              "max    8.900000  6.200000  5.500000  9.200000  9.700000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rNq0EnWk9Pd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d0ac9f94-5d5d-4abf-cc14-593e251c30bd"
      },
      "source": [
        "provas.shape"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cp2UBs-4EziL",
        "colab_type": "text"
      },
      "source": [
        "Criando uma nova variável chamada **Média**, sendo a média aritmética das 4 provas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMkJLgJIjkTG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas['MF'] = provas.mean(axis = 1).round(1)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKhj-2tdExeS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0cc7056d-6111-4e74-8dbf-eb1f1b00da01"
      },
      "source": [
        "provas"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>MF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4   MF\n",
              "Alice  8.9  8.3  8.3  6.2  7.9\n",
              "Bruno  5.6  4.1  3.6  6.2  4.9\n",
              "Carol  5.2  5.5  3.5  5.2  4.8\n",
              "David  8.5  9.2  2.0  4.2  6.0\n",
              "Emily  9.1  9.7  9.3  8.3  9.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSaqnZd0FDqj",
        "colab_type": "text"
      },
      "source": [
        "Criando uma nova variável personalizada, em que é definida como 'Aprovado' se a variável Média for maior ou igual a 5, e 'Reprovado' no caso contrário:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZYSpl3wkNax",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def aux(x):\n",
        "    if x >= 5:\n",
        "        return 'Aprovado'\n",
        "    else:\n",
        "        return 'Reprovado'\n",
        "\n",
        "provas['Situação'] = provas['MF'].apply(aux)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wknqa9sGFWib",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4a5351d0-e550-4bcb-d813-071a98f23312"
      },
      "source": [
        "provas"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>MF</th>\n",
              "      <th>Situação</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Alice</th>\n",
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              "      <td>8.3</td>\n",
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              "      <td>7.9</td>\n",
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              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
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              "      <th>David</th>\n",
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              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4   MF   Situação\n",
              "Alice  8.9  8.3  8.3  6.2  7.9   Aprovado\n",
              "Bruno  5.6  4.1  3.6  6.2  4.9  Reprovado\n",
              "Carol  5.2  5.5  3.5  5.2  4.8  Reprovado\n",
              "David  8.5  9.2  2.0  4.2  6.0   Aprovado\n",
              "Emily  9.1  9.7  9.3  8.3  9.1   Aprovado"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHU-J7KbFU8u",
        "colab_type": "text"
      },
      "source": [
        "Veja a versatilidade do método apply. É possível criar inúmeros tipos de variáveis, basta definir a transformação desejada em uma função."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vkT4lRZFl_Y",
        "colab_type": "text"
      },
      "source": [
        "Ordenando um conjunto de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hNepPRtkjGn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a721cb2d-7924-4b54-f9aa-1bc1d08586bf"
      },
      "source": [
        "provas.sort_values('MF')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
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              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
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              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4   MF   Situação\n",
              "Carol  5.2  5.5  3.5  5.2  4.8  Reprovado\n",
              "Bruno  5.6  4.1  3.6  6.2  4.9  Reprovado\n",
              "David  8.5  9.2  2.0  4.2  6.0   Aprovado\n",
              "Alice  8.9  8.3  8.3  6.2  7.9   Aprovado\n",
              "Emily  9.1  9.7  9.3  8.3  9.1   Aprovado"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
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    {
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        "id": "iB-PjR3rlycx",
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          "height": 206
        },
        "outputId": "bbf5d868-976a-4cd7-ad32-ed9be180865f"
      },
      "source": [
        "provas.sort_values('MF', ascending = False)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>MF</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4   MF   Situação\n",
              "Emily  9.1  9.7  9.3  8.3  9.1   Aprovado\n",
              "Alice  8.9  8.3  8.3  6.2  7.9   Aprovado\n",
              "David  8.5  9.2  2.0  4.2  6.0   Aprovado\n",
              "Bruno  5.6  4.1  3.6  6.2  4.9  Reprovado\n",
              "Carol  5.2  5.5  3.5  5.2  4.8  Reprovado"
            ]
          },
          "metadata": {
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          },
          "execution_count": 73
        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJ3VV_g-FwvH",
        "colab_type": "text"
      },
      "source": [
        "Tornando o índice do conjunto de dados em uma variável, e tornando o índice um valor númerico:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRItQWFCmKkl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas['Nome'] = provas.index"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWh661E9F6hN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "351f2ecf-60d1-4cfe-c6ca-1f4eba667a53"
      },
      "source": [
        "provas"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>P2</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
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              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
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              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
              "      <td>Carol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "      <td>David</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "      <td>Emily</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4   MF   Situação   Nome\n",
              "Alice  8.9  8.3  8.3  6.2  7.9   Aprovado  Alice\n",
              "Bruno  5.6  4.1  3.6  6.2  4.9  Reprovado  Bruno\n",
              "Carol  5.2  5.5  3.5  5.2  4.8  Reprovado  Carol\n",
              "David  8.5  9.2  2.0  4.2  6.0   Aprovado  David\n",
              "Emily  9.1  9.7  9.3  8.3  9.1   Aprovado  Emily"
            ]
          },
          "metadata": {
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          "execution_count": 75
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "_RtzVrb6ktDZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas.reset_index(inplace = True, drop = True) #o argumento inplace pergunta se a alteração deve ser feita no conjunto de dados "
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVhtIyX6F98b",
        "colab_type": "code",
        "colab": {
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          "height": 206
        },
        "outputId": "0e1da869-36be-40bc-c263-31ffd5cf7f32"
      },
      "source": [
        "provas"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>MF</th>\n",
              "      <th>Situação</th>\n",
              "      <th>Nome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
              "      <td>Aprovado</td>\n",
              "      <td>Alice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "      <td>Bruno</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
              "      <td>Carol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "      <td>David</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "      <td>Emily</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    P1   P2   P3   P4   MF   Situação   Nome\n",
              "0  8.9  8.3  8.3  6.2  7.9   Aprovado  Alice\n",
              "1  5.6  4.1  3.6  6.2  4.9  Reprovado  Bruno\n",
              "2  5.2  5.5  3.5  5.2  4.8  Reprovado  Carol\n",
              "3  8.5  9.2  2.0  4.2  6.0   Aprovado  David\n",
              "4  9.1  9.7  9.3  8.3  9.1   Aprovado  Emily"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlIuOcZHGI08",
        "colab_type": "text"
      },
      "source": [
        "Modificando a ordem das variáveis:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiJV4LIymZdK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas = provas[['Nome', 'P1', 'P2', 'P3', 'P4', 'MF', 'Situação']]"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SWTfzNhGQiI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0249908c-da71-4816-e48b-2feb3e50cf4b"
      },
      "source": [
        "provas"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Nome</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>MF</th>\n",
              "      <th>Situação</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alice</td>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bruno</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Carol</td>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>David</td>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Emily</td>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Nome   P1   P2   P3   P4   MF   Situação\n",
              "0  Alice  8.9  8.3  8.3  6.2  7.9   Aprovado\n",
              "1  Bruno  5.6  4.1  3.6  6.2  4.9  Reprovado\n",
              "2  Carol  5.2  5.5  3.5  5.2  4.8  Reprovado\n",
              "3  David  8.5  9.2  2.0  4.2  6.0   Aprovado\n",
              "4  Emily  9.1  9.7  9.3  8.3  9.1   Aprovado"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HRoHZH6dG7Xg",
        "colab_type": "text"
      },
      "source": [
        "Desfazendo o que fizemos anteriormente (voltando a variável nome para o index)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLd7msxXHAB4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas.index = provas['Nome']"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfvG2hf-HMts",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "9bfdc31a-8833-411b-e84a-312384aa62d0"
      },
      "source": [
        "provas"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Nome</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>MF</th>\n",
              "      <th>Situação</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nome</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>Alice</td>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>Bruno</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>Carol</td>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>David</td>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>Emily</td>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Nome   P1   P2   P3   P4   MF   Situação\n",
              "Nome                                            \n",
              "Alice  Alice  8.9  8.3  8.3  6.2  7.9   Aprovado\n",
              "Bruno  Bruno  5.6  4.1  3.6  6.2  4.9  Reprovado\n",
              "Carol  Carol  5.2  5.5  3.5  5.2  4.8  Reprovado\n",
              "David  David  8.5  9.2  2.0  4.2  6.0   Aprovado\n",
              "Emily  Emily  9.1  9.7  9.3  8.3  9.1   Aprovado"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BThc7k0oHPD1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "outputId": "4d965b5e-517a-4729-be15-b688c13b1901"
      },
      "source": [
        "provas.drop('Nome', axis = 1, inplace = True)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3997: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoBmCaayHU3r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "fd716aeb-1436-40a1-e3b0-3dd60edb08b5"
      },
      "source": [
        "provas"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>MF</th>\n",
              "      <th>Situação</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nome</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
              "      <td>Aprovado</td>\n",
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              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4   MF   Situação\n",
              "Nome                                     \n",
              "Alice  8.9  8.3  8.3  6.2  7.9   Aprovado\n",
              "Bruno  5.6  4.1  3.6  6.2  4.9  Reprovado\n",
              "Carol  5.2  5.5  3.5  5.2  4.8  Reprovado\n",
              "David  8.5  9.2  2.0  4.2  6.0   Aprovado\n",
              "Emily  9.1  9.7  9.3  8.3  9.1   Aprovado"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYMyTNbfLxjy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "provas.index.name = ''"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMelyfNLL2AP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "outputId": "f528613e-118c-4549-c9a3-9f6f315ae71c"
      },
      "source": [
        "provas"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
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              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        P1   P2   P3   P4   MF   Situação\n",
              "                                         \n",
              "Alice  8.9  8.3  8.3  6.2  7.9   Aprovado\n",
              "Bruno  5.6  4.1  3.6  6.2  4.9  Reprovado\n",
              "Carol  5.2  5.5  3.5  5.2  4.8  Reprovado\n",
              "David  8.5  9.2  2.0  4.2  6.0   Aprovado\n",
              "Emily  9.1  9.7  9.3  8.3  9.1   Aprovado"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EW1rvABvHwbT",
        "colab_type": "text"
      },
      "source": [
        "O método describe pode ser utilizado em variáveis categóricas, e tem uma saída diferente nesse caso:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPutz2ryrbaC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "2d9c4298-9499-4714-f205-e2b676c393cc"
      },
      "source": [
        "provas['Situação'].describe()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count            5\n",
              "unique           2\n",
              "top       Aprovado\n",
              "freq             3\n",
              "Name: Situação, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YHr6EsIH6Nf",
        "colab_type": "text"
      },
      "source": [
        "O Pandas também oferece suporte a alguns gráficos, baseados na biblioteca matplotlib. Basta chamar o método utilizando os dados adequados:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQXtJEz0mN-N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "e288d396-b456-49d4-88bb-272a5b626bf0"
      },
      "source": [
        "provas['Situação'].value_counts().plot(kind = 'pie') "
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c88920390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbGnT8HFs8uC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "outputId": "4d879f1a-a6eb-4736-801f-05bc5f118ca7"
      },
      "source": [
        "provas['Situação'].value_counts().plot(kind = 'bar', color = ['green', 'red']) "
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c8895afd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SbwJfB3YH/tLnNWp7kWSXJMcmuRD4NnAbg2LXPPK0xfG1FHh3Va3rO4g0jWuB84GTq+ryvsMsFs6hj7EkBwIv7BYvq6pr+8wjbZYkVVXdtRJU1f19Z1oMnHIZU0nexeDy6j271zlJ3tlvKulhByT5LnA9cEOSq5M8ve9QrXMPfUwluQ54XlX9rFt+HHC58+jaHiT5NvA33S0pSHIo8I9V5UMu5pF76OMrwEOTlh/q1knbg8dtLnOAqvom8Lj+4iwOHhQdX58ErpjyxKIzeswjTXZzkr8DPt0t/zlwc495FgWnXMZQkh0Y3Dr3FzzyxKLLfGKRthfd1aEnMfh+FoP7upxUVXf3GqxxFvqYSvLdqjqo7xzSVEl2BL5WVX/cd5bFxjn08fX1JK9K4ry5titV9RDw6yS79p1lsXEPfUwluY/BQaaHGEy9AFRV7dJfKmkgyb8CBwEXMek+/VX1rt5CLQIeFB1TVbVz3xmkWXyxe2kBuYc+xpK8kkkHnarq/J4jSQ9L8ijgKQy+nzdV1YM9R2qehT6mkvwzsD/wmW7Va4EfVtXb+0slDSR5GfBx4IcMro/YD3hLVV3Ya7DGWehjKskPgKdW9z+wO5Xx+qp6ar/JpIe/n0dU1fpu+XeAL1fVU/pN1jbPchlf64Flk5aXduuk7cF9m8u8czNwX19hFgv30MdUkkuA5wBXdqueA1wF3AtQVUf2FE0iyWnAvsDnGMyhvxr4MfA1gKrygOk8sNDHVJI/mrzI4Da6K4G3AVTVJX3kkgCSfHKWzVVVb16wMIuIhT7GkhwE/BmDvZ//Ar5YVaf0m0pSXzwPfcwk+V3gmO51B3Aeg7+Yvcxa243ue3oa8ISqenr3MPMjq+ofeo7WNPfQx0ySXzO40dFxk84guLmqntRvMukR3TGevwI+vvmeQ0m+X1U+5GIeeZbL+HklgwfuXpzkE0kOw/uga/vzG1V15ZR1m3pJsohY6GOmqs6vqpUMrsC7GHg3sGeS05L8Sb/ppIfd0Z17vvk6iaMZ7IhoHjnl0oDu3tOvBl5bVYf1nUdK8iRgNfB84G4GB+1fV1U/6jVY4yx0SfOme9btDsDPgZVVdW7PkZrmlIukkUmyS5L3Jzk1yUsYFPmxDK5ifk2/6drnHrqkkenug343cDlwGLAng4P2x1fVuj6zLQYWuqSRSfK9qvr97v2ODA6ELquqX8z+kxoFp1wkjdKvNr/pHkW3wTJfOO6hSxqZJA/xyCPnAjyWwTx68BGJ885Cl6RGOOUiSY2w0CWpERa6JDXCQpekRvwvro3OXvcV6QcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ugpWbVU8tF6b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "aafc99fd-b422-4e72-977f-b2c96f2f1840"
      },
      "source": [
        "provas[['P1', 'P2', 'P3', 'P4', 'MF']].plot(kind = 'box')"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c888ccd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kyU5HZqfme+OLfS3Jn6xpUV30e4C/nmRb00WsE0u2xYCua7rifxdJXrG9X/PXSfo9wE9pC9tXSLqxs8LWGZLeY/veJDc3UuHa+pakz0u6StJ5zZbS3UAMoWBprGu6wHar0/NW50LWtZKebbaqZiT5XJKLkgxJuknStwckvCXpHkl3JSliuHUgA9z2tKTvSrrY9gu2x5quqSGsa7rgQkn7bT8p6V81Pwbe97fP4WRJXkjyN03XsVrMxASAQg1kDxwA+gEBDgCFIsABoFAEOAAUigAHgEIR4ABQKAIcAApFgANAof4f/UZbr4Xn1ngAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yRsgmvrtQsq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "8d653588-fa1e-44d6-9a56-840182e34a7c"
      },
      "source": [
        "provas[['P1', 'P2', 'P3', 'P4']].transpose().plot(kind = 'line')"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c88802518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "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": "fAH1njuAtcP5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "outputId": "558a0fe6-397e-484a-9bea-b566f2a4fb48"
      },
      "source": [
        "provas[['P1', 'P2','P3','P4','MF' ]].corr().style.background_gradient(cmap='coolwarm')"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        }    #T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row4_col4 {\n",
              "            background-color:  #b40426;\n",
              "            color:  #f1f1f1;\n",
              "        }</style><table id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >P1</th>        <th class=\"col_heading level0 col1\" >P2</th>        <th class=\"col_heading level0 col2\" >P3</th>        <th class=\"col_heading level0 col3\" >P4</th>        <th class=\"col_heading level0 col4\" >MF</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >P1</th>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.937362</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.596510</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.313908</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.872745</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >P2</th>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.937362</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.473987</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.208409</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.801711</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >P3</th>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.596510</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.473987</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.854139</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.894969</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >P4</th>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.313908</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.208409</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.854139</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.709931</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >MF</th>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.872745</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.801711</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.894969</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.709931</td>\n",
              "                        <td id=\"T_9e7dedfc_b881_11ea_8ca4_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f1c88780080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-gTbFKo3YrW",
        "colab_type": "text"
      },
      "source": [
        "### **Importando dados**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwEFNnE66SI9",
        "colab_type": "text"
      },
      "source": [
        "#### Exemplo 1: Lendo arquivos da amostra do Google Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0mA0hy7yph_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-S0tyJH3cpc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "casas = pd.read_csv('/content/sample_data/california_housing_train.csv')"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5iYJJoW4K2c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "1e984d5f-49f4-45a8-d0bd-2cfe510afa90"
      },
      "source": [
        "casas"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16995</th>\n",
              "      <td>-124.26</td>\n",
              "      <td>40.58</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2217.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>907.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>2.3571</td>\n",
              "      <td>111400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16996</th>\n",
              "      <td>-124.27</td>\n",
              "      <td>40.69</td>\n",
              "      <td>36.0</td>\n",
              "      <td>2349.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>1194.0</td>\n",
              "      <td>465.0</td>\n",
              "      <td>2.5179</td>\n",
              "      <td>79000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16997</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.84</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2677.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>1244.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>3.0313</td>\n",
              "      <td>103600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16998</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.80</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2672.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>1298.0</td>\n",
              "      <td>478.0</td>\n",
              "      <td>1.9797</td>\n",
              "      <td>85800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16999</th>\n",
              "      <td>-124.35</td>\n",
              "      <td>40.54</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1820.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>3.0147</td>\n",
              "      <td>94600.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_income  median_house_value\n",
              "0        -114.31     34.19  ...         1.4936             66900.0\n",
              "1        -114.47     34.40  ...         1.8200             80100.0\n",
              "2        -114.56     33.69  ...         1.6509             85700.0\n",
              "3        -114.57     33.64  ...         3.1917             73400.0\n",
              "4        -114.57     33.57  ...         1.9250             65500.0\n",
              "...          ...       ...  ...            ...                 ...\n",
              "16995    -124.26     40.58  ...         2.3571            111400.0\n",
              "16996    -124.27     40.69  ...         2.5179             79000.0\n",
              "16997    -124.30     41.84  ...         3.0313            103600.0\n",
              "16998    -124.30     41.80  ...         1.9797             85800.0\n",
              "16999    -124.35     40.54  ...         3.0147             94600.0\n",
              "\n",
              "[17000 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rUAqRcgr6ggf"
      },
      "source": [
        "#### Exemplo 2: Lendo arquivos do Google Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuzv4VhIKQjR",
        "colab_type": "text"
      },
      "source": [
        "Na aba da esquerda, no Google Colab, clique no botão 'Montar Drive'. Após você autorizar a conexão, você poderá acessar facilmente qualquer arquivo do seu Google Drive aqui no Colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIvJloUh5JSW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "treino = pd.read_csv('/content/drive/My Drive/treinamento.csv')  #só vai funcionar pra mim, pois é o meu drive"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cT4adToCykKv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "afe352c7-3863-4d96-a626-9a5fe5af0c6c"
      },
      "source": [
        "treino.head()"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
              "      <td>female</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24160</td>\n",
              "      <td>211.3375</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Allison, Master. Hudson Trevor</td>\n",
              "      <td>male</td>\n",
              "      <td>0.9167</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Anderson, Mr. Harry</td>\n",
              "      <td>male</td>\n",
              "      <td>48.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>19952</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
              "      <td>female</td>\n",
              "      <td>63.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>13502</td>\n",
              "      <td>77.9583</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   name     sex  ...  pclass  survived\n",
              "0         Allen, Miss. Elisabeth Walton  female  ...       1         1\n",
              "1        Allison, Master. Hudson Trevor    male  ...       1         1\n",
              "2  Allison, Mr. Hudson Joshua Creighton    male  ...       1         0\n",
              "3                   Anderson, Mr. Harry    male  ...       1         1\n",
              "4     Andrews, Miss. Kornelia Theodosia  female  ...       1         1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WwE6951d6jNB"
      },
      "source": [
        "#### Exemplo 3: Lendo arquivos xlsx direto do Github"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XJM-OsVNKnkI",
        "colab_type": "text"
      },
      "source": [
        "Exemplo de leitura de dados do Excel, direto do github. São os dados de Covid-19 pelo mundo, oferecidos pelo Our World In Data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3Xb5q1m5Ni1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = 'https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-data.xlsx?raw=true'\n",
        "\n",
        "covid = pd.read_excel(url)"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sqv7i4A_5ilw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "30b0f06c-886c-4bc2-cbe4-cdb6a30deb4a"
      },
      "source": [
        "covid.head()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>date</th>\n",
              "      <th>total_cases</th>\n",
              "      <th>new_cases</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>total_cases_per_million</th>\n",
              "      <th>new_cases_per_million</th>\n",
              "      <th>total_deaths_per_million</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>total_tests</th>\n",
              "      <th>new_tests</th>\n",
              "      <th>total_tests_per_thousand</th>\n",
              "      <th>new_tests_per_thousand</th>\n",
              "      <th>new_tests_smoothed</th>\n",
              "      <th>new_tests_smoothed_per_thousand</th>\n",
              "      <th>tests_units</th>\n",
              "      <th>stringency_index</th>\n",
              "      <th>population</th>\n",
              "      <th>population_density</th>\n",
              "      <th>median_age</th>\n",
              "      <th>aged_65_older</th>\n",
              "      <th>aged_70_older</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
              "      <th>cvd_death_rate</th>\n",
              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2019-12-31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  iso_code continent  ... hospital_beds_per_thousand life_expectancy\n",
              "0      AFG      Asia  ...                        0.5           64.83\n",
              "1      AFG      Asia  ...                        0.5           64.83\n",
              "2      AFG      Asia  ...                        0.5           64.83\n",
              "3      AFG      Asia  ...                        0.5           64.83\n",
              "4      AFG      Asia  ...                        0.5           64.83\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cik05hc65mFj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "9f7f55dd-44b7-4317-eed1-d53b6b363fea"
      },
      "source": [
        "covid.describe()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_cases</th>\n",
              "      <th>new_cases</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>total_cases_per_million</th>\n",
              "      <th>new_cases_per_million</th>\n",
              "      <th>total_deaths_per_million</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>total_tests</th>\n",
              "      <th>new_tests</th>\n",
              "      <th>total_tests_per_thousand</th>\n",
              "      <th>new_tests_per_thousand</th>\n",
              "      <th>new_tests_smoothed</th>\n",
              "      <th>new_tests_smoothed_per_thousand</th>\n",
              "      <th>stringency_index</th>\n",
              "      <th>population</th>\n",
              "      <th>population_density</th>\n",
              "      <th>median_age</th>\n",
              "      <th>aged_65_older</th>\n",
              "      <th>aged_70_older</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
              "      <th>cvd_death_rate</th>\n",
              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.632400e+04</td>\n",
              "      <td>26324.000000</td>\n",
              "      <td>26324.000000</td>\n",
              "      <td>26324.000000</td>\n",
              "      <td>26260.000000</td>\n",
              "      <td>26260.000000</td>\n",
              "      <td>26260.000000</td>\n",
              "      <td>26260.000000</td>\n",
              "      <td>7.523000e+03</td>\n",
              "      <td>6848.000000</td>\n",
              "      <td>7523.000000</td>\n",
              "      <td>6848.000000</td>\n",
              "      <td>8138.000000</td>\n",
              "      <td>8138.000000</td>\n",
              "      <td>21299.000000</td>\n",
              "      <td>2.647500e+04</td>\n",
              "      <td>25385.000000</td>\n",
              "      <td>23980.000000</td>\n",
              "      <td>23641.000000</td>\n",
              "      <td>23857.000000</td>\n",
              "      <td>23709.000000</td>\n",
              "      <td>15845.000000</td>\n",
              "      <td>24010.00000</td>\n",
              "      <td>24787.000000</td>\n",
              "      <td>19245.000000</td>\n",
              "      <td>19023.000000</td>\n",
              "      <td>10810.000000</td>\n",
              "      <td>21953.000000</td>\n",
              "      <td>26171.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.206331e+04</td>\n",
              "      <td>742.360887</td>\n",
              "      <td>1938.110546</td>\n",
              "      <td>37.524540</td>\n",
              "      <td>786.475861</td>\n",
              "      <td>15.216119</td>\n",
              "      <td>32.259046</td>\n",
              "      <td>0.532144</td>\n",
              "      <td>5.054456e+05</td>\n",
              "      <td>14365.432827</td>\n",
              "      <td>19.572095</td>\n",
              "      <td>0.471663</td>\n",
              "      <td>12952.264316</td>\n",
              "      <td>0.444605</td>\n",
              "      <td>58.139225</td>\n",
              "      <td>1.001075e+08</td>\n",
              "      <td>373.530250</td>\n",
              "      <td>31.886455</td>\n",
              "      <td>9.600010</td>\n",
              "      <td>6.098714</td>\n",
              "      <td>22088.254050</td>\n",
              "      <td>10.960234</td>\n",
              "      <td>247.75268</td>\n",
              "      <td>8.020490</td>\n",
              "      <td>11.114493</td>\n",
              "      <td>32.650297</td>\n",
              "      <td>54.066730</td>\n",
              "      <td>3.179424</td>\n",
              "      <td>74.433625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.212325e+05</td>\n",
              "      <td>6559.985260</td>\n",
              "      <td>19022.679917</td>\n",
              "      <td>336.870958</td>\n",
              "      <td>2053.528632</td>\n",
              "      <td>63.390111</td>\n",
              "      <td>111.467663</td>\n",
              "      <td>3.138024</td>\n",
              "      <td>1.904324e+06</td>\n",
              "      <td>48280.771173</td>\n",
              "      <td>32.351658</td>\n",
              "      <td>0.755190</td>\n",
              "      <td>42638.216821</td>\n",
              "      <td>0.654483</td>\n",
              "      <td>31.778180</td>\n",
              "      <td>6.584119e+08</td>\n",
              "      <td>1715.910842</td>\n",
              "      <td>8.988310</td>\n",
              "      <td>6.416237</td>\n",
              "      <td>4.399612</td>\n",
              "      <td>20933.098395</td>\n",
              "      <td>18.292312</td>\n",
              "      <td>118.39188</td>\n",
              "      <td>4.085027</td>\n",
              "      <td>10.542379</td>\n",
              "      <td>13.309658</td>\n",
              "      <td>31.262364</td>\n",
              "      <td>2.576155</td>\n",
              "      <td>7.253754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-2461.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1918.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-265.189000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-41.023000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.090000e+02</td>\n",
              "      <td>0.137000</td>\n",
              "      <td>15.100000</td>\n",
              "      <td>1.144000</td>\n",
              "      <td>0.526000</td>\n",
              "      <td>661.240000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>79.37000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>7.700000</td>\n",
              "      <td>1.188000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>53.280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.400000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.868800e+04</td>\n",
              "      <td>803.750000</td>\n",
              "      <td>0.913500</td>\n",
              "      <td>0.049000</td>\n",
              "      <td>915.000000</td>\n",
              "      <td>0.049000</td>\n",
              "      <td>33.330000</td>\n",
              "      <td>1.967998e+06</td>\n",
              "      <td>39.497000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>3.808000</td>\n",
              "      <td>2.242000</td>\n",
              "      <td>6222.554000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>151.68900</td>\n",
              "      <td>5.310000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>21.400000</td>\n",
              "      <td>23.437000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>70.780000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.380000e+02</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>82.173000</td>\n",
              "      <td>0.481000</td>\n",
              "      <td>0.809500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.990900e+04</td>\n",
              "      <td>2652.500000</td>\n",
              "      <td>5.209000</td>\n",
              "      <td>0.209000</td>\n",
              "      <td>2921.000000</td>\n",
              "      <td>0.212000</td>\n",
              "      <td>69.440000</td>\n",
              "      <td>9.006400e+06</td>\n",
              "      <td>90.672000</td>\n",
              "      <td>31.900000</td>\n",
              "      <td>7.151000</td>\n",
              "      <td>4.625000</td>\n",
              "      <td>15469.207000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>235.84800</td>\n",
              "      <td>7.110000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>31.400000</td>\n",
              "      <td>59.550000</td>\n",
              "      <td>2.540000</td>\n",
              "      <td>75.880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.686000e+03</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>60.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>530.088750</td>\n",
              "      <td>7.992000</td>\n",
              "      <td>11.808000</td>\n",
              "      <td>0.099000</td>\n",
              "      <td>3.044900e+05</td>\n",
              "      <td>8472.500000</td>\n",
              "      <td>24.875500</td>\n",
              "      <td>0.630500</td>\n",
              "      <td>8013.000000</td>\n",
              "      <td>0.612000</td>\n",
              "      <td>83.330000</td>\n",
              "      <td>3.297185e+07</td>\n",
              "      <td>222.873000</td>\n",
              "      <td>40.100000</td>\n",
              "      <td>14.952000</td>\n",
              "      <td>9.732000</td>\n",
              "      <td>34272.360000</td>\n",
              "      <td>13.400000</td>\n",
              "      <td>317.84000</td>\n",
              "      <td>10.080000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>40.850000</td>\n",
              "      <td>83.841000</td>\n",
              "      <td>4.210000</td>\n",
              "      <td>80.220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.770954e+06</td>\n",
              "      <td>188633.000000</td>\n",
              "      <td>493898.000000</td>\n",
              "      <td>10512.000000</td>\n",
              "      <td>32204.814000</td>\n",
              "      <td>4944.376000</td>\n",
              "      <td>1237.551000</td>\n",
              "      <td>200.040000</td>\n",
              "      <td>2.920782e+07</td>\n",
              "      <td>637587.000000</td>\n",
              "      <td>300.578000</td>\n",
              "      <td>13.467000</td>\n",
              "      <td>538019.000000</td>\n",
              "      <td>9.451000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>7.794799e+09</td>\n",
              "      <td>19347.500000</td>\n",
              "      <td>48.200000</td>\n",
              "      <td>27.049000</td>\n",
              "      <td>18.493000</td>\n",
              "      <td>116935.600000</td>\n",
              "      <td>77.600000</td>\n",
              "      <td>724.41700</td>\n",
              "      <td>23.360000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>78.100000</td>\n",
              "      <td>98.999000</td>\n",
              "      <td>13.800000</td>\n",
              "      <td>86.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        total_cases      new_cases  ...  hospital_beds_per_thousand  life_expectancy\n",
              "count  2.632400e+04   26324.000000  ...                21953.000000     26171.000000\n",
              "mean   3.206331e+04     742.360887  ...                    3.179424        74.433625\n",
              "std    3.212325e+05    6559.985260  ...                    2.576155         7.253754\n",
              "min    0.000000e+00   -2461.000000  ...                    0.100000        53.280000\n",
              "25%    1.400000e+01       0.000000  ...                    1.400000        70.780000\n",
              "50%    2.380000e+02       4.000000  ...                    2.540000        75.880000\n",
              "75%    2.686000e+03      69.000000  ...                    4.210000        80.220000\n",
              "max    9.770954e+06  188633.000000  ...                   13.800000        86.750000\n",
              "\n",
              "[8 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1mGqQmLJ66t0"
      },
      "source": [
        "## **PROJETO**: Lendo arquivos de uma webpage (dependendo da webpage, pode não funcionar)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XwDJKXA7F_c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = 'https://pt.wikipedia.org/wiki/Salvador'\n",
        "wiki = pd.read_html(url, decimal= ',', thousands= '.')"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVCJTIok7W23",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "outputId": "4f1d41c9-db49-45d7-eec2-b339ce5cba41"
      },
      "source": [
        "wiki[4]"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"14\" halign=\"left\">Dados climatológicos para Salvador (Ondina)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Mês</th>\n",
              "      <th>Jan</th>\n",
              "      <th>Fev</th>\n",
              "      <th>Mar</th>\n",
              "      <th>Abr</th>\n",
              "      <th>Mai</th>\n",
              "      <th>Jun</th>\n",
              "      <th>Jul</th>\n",
              "      <th>Ago</th>\n",
              "      <th>Set</th>\n",
              "      <th>Out</th>\n",
              "      <th>Nov</th>\n",
              "      <th>Dez</th>\n",
              "      <th>Ano</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Temperatura máxima recorde (°C)</td>\n",
              "      <td>34.3</td>\n",
              "      <td>34.7</td>\n",
              "      <td>35.6</td>\n",
              "      <td>34.5</td>\n",
              "      <td>31.6</td>\n",
              "      <td>30.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.3</td>\n",
              "      <td>30.7</td>\n",
              "      <td>32.4</td>\n",
              "      <td>33.1</td>\n",
              "      <td>34.3</td>\n",
              "      <td>35.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Temperatura máxima média (°C)</td>\n",
              "      <td>30.9</td>\n",
              "      <td>31</td>\n",
              "      <td>30.7</td>\n",
              "      <td>29.5</td>\n",
              "      <td>28.3</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.5</td>\n",
              "      <td>26.7</td>\n",
              "      <td>27.7</td>\n",
              "      <td>29</td>\n",
              "      <td>29.8</td>\n",
              "      <td>30.5</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Temperatura média compensada (°C)</td>\n",
              "      <td>27</td>\n",
              "      <td>27.1</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>24.4</td>\n",
              "      <td>23.8</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>26.6</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Temperatura mínima média (°C)</td>\n",
              "      <td>24</td>\n",
              "      <td>24.1</td>\n",
              "      <td>24.2</td>\n",
              "      <td>23.7</td>\n",
              "      <td>23</td>\n",
              "      <td>22.1</td>\n",
              "      <td>21.3</td>\n",
              "      <td>21.1</td>\n",
              "      <td>21.6</td>\n",
              "      <td>22.5</td>\n",
              "      <td>23.1</td>\n",
              "      <td>23.6</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Temperatura mínima recorde (°C)</td>\n",
              "      <td>19.8</td>\n",
              "      <td>19.5</td>\n",
              "      <td>20.8</td>\n",
              "      <td>19.4</td>\n",
              "      <td>18</td>\n",
              "      <td>18.2</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.7</td>\n",
              "      <td>17.6</td>\n",
              "      <td>18.3</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.8</td>\n",
              "      <td>17.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Precipitação (mm)</td>\n",
              "      <td>82.5</td>\n",
              "      <td>107.2</td>\n",
              "      <td>156.8</td>\n",
              "      <td>295.7</td>\n",
              "      <td>279.8</td>\n",
              "      <td>245.6</td>\n",
              "      <td>208.6</td>\n",
              "      <td>133.5</td>\n",
              "      <td>101.6</td>\n",
              "      <td>95.2</td>\n",
              "      <td>106.5</td>\n",
              "      <td>58.1</td>\n",
              "      <td>1 871,1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dias com precipitação (≥ 1 mm)</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Umidade relativa compensada (%)</td>\n",
              "      <td>78.5</td>\n",
              "      <td>79.4</td>\n",
              "      <td>80.7</td>\n",
              "      <td>82.7</td>\n",
              "      <td>83.8</td>\n",
              "      <td>83.9</td>\n",
              "      <td>82.7</td>\n",
              "      <td>81.2</td>\n",
              "      <td>80.2</td>\n",
              "      <td>79.9</td>\n",
              "      <td>80.5</td>\n",
              "      <td>79.7</td>\n",
              "      <td>81.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Horas de sol</td>\n",
              "      <td>246.9</td>\n",
              "      <td>215</td>\n",
              "      <td>227.2</td>\n",
              "      <td>194.1</td>\n",
              "      <td>165.8</td>\n",
              "      <td>147.8</td>\n",
              "      <td>169.1</td>\n",
              "      <td>189.1</td>\n",
              "      <td>211.4</td>\n",
              "      <td>235.5</td>\n",
              "      <td>210.9</td>\n",
              "      <td>230.2</td>\n",
              "      <td>2 443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Dados climatológicos para Salvador (Ondina)  ...                                                   \n",
              "                                                 Mês  ...                                                Ano\n",
              "0                    Temperatura máxima recorde (°C)  ...                                               35.6\n",
              "1                      Temperatura máxima média (°C)  ...                                                 29\n",
              "2                  Temperatura média compensada (°C)  ...                                               25.6\n",
              "3                      Temperatura mínima média (°C)  ...                                               22.9\n",
              "4                    Temperatura mínima recorde (°C)  ...                                               17.5\n",
              "5                                  Precipitação (mm)  ...                                            1 871,1\n",
              "6                     Dias com precipitação (≥ 1 mm)  ...                                                150\n",
              "7                    Umidade relativa compensada (%)  ...                                               81.1\n",
              "8                                       Horas de sol  ...                                              2 443\n",
              "9  Fonte: Instituto Nacional de Meteorologia (INM...  ...  Fonte: Instituto Nacional de Meteorologia (INM...\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHGhE3jc_gOq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "salvador = wiki[4]"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRR5GtHU7ss5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "colunas = salvador.columns "
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBGeKJDMMozK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "dc06c3a1-30c4-49f2-9178-7aa9861c56bf"
      },
      "source": [
        "colunas"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([('Dados climatológicos para Salvador (Ondina)', 'Mês'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Jan'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Fev'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Mar'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Abr'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Mai'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Jun'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Jul'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Ago'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Set'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Out'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Nov'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Dez'),\n",
              "            ('Dados climatológicos para Salvador (Ondina)', 'Ano')],\n",
              "           )"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULJjWIKZ_mmI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "9090b9ae-4483-485f-a538-a922a57fcaea"
      },
      "source": [
        "cols = []\n",
        "for i in range(len(colunas)):\n",
        "    cols.append(colunas[i][1])\n",
        "cols[0] = 'Medida'\n",
        "print(cols)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Medida', 'Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez', 'Ano']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sk5JISEl7meu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "salvador.columns = cols"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Py7rf1OMt8H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "outputId": "27fd96ad-5941-4039-dbb5-b87b0db8bdea"
      },
      "source": [
        "salvador"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Medida</th>\n",
              "      <th>Jan</th>\n",
              "      <th>Fev</th>\n",
              "      <th>Mar</th>\n",
              "      <th>Abr</th>\n",
              "      <th>Mai</th>\n",
              "      <th>Jun</th>\n",
              "      <th>Jul</th>\n",
              "      <th>Ago</th>\n",
              "      <th>Set</th>\n",
              "      <th>Out</th>\n",
              "      <th>Nov</th>\n",
              "      <th>Dez</th>\n",
              "      <th>Ano</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Temperatura máxima recorde (°C)</td>\n",
              "      <td>34.3</td>\n",
              "      <td>34.7</td>\n",
              "      <td>35.6</td>\n",
              "      <td>34.5</td>\n",
              "      <td>31.6</td>\n",
              "      <td>30.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.3</td>\n",
              "      <td>30.7</td>\n",
              "      <td>32.4</td>\n",
              "      <td>33.1</td>\n",
              "      <td>34.3</td>\n",
              "      <td>35.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Temperatura máxima média (°C)</td>\n",
              "      <td>30.9</td>\n",
              "      <td>31</td>\n",
              "      <td>30.7</td>\n",
              "      <td>29.5</td>\n",
              "      <td>28.3</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.5</td>\n",
              "      <td>26.7</td>\n",
              "      <td>27.7</td>\n",
              "      <td>29</td>\n",
              "      <td>29.8</td>\n",
              "      <td>30.5</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Temperatura média compensada (°C)</td>\n",
              "      <td>27</td>\n",
              "      <td>27.1</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>24.4</td>\n",
              "      <td>23.8</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>26.6</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Temperatura mínima média (°C)</td>\n",
              "      <td>24</td>\n",
              "      <td>24.1</td>\n",
              "      <td>24.2</td>\n",
              "      <td>23.7</td>\n",
              "      <td>23</td>\n",
              "      <td>22.1</td>\n",
              "      <td>21.3</td>\n",
              "      <td>21.1</td>\n",
              "      <td>21.6</td>\n",
              "      <td>22.5</td>\n",
              "      <td>23.1</td>\n",
              "      <td>23.6</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Temperatura mínima recorde (°C)</td>\n",
              "      <td>19.8</td>\n",
              "      <td>19.5</td>\n",
              "      <td>20.8</td>\n",
              "      <td>19.4</td>\n",
              "      <td>18</td>\n",
              "      <td>18.2</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.7</td>\n",
              "      <td>17.6</td>\n",
              "      <td>18.3</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.8</td>\n",
              "      <td>17.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Precipitação (mm)</td>\n",
              "      <td>82.5</td>\n",
              "      <td>107.2</td>\n",
              "      <td>156.8</td>\n",
              "      <td>295.7</td>\n",
              "      <td>279.8</td>\n",
              "      <td>245.6</td>\n",
              "      <td>208.6</td>\n",
              "      <td>133.5</td>\n",
              "      <td>101.6</td>\n",
              "      <td>95.2</td>\n",
              "      <td>106.5</td>\n",
              "      <td>58.1</td>\n",
              "      <td>1 871,1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dias com precipitação (≥ 1 mm)</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Umidade relativa compensada (%)</td>\n",
              "      <td>78.5</td>\n",
              "      <td>79.4</td>\n",
              "      <td>80.7</td>\n",
              "      <td>82.7</td>\n",
              "      <td>83.8</td>\n",
              "      <td>83.9</td>\n",
              "      <td>82.7</td>\n",
              "      <td>81.2</td>\n",
              "      <td>80.2</td>\n",
              "      <td>79.9</td>\n",
              "      <td>80.5</td>\n",
              "      <td>79.7</td>\n",
              "      <td>81.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Horas de sol</td>\n",
              "      <td>246.9</td>\n",
              "      <td>215</td>\n",
              "      <td>227.2</td>\n",
              "      <td>194.1</td>\n",
              "      <td>165.8</td>\n",
              "      <td>147.8</td>\n",
              "      <td>169.1</td>\n",
              "      <td>189.1</td>\n",
              "      <td>211.4</td>\n",
              "      <td>235.5</td>\n",
              "      <td>210.9</td>\n",
              "      <td>230.2</td>\n",
              "      <td>2 443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "      <td>Fonte: Instituto Nacional de Meteorologia (INM...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              Medida  ...                                                Ano\n",
              "0                    Temperatura máxima recorde (°C)  ...                                               35.6\n",
              "1                      Temperatura máxima média (°C)  ...                                                 29\n",
              "2                  Temperatura média compensada (°C)  ...                                               25.6\n",
              "3                      Temperatura mínima média (°C)  ...                                               22.9\n",
              "4                    Temperatura mínima recorde (°C)  ...                                               17.5\n",
              "5                                  Precipitação (mm)  ...                                            1 871,1\n",
              "6                     Dias com precipitação (≥ 1 mm)  ...                                                150\n",
              "7                    Umidade relativa compensada (%)  ...                                               81.1\n",
              "8                                       Horas de sol  ...                                              2 443\n",
              "9  Fonte: Instituto Nacional de Meteorologia (INM...  ...  Fonte: Instituto Nacional de Meteorologia (INM...\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gN2mW4eO7lCO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "salvador = salvador.drop(9, axis = 0)"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IIlyvhiAjyN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "07446313-8a38-4fd6-a877-d3dae3fe21af"
      },
      "source": [
        "salvador"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Medida</th>\n",
              "      <th>Jan</th>\n",
              "      <th>Fev</th>\n",
              "      <th>Mar</th>\n",
              "      <th>Abr</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Temperatura máxima recorde (°C)</td>\n",
              "      <td>34.3</td>\n",
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              "      <td>35.6</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Temperatura máxima média (°C)</td>\n",
              "      <td>30.9</td>\n",
              "      <td>31</td>\n",
              "      <td>30.7</td>\n",
              "      <td>29.5</td>\n",
              "      <td>28.3</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.5</td>\n",
              "      <td>26.7</td>\n",
              "      <td>27.7</td>\n",
              "      <td>29</td>\n",
              "      <td>29.8</td>\n",
              "      <td>30.5</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Temperatura média compensada (°C)</td>\n",
              "      <td>27</td>\n",
              "      <td>27.1</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>24.4</td>\n",
              "      <td>23.8</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>26.6</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Temperatura mínima média (°C)</td>\n",
              "      <td>24</td>\n",
              "      <td>24.1</td>\n",
              "      <td>24.2</td>\n",
              "      <td>23.7</td>\n",
              "      <td>23</td>\n",
              "      <td>22.1</td>\n",
              "      <td>21.3</td>\n",
              "      <td>21.1</td>\n",
              "      <td>21.6</td>\n",
              "      <td>22.5</td>\n",
              "      <td>23.1</td>\n",
              "      <td>23.6</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Temperatura mínima recorde (°C)</td>\n",
              "      <td>19.8</td>\n",
              "      <td>19.5</td>\n",
              "      <td>20.8</td>\n",
              "      <td>19.4</td>\n",
              "      <td>18</td>\n",
              "      <td>18.2</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.7</td>\n",
              "      <td>17.6</td>\n",
              "      <td>18.3</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.8</td>\n",
              "      <td>17.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Precipitação (mm)</td>\n",
              "      <td>82.5</td>\n",
              "      <td>107.2</td>\n",
              "      <td>156.8</td>\n",
              "      <td>295.7</td>\n",
              "      <td>279.8</td>\n",
              "      <td>245.6</td>\n",
              "      <td>208.6</td>\n",
              "      <td>133.5</td>\n",
              "      <td>101.6</td>\n",
              "      <td>95.2</td>\n",
              "      <td>106.5</td>\n",
              "      <td>58.1</td>\n",
              "      <td>1 871,1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dias com precipitação (≥ 1 mm)</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Umidade relativa compensada (%)</td>\n",
              "      <td>78.5</td>\n",
              "      <td>79.4</td>\n",
              "      <td>80.7</td>\n",
              "      <td>82.7</td>\n",
              "      <td>83.8</td>\n",
              "      <td>83.9</td>\n",
              "      <td>82.7</td>\n",
              "      <td>81.2</td>\n",
              "      <td>80.2</td>\n",
              "      <td>79.9</td>\n",
              "      <td>80.5</td>\n",
              "      <td>79.7</td>\n",
              "      <td>81.1</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Horas de sol</td>\n",
              "      <td>246.9</td>\n",
              "      <td>215</td>\n",
              "      <td>227.2</td>\n",
              "      <td>194.1</td>\n",
              "      <td>165.8</td>\n",
              "      <td>147.8</td>\n",
              "      <td>169.1</td>\n",
              "      <td>189.1</td>\n",
              "      <td>211.4</td>\n",
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              "      <td>210.9</td>\n",
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              "      <td>2 443</td>\n",
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              "                              Medida    Jan    Fev  ...    Nov    Dez      Ano\n",
              "0    Temperatura máxima recorde (°C)   34.3   34.7  ...   33.1   34.3     35.6\n",
              "1      Temperatura máxima média (°C)   30.9     31  ...   29.8   30.5       29\n",
              "2  Temperatura média compensada (°C)     27   27.1  ...   26.1   26.6     25.6\n",
              "3      Temperatura mínima média (°C)     24   24.1  ...   23.1   23.6     22.9\n",
              "4    Temperatura mínima recorde (°C)   19.8   19.5  ...   18.9   19.8     17.5\n",
              "5                  Precipitação (mm)   82.5  107.2  ...  106.5   58.1  1 871,1\n",
              "6     Dias com precipitação (≥ 1 mm)      8     10  ...      8      6      150\n",
              "7    Umidade relativa compensada (%)   78.5   79.4  ...   80.5   79.7     81.1\n",
              "8                       Horas de sol  246.9    215  ...  210.9  230.2    2 443\n",
              "\n",
              "[9 rows x 14 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 115
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4LzbkhXM7S2k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "salvador.index = salvador['Medida']"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2a_Iml7AMkT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
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        "outputId": "e2ed69ce-df72-4184-9769-390a5c665530"
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      "source": [
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      "execution_count": 119,
      "outputs": [
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              "      <th>Temperatura máxima recorde (°C)</th>\n",
              "      <td>Temperatura máxima recorde (°C)</td>\n",
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              "      <td>31.6</td>\n",
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              "      <td>31.3</td>\n",
              "      <td>30.7</td>\n",
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              "      <td>34.3</td>\n",
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              "    <tr>\n",
              "      <th>Temperatura máxima média (°C)</th>\n",
              "      <td>Temperatura máxima média (°C)</td>\n",
              "      <td>30.9</td>\n",
              "      <td>31</td>\n",
              "      <td>30.7</td>\n",
              "      <td>29.5</td>\n",
              "      <td>28.3</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.5</td>\n",
              "      <td>26.7</td>\n",
              "      <td>27.7</td>\n",
              "      <td>29</td>\n",
              "      <td>29.8</td>\n",
              "      <td>30.5</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperatura média compensada (°C)</th>\n",
              "      <td>Temperatura média compensada (°C)</td>\n",
              "      <td>27</td>\n",
              "      <td>27.1</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>24.4</td>\n",
              "      <td>23.8</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>26.6</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperatura mínima média (°C)</th>\n",
              "      <td>Temperatura mínima média (°C)</td>\n",
              "      <td>24</td>\n",
              "      <td>24.1</td>\n",
              "      <td>24.2</td>\n",
              "      <td>23.7</td>\n",
              "      <td>23</td>\n",
              "      <td>22.1</td>\n",
              "      <td>21.3</td>\n",
              "      <td>21.1</td>\n",
              "      <td>21.6</td>\n",
              "      <td>22.5</td>\n",
              "      <td>23.1</td>\n",
              "      <td>23.6</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperatura mínima recorde (°C)</th>\n",
              "      <td>Temperatura mínima recorde (°C)</td>\n",
              "      <td>19.8</td>\n",
              "      <td>19.5</td>\n",
              "      <td>20.8</td>\n",
              "      <td>19.4</td>\n",
              "      <td>18</td>\n",
              "      <td>18.2</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.7</td>\n",
              "      <td>17.6</td>\n",
              "      <td>18.3</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.8</td>\n",
              "      <td>17.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precipitação (mm)</th>\n",
              "      <td>Precipitação (mm)</td>\n",
              "      <td>82.5</td>\n",
              "      <td>107.2</td>\n",
              "      <td>156.8</td>\n",
              "      <td>295.7</td>\n",
              "      <td>279.8</td>\n",
              "      <td>245.6</td>\n",
              "      <td>208.6</td>\n",
              "      <td>133.5</td>\n",
              "      <td>101.6</td>\n",
              "      <td>95.2</td>\n",
              "      <td>106.5</td>\n",
              "      <td>58.1</td>\n",
              "      <td>1 871,1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dias com precipitação (≥ 1 mm)</th>\n",
              "      <td>Dias com precipitação (≥ 1 mm)</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Umidade relativa compensada (%)</th>\n",
              "      <td>Umidade relativa compensada (%)</td>\n",
              "      <td>78.5</td>\n",
              "      <td>79.4</td>\n",
              "      <td>80.7</td>\n",
              "      <td>82.7</td>\n",
              "      <td>83.8</td>\n",
              "      <td>83.9</td>\n",
              "      <td>82.7</td>\n",
              "      <td>81.2</td>\n",
              "      <td>80.2</td>\n",
              "      <td>79.9</td>\n",
              "      <td>80.5</td>\n",
              "      <td>79.7</td>\n",
              "      <td>81.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horas de sol</th>\n",
              "      <td>Horas de sol</td>\n",
              "      <td>246.9</td>\n",
              "      <td>215</td>\n",
              "      <td>227.2</td>\n",
              "      <td>194.1</td>\n",
              "      <td>165.8</td>\n",
              "      <td>147.8</td>\n",
              "      <td>169.1</td>\n",
              "      <td>189.1</td>\n",
              "      <td>211.4</td>\n",
              "      <td>235.5</td>\n",
              "      <td>210.9</td>\n",
              "      <td>230.2</td>\n",
              "      <td>2 443</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                              Medida  ...      Ano\n",
              "Medida                                                                ...         \n",
              "Temperatura máxima recorde (°C)      Temperatura máxima recorde (°C)  ...     35.6\n",
              "Temperatura máxima média (°C)          Temperatura máxima média (°C)  ...       29\n",
              "Temperatura média compensada (°C)  Temperatura média compensada (°C)  ...     25.6\n",
              "Temperatura mínima média (°C)          Temperatura mínima média (°C)  ...     22.9\n",
              "Temperatura mínima recorde (°C)      Temperatura mínima recorde (°C)  ...     17.5\n",
              "Precipitação (mm)                                  Precipitação (mm)  ...  1 871,1\n",
              "Dias com precipitação (≥ 1 mm)        Dias com precipitação (≥ 1 mm)  ...      150\n",
              "Umidade relativa compensada (%)      Umidade relativa compensada (%)  ...     81.1\n",
              "Horas de sol                                            Horas de sol  ...    2 443\n",
              "\n",
              "[9 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aVPuFcZBn7E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "salvador.drop('Medida', axis = 1, inplace = True)"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oxb0gKNQB_XE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "2c715c75-c514-4936-8605-d439b6dc2980"
      },
      "source": [
        "salvador"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Jan</th>\n",
              "      <th>Fev</th>\n",
              "      <th>Mar</th>\n",
              "      <th>Abr</th>\n",
              "      <th>Mai</th>\n",
              "      <th>Jun</th>\n",
              "      <th>Jul</th>\n",
              "      <th>Ago</th>\n",
              "      <th>Set</th>\n",
              "      <th>Out</th>\n",
              "      <th>Nov</th>\n",
              "      <th>Dez</th>\n",
              "      <th>Ano</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medida</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Temperatura máxima recorde (°C)</th>\n",
              "      <td>34.3</td>\n",
              "      <td>34.7</td>\n",
              "      <td>35.6</td>\n",
              "      <td>34.5</td>\n",
              "      <td>31.6</td>\n",
              "      <td>30.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.3</td>\n",
              "      <td>30.7</td>\n",
              "      <td>32.4</td>\n",
              "      <td>33.1</td>\n",
              "      <td>34.3</td>\n",
              "      <td>35.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperatura máxima média (°C)</th>\n",
              "      <td>30.9</td>\n",
              "      <td>31</td>\n",
              "      <td>30.7</td>\n",
              "      <td>29.5</td>\n",
              "      <td>28.3</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.5</td>\n",
              "      <td>26.7</td>\n",
              "      <td>27.7</td>\n",
              "      <td>29</td>\n",
              "      <td>29.8</td>\n",
              "      <td>30.5</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperatura média compensada (°C)</th>\n",
              "      <td>27</td>\n",
              "      <td>27.1</td>\n",
              "      <td>27.1</td>\n",
              "      <td>26.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>24.4</td>\n",
              "      <td>23.8</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.4</td>\n",
              "      <td>25.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>26.6</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperatura mínima média (°C)</th>\n",
              "      <td>24</td>\n",
              "      <td>24.1</td>\n",
              "      <td>24.2</td>\n",
              "      <td>23.7</td>\n",
              "      <td>23</td>\n",
              "      <td>22.1</td>\n",
              "      <td>21.3</td>\n",
              "      <td>21.1</td>\n",
              "      <td>21.6</td>\n",
              "      <td>22.5</td>\n",
              "      <td>23.1</td>\n",
              "      <td>23.6</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperatura mínima recorde (°C)</th>\n",
              "      <td>19.8</td>\n",
              "      <td>19.5</td>\n",
              "      <td>20.8</td>\n",
              "      <td>19.4</td>\n",
              "      <td>18</td>\n",
              "      <td>18.2</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.7</td>\n",
              "      <td>17.6</td>\n",
              "      <td>18.3</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.8</td>\n",
              "      <td>17.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precipitação (mm)</th>\n",
              "      <td>82.5</td>\n",
              "      <td>107.2</td>\n",
              "      <td>156.8</td>\n",
              "      <td>295.7</td>\n",
              "      <td>279.8</td>\n",
              "      <td>245.6</td>\n",
              "      <td>208.6</td>\n",
              "      <td>133.5</td>\n",
              "      <td>101.6</td>\n",
              "      <td>95.2</td>\n",
              "      <td>106.5</td>\n",
              "      <td>58.1</td>\n",
              "      <td>1 871,1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dias com precipitação (≥ 1 mm)</th>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Umidade relativa compensada (%)</th>\n",
              "      <td>78.5</td>\n",
              "      <td>79.4</td>\n",
              "      <td>80.7</td>\n",
              "      <td>82.7</td>\n",
              "      <td>83.8</td>\n",
              "      <td>83.9</td>\n",
              "      <td>82.7</td>\n",
              "      <td>81.2</td>\n",
              "      <td>80.2</td>\n",
              "      <td>79.9</td>\n",
              "      <td>80.5</td>\n",
              "      <td>79.7</td>\n",
              "      <td>81.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horas de sol</th>\n",
              "      <td>246.9</td>\n",
              "      <td>215</td>\n",
              "      <td>227.2</td>\n",
              "      <td>194.1</td>\n",
              "      <td>165.8</td>\n",
              "      <td>147.8</td>\n",
              "      <td>169.1</td>\n",
              "      <td>189.1</td>\n",
              "      <td>211.4</td>\n",
              "      <td>235.5</td>\n",
              "      <td>210.9</td>\n",
              "      <td>230.2</td>\n",
              "      <td>2 443</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     Jan    Fev    Mar  ...    Nov    Dez      Ano\n",
              "Medida                                                  ...                       \n",
              "Temperatura máxima recorde (°C)     34.3   34.7   35.6  ...   33.1   34.3     35.6\n",
              "Temperatura máxima média (°C)       30.9     31   30.7  ...   29.8   30.5       29\n",
              "Temperatura média compensada (°C)     27   27.1   27.1  ...   26.1   26.6     25.6\n",
              "Temperatura mínima média (°C)         24   24.1   24.2  ...   23.1   23.6     22.9\n",
              "Temperatura mínima recorde (°C)     19.8   19.5   20.8  ...   18.9   19.8     17.5\n",
              "Precipitação (mm)                   82.5  107.2  156.8  ...  106.5   58.1  1 871,1\n",
              "Dias com precipitação (≥ 1 mm)         8     10     12  ...      8      6      150\n",
              "Umidade relativa compensada (%)     78.5   79.4   80.7  ...   80.5   79.7     81.1\n",
              "Horas de sol                       246.9    215  227.2  ...  210.9  230.2    2 443\n",
              "\n",
              "[9 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_62cIz_e6OyR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "64339ff7-42f4-4745-af05-01bcaca74f6e"
      },
      "source": [
        "salvador.info() #Dtype igual 'object' representa as variáveis do tipo string. Nesse caso, todas estão como string"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 9 entries, Temperatura máxima recorde (°C) to Horas de sol\n",
            "Data columns (total 13 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   Jan     9 non-null      object\n",
            " 1   Fev     9 non-null      object\n",
            " 2   Mar     9 non-null      object\n",
            " 3   Abr     9 non-null      object\n",
            " 4   Mai     9 non-null      object\n",
            " 5   Jun     9 non-null      object\n",
            " 6   Jul     9 non-null      object\n",
            " 7   Ago     9 non-null      object\n",
            " 8   Set     9 non-null      object\n",
            " 9   Out     9 non-null      object\n",
            " 10  Nov     9 non-null      object\n",
            " 11  Dez     9 non-null      object\n",
            " 12  Ano     9 non-null      object\n",
            "dtypes: object(13)\n",
            "memory usage: 1008.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z96ViYBIdO5B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "c59ee804-9b05-498b-e675-8232c027963e"
      },
      "source": [
        "salvador.info()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 9 entries, Temperatura máxima recorde (°C) to Horas de sol\n",
            "Data columns (total 13 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   Jan     9 non-null      object\n",
            " 1   Fev     9 non-null      object\n",
            " 2   Mar     9 non-null      object\n",
            " 3   Abr     9 non-null      object\n",
            " 4   Mai     9 non-null      object\n",
            " 5   Jun     9 non-null      object\n",
            " 6   Jul     9 non-null      object\n",
            " 7   Ago     9 non-null      object\n",
            " 8   Set     9 non-null      object\n",
            " 9   Out     9 non-null      object\n",
            " 10  Nov     9 non-null      object\n",
            " 11  Dez     9 non-null      object\n",
            " 12  Ano     9 non-null      object\n",
            "dtypes: object(13)\n",
            "memory usage: 1008.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Og6Hc56iJTPh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab.data_table import DataTable"
      ],
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adLUktgpJaAs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "431c5020-621b-4f1d-ce6f-d290b3495c5b"
      },
      "source": [
        "DataTable(provas)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[\"Alice\",\n{\n            'v': 8.9,\n            'f': \"8.9\",\n        },\n{\n            'v': 8.3,\n            'f': \"8.3\",\n        },\n{\n            'v': 8.3,\n            'f': \"8.3\",\n        },\n{\n            'v': 6.2,\n            'f': \"6.2\",\n        },\n{\n            'v': 7.9,\n            'f': \"7.9\",\n        },\n\"Aprovado\"],\n [\"Bruno\",\n{\n            'v': 5.6,\n            'f': \"5.6\",\n        },\n{\n            'v': 4.1,\n            'f': \"4.1\",\n        },\n{\n            'v': 3.6,\n            'f': \"3.6\",\n        },\n{\n            'v': 6.2,\n            'f': \"6.2\",\n        },\n{\n            'v': 4.9,\n            'f': \"4.9\",\n        },\n\"Reprovado\"],\n [\"Carol\",\n{\n            'v': 5.2,\n            'f': \"5.2\",\n        },\n{\n            'v': 5.5,\n            'f': \"5.5\",\n        },\n{\n            'v': 3.5,\n            'f': \"3.5\",\n        },\n{\n            'v': 5.2,\n            'f': \"5.2\",\n        },\n{\n            'v': 4.8,\n            'f': \"4.8\",\n        },\n\"Reprovado\"],\n [\"David\",\n{\n            'v': 8.5,\n            'f': \"8.5\",\n        },\n{\n            'v': 9.2,\n            'f': \"9.2\",\n        },\n{\n            'v': 2.0,\n            'f': \"2.0\",\n        },\n{\n            'v': 4.2,\n            'f': \"4.2\",\n        },\n{\n            'v': 6.0,\n            'f': \"6.0\",\n        },\n\"Aprovado\"],\n [\"Emily\",\n{\n            'v': 9.1,\n            'f': \"9.1\",\n        },\n{\n            'v': 9.7,\n            'f': \"9.7\",\n        },\n{\n            'v': 9.3,\n            'f': \"9.3\",\n        },\n{\n            'v': 8.3,\n            'f': \"8.3\",\n        },\n{\n            'v': 9.1,\n            'f': \"9.1\",\n        },\n\"Aprovado\"]],\n        columns: [[\"string\", \"\"], [\"number\", \"P1\"], [\"number\", \"P2\"], [\"number\", \"P3\"], [\"number\", \"P4\"], [\"number\", \"MF\"], [\"string\", \"Situa\\u00e7\\u00e3o\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "text/plain": [
              "<google.colab.data_table.DataTable object>"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>MF</th>\n",
              "      <th>Situação</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alice</th>\n",
              "      <td>8.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.9</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno</th>\n",
              "      <td>5.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carol</th>\n",
              "      <td>5.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Reprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emily</th>\n",
              "      <td>9.1</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>Aprovado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ReBMB8vb6k_W"
      },
      "source": [
        "## **EXERCÍCIO**: Análise dos dados de Redes Sociais\n",
        "\n",
        "No meu gitub, repositório Dados-Redes-Sociais ([link](https://github.com/ricardorocha86/Dados-Redes-Sociais)) possui um conjunto de dados em CSV. Ele é referente a diversas pessoas que foram expostas a um anúncio em redes sociais. \n",
        "As variáeis são: \n",
        "- User ID (identificação do usuário)\n",
        "- Gender (Gênero do usuário)\n",
        "- Age (Idade do usuário)\n",
        "- EstimatedSalary (Salário Estimado do usuário por ano)\n",
        "- Purchased (Indicação se o usuário fez a compra (1: Sim, 0: Não)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vkCVJvjSROA",
        "colab_type": "text"
      },
      "source": [
        "**Utilizando o conjunto de dados, faça as seguintes tarefas:**\n",
        "1. Remova as variáveis sem importância\n",
        "2. Verifique a distribuição dos gêneros nos dados (faça um gráfico)\n",
        "3. Crie uma variável que represente o salário mensal do usuário\n",
        "4. Crie uma nova variável com o valor 'Sim' se a pessoa comprou ou 'Não se não comprou. \n",
        "5. Verifique a distribuição entre quem comprou e quem não comprou.\n",
        "6. Faça um boxplot das variáveis pertinentes\n",
        "7. Crie uma nova variável chamada 'Classe' com os valores 'A', 'B', 'C' e 'D', utilizando os quartis dos salários estimados e verifique a contagem em cada categoria. \n",
        "8. Crie uma variável que discretize a variável Idade e verifique a contagem em cada categoria.\n",
        "9. Faça gráficos adequados para a idade categorizada e a variável classe\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihkdYJz4UFiT",
        "colab_type": "text"
      },
      "source": [
        "## Links Úteis\n",
        "- [Documentação da biblioteca Pandas](https://pandas.pydata.org/docs/)"
      ]
    }
  ]
}