tutorial_pandas.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tutorial-pandas.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Fe6-iAooyym",
        "colab_type": "text"
      },
      "source": [
        "# PANDAS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_I6keT8Lo2pG",
        "colab_type": "text"
      },
      "source": [
        "### É a biblioteca mais popular para lidar com dados em Ciência de Dados.\n",
        "### Possui duas coleções: a) `Series` e b) `DataFrame`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lue8YQUPCwRc",
        "colab_type": "text"
      },
      "source": [
        "# Importando a biblioteca PANDAS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ly8gDyQ_kZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OMWaQm0GrBA",
        "colab_type": "text"
      },
      "source": [
        "# `Series`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NoVgGnHC0-xT",
        "colab_type": "text"
      },
      "source": [
        "### Características:\n",
        "\n",
        "*   É uma **lista**.\n",
        "*   Diferentemente de vetores (*arrays*) e/ou listas convencionais, `Series` suporta customização dos índices."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "riQZfLMMG36g",
        "colab_type": "text"
      },
      "source": [
        "### Criação de `Series` com índices padrão (*default*)\n",
        "\n",
        "A criação é realizada através do comando `pd.Series()`.\n",
        "\n",
        "Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Q1JS1mXG_hg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s = pd.Series ([10, 20, 30, 40, 50])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMWQIZKh2GNA",
        "colab_type": "text"
      },
      "source": [
        "### Exibição de uma `Series`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93HHxQHI2Ni-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "08b85660-b332-4b31-d40e-9da8770ff0fc"
      },
      "source": [
        "print (s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    10\n",
            "1    20\n",
            "2    30\n",
            "3    40\n",
            "4    50\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "femefU2P2Y7Y",
        "colab_type": "text"
      },
      "source": [
        "Partes:\n",
        "\n",
        "*   Coluna à esquerda -> índice\n",
        "*   Coluna à direita -> valores\n",
        "*   Última linha -> tipo de dados (`dtype`)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mP28qtg3FdV",
        "colab_type": "text"
      },
      "source": [
        "### Acessar a um elemento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtuN22303Hzc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "df28caed-dce7-443f-c138-6f938d38ef6c"
      },
      "source": [
        "print (s[2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSKjQbOU25XD",
        "colab_type": "text"
      },
      "source": [
        "### Criação de uma `Series` com valores iguais"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-v1o21aM29OG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "5397e4d3-2ca0-41ea-f4e2-45227db536e9"
      },
      "source": [
        "pd.Series (9.7, range(4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    9.7\n",
              "1    9.7\n",
              "2    9.7\n",
              "3    9.7\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yaj5vTop3eUy",
        "colab_type": "text"
      },
      "source": [
        "### Estatísticas para uma `Series`\n",
        "\n",
        "Funções:\n",
        "\n",
        "*   `count`\n",
        "*   `mean`\n",
        "*   `min`\n",
        "*   `max`\n",
        "*   `std`\n",
        "\n",
        "Exemplos:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ba8obC77h2pt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "13ea1651-a590-4679-b1bb-8734cc253d6a"
      },
      "source": [
        "print (s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    10\n",
            "1    20\n",
            "2    30\n",
            "3    40\n",
            "4    50\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGzmZptF3y0-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7c7decd2-28b8-46c9-b618-5c8ce353a937"
      },
      "source": [
        "s.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjqubpN833LH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7a7e0614-046e-46f3-8624-ae5a4692488f"
      },
      "source": [
        "s.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iW2u_XTP36p8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8529c093-b090-4a68-e26f-4fb9a6b3d553"
      },
      "source": [
        "s.min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Tyj_jON39Id",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "389e79ae-5112-4b0b-f762-5922d5018ec3"
      },
      "source": [
        "s.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEXsANpI3_Mo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1254574f-7e17-404b-a258-55c68ae51525"
      },
      "source": [
        "s.std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.811388300841896"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IuguYWy4FoV",
        "colab_type": "text"
      },
      "source": [
        "As funções de estatística podem ser resumidas com `describe()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANoJsm0J4Lqe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "31b5bea8-321e-4ced-81d0-b1f71fb017d6"
      },
      "source": [
        "s.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     5.000000\n",
              "mean     30.000000\n",
              "std      15.811388\n",
              "min      10.000000\n",
              "25%      20.000000\n",
              "50%      30.000000\n",
              "75%      40.000000\n",
              "max      50.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOqArmW9HIO-",
        "colab_type": "text"
      },
      "source": [
        "### Criação de índices customizados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Se28dCUh5IwN",
        "colab_type": "text"
      },
      "source": [
        "*   `Series` não tem o nome de uma coluna.\n",
        "*   Porém, é possível nomear os índices (*index*)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "om1cC1uI5V1E",
        "colab_type": "text"
      },
      "source": [
        "Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgDeHD1rHT--",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s_custom = pd.Series ([10, 20, 30], index=['1a dezena', '2a dezena', '3a dezena'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCinkEV-5dhn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "d99b658a-a635-472d-e54c-884b90b57c7a"
      },
      "source": [
        "print (s_custom)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1a dezena    10\n",
            "2a dezena    20\n",
            "3a dezena    30\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfve-uDB50vO",
        "colab_type": "text"
      },
      "source": [
        "É possível acessar os elementos individualmente de várias formas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecMuzrPo5ru-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "38681775-b3e1-4f3a-e3ff-ddf02b598358"
      },
      "source": [
        "print (s_custom[2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFX5g0vT59gV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "15cd5437-2ffe-44cc-ca4b-0633719e6033"
      },
      "source": [
        "print (s_custom['3a dezena'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IoUtSKexHrcN",
        "colab_type": "text"
      },
      "source": [
        "### Como seria possível criar uma `Series` com duas colunas?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKyJPFUhHy2D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "a36102d2-bb01-48a6-f910-999f9fd85a5d"
      },
      "source": [
        "pd.Series ([10, 20, 30, 40], [15, 25, 35, 45])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15    10\n",
              "25    20\n",
              "35    30\n",
              "45    40\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34qIjj3AC1Qi",
        "colab_type": "text"
      },
      "source": [
        "# `DataFrame`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nTue1P9XDD1o",
        "colab_type": "text"
      },
      "source": [
        "### Características:\n",
        "\n",
        "\n",
        "*   Estrutura bidimensional.\n",
        "*   É uma **tabela**.\n",
        "*   Cada coluna de um `DataFrame` é uma `Series`.\n",
        "*   Cada coluna pode ser de um tipo diferente.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-YQYNrFDM4D",
        "colab_type": "text"
      },
      "source": [
        "### Criação um `DataFrame` a partir de um dicionário."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSwN1B9H7cMq",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsHhziHn7umN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic1 = {'ESTADO': ['Amazonas', 'Amapá', 'Acre'], 'SIGLA': ['AM', 'AP', 'AC'], 'CAPITAL': ['Manaus', 'Macapá', 'Rio Branco']} "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QZSJ5fVC-FV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "ca777081-ee04-48cd-9c8b-87773a4a8cce"
      },
      "source": [
        "pd.DataFrame(dic1)"
      ],
      "execution_count": null,
      "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>ESTADO</th>\n",
              "      <th>SIGLA</th>\n",
              "      <th>CAPITAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Amazonas</td>\n",
              "      <td>AM</td>\n",
              "      <td>Manaus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Amapá</td>\n",
              "      <td>AP</td>\n",
              "      <td>Macapá</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>Rio Branco</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     ESTADO SIGLA     CAPITAL\n",
              "0  Amazonas    AM      Manaus\n",
              "1     Amapá    AP      Macapá\n",
              "2      Acre    AC  Rio Branco"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfANyzVRDxYD",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JS8m2MYWEGPj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "cd0871cf-c152-4bd4-b293-271c636ab5cc"
      },
      "source": [
        "pd.DataFrame({'ESTADO': ['Amazonas', 'Amapá', 'Acre'], 'POPULACAO': ['4144597', '845731', '881935']})"
      ],
      "execution_count": null,
      "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>ESTADO</th>\n",
              "      <th>POPULACAO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Amazonas</td>\n",
              "      <td>4144597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Amapá</td>\n",
              "      <td>845731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acre</td>\n",
              "      <td>881935</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     ESTADO POPULACAO\n",
              "0  Amazonas   4144597\n",
              "1     Amapá    845731\n",
              "2      Acre    881935"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDP1WeylEssA",
        "colab_type": "text"
      },
      "source": [
        "### Construtor DataFrame\n",
        "\n",
        "```\n",
        "pd.DataFrame()\n",
        "```\n",
        "\n",
        "É a forma padrão de se construir um novo `DataFrame`.\n",
        "\n",
        "Sintaxe:\n",
        "\n",
        "\n",
        "*   É um dicionário.\n",
        "*   As chaves do dicionário são as colunas.\n",
        "*   Os valores são a lista de entrada.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IP2HQZ65FmFX",
        "colab_type": "text"
      },
      "source": [
        "### Características do construtor:\n",
        "\n",
        "*   O construtor do tipo `DataFrame` atribui valores sequenciais.\n",
        "*   Estes valores são conhecidos como índices (*index*).\n",
        "*   Porém, é possível personalizar (customizar) os valores dos índices.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "olvmiF8FGLLG",
        "colab_type": "text"
      },
      "source": [
        "### Customização do índice:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hbFQZ9yt9Que",
        "colab_type": "text"
      },
      "source": [
        "Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qe3-oIgGYkU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic2 = pd.DataFrame({'ESTADO': ['Amazonas', 'Amapá', 'Acre'], \n",
        "              'POPULACAO': ['4144597', '845731', '881935']},\n",
        "             index=['AM', 'AP', 'AC'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPxKq2rX9zvM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "490f1726-0a7d-40e4-d07f-8c179925932f"
      },
      "source": [
        "dic2"
      ],
      "execution_count": null,
      "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>ESTADO</th>\n",
              "      <th>POPULACAO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AM</th>\n",
              "      <td>Amazonas</td>\n",
              "      <td>4144597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AP</th>\n",
              "      <td>Amapá</td>\n",
              "      <td>845731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AC</th>\n",
              "      <td>Acre</td>\n",
              "      <td>881935</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ESTADO POPULACAO\n",
              "AM  Amazonas   4144597\n",
              "AP     Amapá    845731\n",
              "AC      Acre    881935"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dztmgyBs9lUH",
        "colab_type": "text"
      },
      "source": [
        "### Acessando elementos do `DataFrame`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2uOU2Qv-B7H",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-UJMMAg9sCk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "d1adeafa-c9d8-4d04-ac1c-2e277bad1a86"
      },
      "source": [
        "dic2['ESTADO']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AM    Amazonas\n",
              "AP       Amapá\n",
              "AC        Acre\n",
              "Name: ESTADO, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MlIqbK04-Dbk",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wQ29Sp_9_J4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "9079174c-bd07-4028-a0ed-0a959f46c7c6"
      },
      "source": [
        "dic2.ESTADO"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AM    Amazonas\n",
              "AP       Amapá\n",
              "AC        Acre\n",
              "Name: ESTADO, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zv9aSkbpITzy",
        "colab_type": "text"
      },
      "source": [
        "### Seria possível criar um DataFrame com quantidades de linhas diferentes por coluna?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sEyhBsmaIcyN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "bffdd983-87e6-446d-f649-d34363181c2f"
      },
      "source": [
        "pd.DataFrame({'ESTADO': ['Amazonas', 'Amapá', 'Acre'], 'POPULACAO': ['4144597', '881935']})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-52ff3e604577>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'ESTADO'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Amazonas'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Amapá'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Acre'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'POPULACAO'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'4144597'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'881935'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    433\u001b[0m             )\n\u001b[1;32m    434\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[0;34m(data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0marr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         ]\n\u001b[0;32m--> 254\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    363\u001b[0m             \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 365\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"arrays must all be same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: arrays must all be same length"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wh91QvTSWW9V",
        "colab_type": "text"
      },
      "source": [
        "# Leitura de arquivos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9QbKfiiKWbVm",
        "colab_type": "text"
      },
      "source": [
        "### Características gerais sobre a leitura de arquivos:\n",
        "\n",
        "\n",
        "\n",
        "*   Na prática, iremos trabalhar com dados já existentes.\n",
        "*   Existem vários formatos de arquivos.\n",
        "*   O formato CSV é um dos formatos mais comuns.\n",
        "*   Um arquivo CSV é uma tabela cujos valores estão separados por vírgulas (*Comma-Separated Values*)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PoOr6gbgXCuV",
        "colab_type": "text"
      },
      "source": [
        "### Leitura de arquivos CSV em Pandas como um `DataFrame`:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtTmYNVVkOFg",
        "colab_type": "text"
      },
      "source": [
        "Comando padrão:\n",
        "\n",
        "`pd.read_csv()`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2I0SYsNXh3x",
        "colab_type": "text"
      },
      "source": [
        "Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znr68k-pXj_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dados = pd.read_csv(\"http://tiagodemelo.info/datasets/dados-curso.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKCrNpVGXytj",
        "colab_type": "text"
      },
      "source": [
        "### Atributo `shape` \n",
        "\n",
        "Permite verificar o tamanho dos dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5GIn97oYES-",
        "colab_type": "text"
      },
      "source": [
        "Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgT_he5nYHc-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "03b7c10c-7f98-4013-c845-2f39fce35151"
      },
      "source": [
        "dados.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqXrD1ORYRsF",
        "colab_type": "text"
      },
      "source": [
        "### Comando `head`\n",
        "\n",
        "Podemos ter uma noção do conjunto de dados.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12zdtI44YfAc",
        "colab_type": "text"
      },
      "source": [
        "Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgqrIApwYhLL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "330534bc-c25f-434c-8335-e9a826dd7144"
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-23 00:21:14</td>\n",
              "      <td>Para voltar tudo ao normal, você precisa fazer...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio das Ostras</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-41.937900</td>\n",
              "      <td>-22.522600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-22 22:57:51</td>\n",
              "      <td>14.245 – O que é a hidroxicloroquina? https://...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.674739</td>\n",
              "      <td>-23.606067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-14 00:11:33</td>\n",
              "      <td>Quarta morte em Lar de Estarreja associada à C...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-25 20:45:43</td>\n",
              "      <td>COVID-19 |  Hospital municipal Tide Setúbal, n...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-15 10:34:39</td>\n",
              "      <td>#cenasdocotidiano #santos #distanciamentosocia...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Santos</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.293700</td>\n",
              "      <td>-23.975947</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  data  ...  longitude\n",
              "0  2020-05-23 00:21:14  ... -22.522600\n",
              "1  2020-03-22 22:57:51  ... -23.606067\n",
              "2  2020-04-14 00:11:33  ...  38.747518\n",
              "3  2020-05-25 20:45:43  ... -23.550000\n",
              "4  2020-04-15 10:34:39  ... -23.975947\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DuN1XzUZYj5",
        "colab_type": "text"
      },
      "source": [
        "### Manipulação de dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Qkqjt5eZfze",
        "colab_type": "text"
      },
      "source": [
        "Pandas possui uma série de recursos que permite manipular os dados no formato de `DataFrame`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eostPPisZzQf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "1ac6fbe5-4bc7-4540-e53c-6fd700360117"
      },
      "source": [
        "dados"
      ],
      "execution_count": null,
      "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",
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              "    .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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-23 00:21:14</td>\n",
              "      <td>Para voltar tudo ao normal, você precisa fazer...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio das Ostras</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-41.937900</td>\n",
              "      <td>-22.522600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-22 22:57:51</td>\n",
              "      <td>14.245 – O que é a hidroxicloroquina? https://...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.674739</td>\n",
              "      <td>-23.606067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-14 00:11:33</td>\n",
              "      <td>Quarta morte em Lar de Estarreja associada à C...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-25 20:45:43</td>\n",
              "      <td>COVID-19 |  Hospital municipal Tide Setúbal, n...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-15 10:34:39</td>\n",
              "      <td>#cenasdocotidiano #santos #distanciamentosocia...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Santos</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.293700</td>\n",
              "      <td>-23.975947</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>495</th>\n",
              "      <td>2020-04-07 16:14:13</td>\n",
              "      <td>O isolamento social também serve para mostrar ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-67.859657</td>\n",
              "      <td>-9.932246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>2020-03-26 18:02:28</td>\n",
              "      <td>Enquanto o mundo está atento ao covid 19 é o B...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-43.358100</td>\n",
              "      <td>-22.942800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2020-03-18 05:28:16</td>\n",
              "      <td>#covid_19 #stayhome @ Toronto, Ontario https:/...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Toronto</td>\n",
              "      <td>Canada</td>\n",
              "      <td>CA</td>\n",
              "      <td>-79.387200</td>\n",
              "      <td>43.648000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>2020-03-03 21:27:11</td>\n",
              "      <td>Um vira-lata sem Coronavirus #joeviralata #dog...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>2020-01-21 13:18:03</td>\n",
              "      <td>Portugal já está a preparar-se para responder ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ...  longitude\n",
              "0    2020-05-23 00:21:14  ... -22.522600\n",
              "1    2020-03-22 22:57:51  ... -23.606067\n",
              "2    2020-04-14 00:11:33  ...  38.747518\n",
              "3    2020-05-25 20:45:43  ... -23.550000\n",
              "4    2020-04-15 10:34:39  ... -23.975947\n",
              "..                   ...  ...        ...\n",
              "495  2020-04-07 16:14:13  ...  -9.932246\n",
              "496  2020-03-26 18:02:28  ... -22.942800\n",
              "497  2020-03-18 05:28:16  ...  43.648000\n",
              "498  2020-03-03 21:27:11  ... -23.550000\n",
              "499  2020-01-21 13:18:03  ...  38.747518\n",
              "\n",
              "[500 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jWjVuq2aGof",
        "colab_type": "text"
      },
      "source": [
        "### As propriedades de um objeto podem ser acessadas como um atributo:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6lgsZ4JBabrp",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fd9ZSQwBaeBL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "ac1eddc2-60fa-4107-a967-b21944ac9914"
      },
      "source": [
        "dados.lugar"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      Rio das Ostras\n",
              "1           Sao Paulo\n",
              "2              Lisbon\n",
              "3           Sao Paulo\n",
              "4              Santos\n",
              "            ...      \n",
              "495        Rio Branco\n",
              "496    Rio de Janeiro\n",
              "497           Toronto\n",
              "498         Sao Paulo\n",
              "499            Lisbon\n",
              "Name: lugar, Length: 500, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vMOKQYP7auBT",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBvkUBuaayTK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "ef4667ff-0a2d-439e-8094-56bfdb44a173"
      },
      "source": [
        "dados['lugar']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      Rio das Ostras\n",
              "1           Sao Paulo\n",
              "2              Lisbon\n",
              "3           Sao Paulo\n",
              "4              Santos\n",
              "            ...      \n",
              "495        Rio Branco\n",
              "496    Rio de Janeiro\n",
              "497           Toronto\n",
              "498         Sao Paulo\n",
              "499            Lisbon\n",
              "Name: lugar, Length: 500, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1E9aaBM_bXAV",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 3:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnTdfyIXbYnh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7d71e907-02c4-4aca-ff75-c8778d506f99"
      },
      "source": [
        "dados['lugar'][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Rio das Ostras'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pImKeUbnbpAC",
        "colab_type": "text"
      },
      "source": [
        "# Indexação em Pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hic5mwsweGLm",
        "colab_type": "text"
      },
      "source": [
        "### Seleção baseada em índices\n",
        "\n",
        "#### Seleção de dados baseada na posição numérica dos dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-NyJR05ffVM",
        "colab_type": "text"
      },
      "source": [
        "### Selecionar a primeira linha\n",
        "\n",
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQrRq8nUcY_s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "76892964-b5b3-485d-bf65-6d979296bb20"
      },
      "source": [
        "dados.iloc[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "data                                       2020-05-23 00:21:14\n",
              "texto        Para voltar tudo ao normal, você precisa fazer...\n",
              "retweet                                                      0\n",
              "idioma                                                      pt\n",
              "lugar                                           Rio das Ostras\n",
              "pais                                                    Brazil\n",
              "sigla                                                       BR\n",
              "latitude                                              -41.9379\n",
              "longitude                                             -22.5226\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIVASYROk_Tg",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-kPc_pBf_6-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "9dd753c2-c123-4d82-a08e-df4bc8c13478"
      },
      "source": [
        "dados.iloc[:,1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      Para voltar tudo ao normal, você precisa fazer...\n",
              "1      14.245 – O que é a hidroxicloroquina? https://...\n",
              "2      Quarta morte em Lar de Estarreja associada à C...\n",
              "3      COVID-19 |  Hospital municipal Tide Setúbal, n...\n",
              "4      #cenasdocotidiano #santos #distanciamentosocia...\n",
              "                             ...                        \n",
              "495    O isolamento social também serve para mostrar ...\n",
              "496    Enquanto o mundo está atento ao covid 19 é o B...\n",
              "497    #covid_19 #stayhome @ Toronto, Ontario https:/...\n",
              "498    Um vira-lata sem Coronavirus #joeviralata #dog...\n",
              "499    Portugal já está a preparar-se para responder ...\n",
              "Name: texto, Length: 500, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pgu50YnrlDcb",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 3:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ey2gsjFBgJB7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "919b1323-3989-4e28-9bb8-f9f7c0045b28"
      },
      "source": [
        "dados.iloc[-5:]"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>2020-04-07 16:14:13</td>\n",
              "      <td>O isolamento social também serve para mostrar ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-67.859657</td>\n",
              "      <td>-9.932246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>2020-03-26 18:02:28</td>\n",
              "      <td>Enquanto o mundo está atento ao covid 19 é o B...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-43.358100</td>\n",
              "      <td>-22.942800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2020-03-18 05:28:16</td>\n",
              "      <td>#covid_19 #stayhome @ Toronto, Ontario https:/...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Toronto</td>\n",
              "      <td>Canada</td>\n",
              "      <td>CA</td>\n",
              "      <td>-79.387200</td>\n",
              "      <td>43.648000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>2020-03-03 21:27:11</td>\n",
              "      <td>Um vira-lata sem Coronavirus #joeviralata #dog...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>2020-01-21 13:18:03</td>\n",
              "      <td>Portugal já está a preparar-se para responder ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ...  longitude\n",
              "495  2020-04-07 16:14:13  ...  -9.932246\n",
              "496  2020-03-26 18:02:28  ... -22.942800\n",
              "497  2020-03-18 05:28:16  ...  43.648000\n",
              "498  2020-03-03 21:27:11  ... -23.550000\n",
              "499  2020-01-21 13:18:03  ...  38.747518\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imx0mjKlgQsG",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 5:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohmxTrlTg17l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6b3a18e4-6ce7-4266-e897-6b3badad59dd"
      },
      "source": [
        "dados.loc[0, 'pais']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Brazil'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BMqcU4nog-kQ",
        "colab_type": "text"
      },
      "source": [
        "# Manipulação de índices\n",
        "\n",
        "### É possível fazer a indexação dados por um atributo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epTQLNOjjARa",
        "colab_type": "text"
      },
      "source": [
        "### Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_MijJSGiVps",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "c193390d-85ea-408a-8bb5-e62a57a13505"
      },
      "source": [
        "dados.set_index('pais')"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pais</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>Brazil</th>\n",
              "      <td>2020-05-23 00:21:14</td>\n",
              "      <td>Para voltar tudo ao normal, você precisa fazer...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio das Ostras</td>\n",
              "      <td>BR</td>\n",
              "      <td>-41.937900</td>\n",
              "      <td>-22.522600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>2020-03-22 22:57:51</td>\n",
              "      <td>14.245 – O que é a hidroxicloroquina? https://...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.674739</td>\n",
              "      <td>-23.606067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portugal</th>\n",
              "      <td>2020-04-14 00:11:33</td>\n",
              "      <td>Quarta morte em Lar de Estarreja associada à C...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>2020-05-25 20:45:43</td>\n",
              "      <td>COVID-19 |  Hospital municipal Tide Setúbal, n...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>2020-04-15 10:34:39</td>\n",
              "      <td>#cenasdocotidiano #santos #distanciamentosocia...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Santos</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.293700</td>\n",
              "      <td>-23.975947</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>2020-04-07 16:14:13</td>\n",
              "      <td>O isolamento social também serve para mostrar ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>BR</td>\n",
              "      <td>-67.859657</td>\n",
              "      <td>-9.932246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>2020-03-26 18:02:28</td>\n",
              "      <td>Enquanto o mundo está atento ao covid 19 é o B...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>BR</td>\n",
              "      <td>-43.358100</td>\n",
              "      <td>-22.942800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>2020-03-18 05:28:16</td>\n",
              "      <td>#covid_19 #stayhome @ Toronto, Ontario https:/...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Toronto</td>\n",
              "      <td>CA</td>\n",
              "      <td>-79.387200</td>\n",
              "      <td>43.648000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>2020-03-03 21:27:11</td>\n",
              "      <td>Um vira-lata sem Coronavirus #joeviralata #dog...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portugal</th>\n",
              "      <td>2020-01-21 13:18:03</td>\n",
              "      <td>Portugal já está a preparar-se para responder ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                         data  ...  longitude\n",
              "pais                           ...           \n",
              "Brazil    2020-05-23 00:21:14  ... -22.522600\n",
              "Brazil    2020-03-22 22:57:51  ... -23.606067\n",
              "Portugal  2020-04-14 00:11:33  ...  38.747518\n",
              "Brazil    2020-05-25 20:45:43  ... -23.550000\n",
              "Brazil    2020-04-15 10:34:39  ... -23.975947\n",
              "...                       ...  ...        ...\n",
              "Brazil    2020-04-07 16:14:13  ...  -9.932246\n",
              "Brazil    2020-03-26 18:02:28  ... -22.942800\n",
              "Canada    2020-03-18 05:28:16  ...  43.648000\n",
              "Brazil    2020-03-03 21:27:11  ... -23.550000\n",
              "Portugal  2020-01-21 13:18:03  ...  38.747518\n",
              "\n",
              "[500 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IA9RsWOZjaLj",
        "colab_type": "text"
      },
      "source": [
        "# Seleção condicional"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DNimsIsBjcZK",
        "colab_type": "text"
      },
      "source": [
        "É possível consultar os dados de acordo com algum critério (condição)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mf7_pS-Ejm3A",
        "colab_type": "text"
      },
      "source": [
        "Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9vh_G23jb5I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "017df9a4-3984-4f31-ed8c-289977a8af41"
      },
      "source": [
        "dados.sigla == 'BR'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       True\n",
              "1       True\n",
              "2      False\n",
              "3       True\n",
              "4       True\n",
              "       ...  \n",
              "495     True\n",
              "496     True\n",
              "497    False\n",
              "498     True\n",
              "499    False\n",
              "Name: sigla, Length: 500, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TH5hYOW-kOJ8",
        "colab_type": "text"
      },
      "source": [
        "### Essa operação pode ser usado em combinação `loc`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MV5bTVpHkY80",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "1af13f15-1ea4-4d5d-f51d-ceb03bd7e29a"
      },
      "source": [
        "dados.loc[dados.sigla == 'BR']"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-23 00:21:14</td>\n",
              "      <td>Para voltar tudo ao normal, você precisa fazer...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio das Ostras</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-41.937900</td>\n",
              "      <td>-22.522600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-22 22:57:51</td>\n",
              "      <td>14.245 – O que é a hidroxicloroquina? https://...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.674739</td>\n",
              "      <td>-23.606067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-25 20:45:43</td>\n",
              "      <td>COVID-19 |  Hospital municipal Tide Setúbal, n...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-15 10:34:39</td>\n",
              "      <td>#cenasdocotidiano #santos #distanciamentosocia...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Santos</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.293700</td>\n",
              "      <td>-23.975947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-03-22 19:12:37</td>\n",
              "      <td>Memórias do cárcere. Hoje deu até pra treinar ...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-43.273900</td>\n",
              "      <td>-22.961900</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>493</th>\n",
              "      <td>2020-04-08 23:40:59</td>\n",
              "      <td>A Lei 13.987/20 garante a distribuição dos ali...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Medianeira</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-54.104104</td>\n",
              "      <td>-25.297434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>2020-04-14 12:06:38</td>\n",
              "      <td>A 2ª Vara de Família e Sucessões de Jacareí, e...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.638670</td>\n",
              "      <td>-23.540780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>2020-04-07 16:14:13</td>\n",
              "      <td>O isolamento social também serve para mostrar ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-67.859657</td>\n",
              "      <td>-9.932246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>2020-03-26 18:02:28</td>\n",
              "      <td>Enquanto o mundo está atento ao covid 19 é o B...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-43.358100</td>\n",
              "      <td>-22.942800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>2020-03-03 21:27:11</td>\n",
              "      <td>Um vira-lata sem Coronavirus #joeviralata #dog...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>392 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ...  longitude\n",
              "0    2020-05-23 00:21:14  ... -22.522600\n",
              "1    2020-03-22 22:57:51  ... -23.606067\n",
              "3    2020-05-25 20:45:43  ... -23.550000\n",
              "4    2020-04-15 10:34:39  ... -23.975947\n",
              "7    2020-03-22 19:12:37  ... -22.961900\n",
              "..                   ...  ...        ...\n",
              "493  2020-04-08 23:40:59  ... -25.297434\n",
              "494  2020-04-14 12:06:38  ... -23.540780\n",
              "495  2020-04-07 16:14:13  ...  -9.932246\n",
              "496  2020-03-26 18:02:28  ... -22.942800\n",
              "498  2020-03-03 21:27:11  ... -23.550000\n",
              "\n",
              "[392 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ji2GuZoGf1cm",
        "colab_type": "text"
      },
      "source": [
        "# Funções de Sumário (*Summary*)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaPpix8wlp3-",
        "colab_type": "text"
      },
      "source": [
        "### `describe()` é a função que apresenta um conjunto de estatísticas sobre um determinado campo (coluna)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0IwMTUqDl0E6",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4Ts99jHgOnw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "9e86379b-4705-4866-a8ba-2e1603cb463d"
      },
      "source": [
        "dados.retweet.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    500.000000\n",
              "mean       0.172000\n",
              "std        0.508855\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%        0.000000\n",
              "75%        0.000000\n",
              "max        4.000000\n",
              "Name: retweet, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CY_rDCGhLHt",
        "colab_type": "text"
      },
      "source": [
        "### A função `describe()` ainda pode ser utilizada para campos não numéricos\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1_g9SRwl-Nt",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQS3j1NOhUPI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "239f5fb7-f79d-44ca-e4de-8a889230856b"
      },
      "source": [
        "dados.sigla.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     499\n",
              "unique     21\n",
              "top        BR\n",
              "freq      392\n",
              "Name: sigla, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzb29OA9l_-N",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kw9CurPOhkW1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "75564061-4788-48b0-b74a-12e6e9e3b4ca"
      },
      "source": [
        "dados.data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                     500\n",
              "unique                    500\n",
              "top       2020-03-18 16:40:26\n",
              "freq                        1\n",
              "Name: data, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YVKPBBJh6ja",
        "colab_type": "text"
      },
      "source": [
        "# É possível ainda usar uma função de estatística específica para um determinado campo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xl6aUlP5hlXF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2f732afd-32c3-451f-c2d3-052a617b132c"
      },
      "source": [
        "dados.retweet.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.172"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vpm0TGYXixXW",
        "colab_type": "text"
      },
      "source": [
        "### A função `unique()` apresenta os valores únicos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8nEf0sOi4kD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "742b21be-7d28-4e59-e750-652006036be0"
      },
      "source": [
        "dados.sigla.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['BR', 'PT', 'VA', 'CA', 'US', 'ID', 'ES', 'AU', 'MX', 'NL', 'CH',\n",
              "       'NZ', 'CW', 'IT', 'IN', 'DO', 'EC', 'CN', nan, 'FR', 'MZ', 'GB'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5MtjoklmFH-",
        "colab_type": "text"
      },
      "source": [
        "Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_WjSZ22jNnR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "85949183-30ad-4750-e8ea-6333a9c51232"
      },
      "source": [
        "dados.sigla.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BR    392\n",
              "PT     67\n",
              "US     11\n",
              "CA      6\n",
              "ES      3\n",
              "AU      2\n",
              "NZ      2\n",
              "FR      2\n",
              "IT      2\n",
              "ID      1\n",
              "CW      1\n",
              "CN      1\n",
              "DO      1\n",
              "IN      1\n",
              "MZ      1\n",
              "EC      1\n",
              "GB      1\n",
              "CH      1\n",
              "MX      1\n",
              "NL      1\n",
              "VA      1\n",
              "Name: sigla, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uG15rtV5mHuC",
        "colab_type": "text"
      },
      "source": [
        "#Agrupamento de dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6bK-4cqmfjJ",
        "colab_type": "text"
      },
      "source": [
        "### Função `groupby()`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vS8o9GpgmJHV",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2AiETx1mmum",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e03a0a0-f75b-42d2-a4d0-5667d8e4602c"
      },
      "source": [
        "dados.groupby('sigla')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fe87f8482e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjyN85d2mMUy",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5djlcgynliS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "665493d0-1849-477e-c321-f35f0bea04b5"
      },
      "source": [
        "dados.groupby('sigla').sigla"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7fe87f029358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjL0yM8emOLG",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 3:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_uiLKFtm69H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "4933166f-3557-4676-f324-a2601ce40289"
      },
      "source": [
        "dados.groupby('sigla').sigla.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sigla\n",
              "AU      2\n",
              "BR    392\n",
              "CA      6\n",
              "CH      1\n",
              "CN      1\n",
              "CW      1\n",
              "DO      1\n",
              "EC      1\n",
              "ES      3\n",
              "FR      2\n",
              "GB      1\n",
              "ID      1\n",
              "IN      1\n",
              "IT      2\n",
              "MX      1\n",
              "MZ      1\n",
              "NL      1\n",
              "NZ      2\n",
              "PT     67\n",
              "US     11\n",
              "VA      1\n",
              "Name: sigla, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIeDhCHRn842",
        "colab_type": "text"
      },
      "source": [
        "### A função `groupby` pode ainda ser usada para exibir outro campo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mjmh4neLoDD9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "f56b9a96-0209-48a2-dc93-5f840c918f3a"
      },
      "source": [
        "dados.groupby('sigla').retweet.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sigla\n",
              "AU    1\n",
              "BR    3\n",
              "CA    2\n",
              "CH    0\n",
              "CN    0\n",
              "CW    0\n",
              "DO    0\n",
              "EC    0\n",
              "ES    0\n",
              "FR    0\n",
              "GB    1\n",
              "ID    0\n",
              "IN    0\n",
              "IT    0\n",
              "MX    0\n",
              "MZ    0\n",
              "NL    0\n",
              "NZ    0\n",
              "PT    4\n",
              "US    2\n",
              "VA    0\n",
              "Name: retweet, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5W7iDbFqBjo",
        "colab_type": "text"
      },
      "source": [
        "#Ordenação de dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vPtAgJqBqcDM",
        "colab_type": "text"
      },
      "source": [
        "### A função `sort_values()` pode ser usada para ordenar os valores por um determinado campo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5JU3U2AqRGF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "e9b973c2-1648-4ede-d8a7-f03a6118d147"
      },
      "source": [
        "dados.sort_values(by='sigla')"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>2020-03-19 20:45:14</td>\n",
              "      <td>As pesquisas não param! 💻 A equipe do Lab404 s...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Australia</td>\n",
              "      <td>AU</td>\n",
              "      <td>144.960154</td>\n",
              "      <td>-37.818769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>2020-02-21 10:34:40</td>\n",
              "      <td>Australia’s first dedicated e-sports bar @ GG ...</td>\n",
              "      <td>0</td>\n",
              "      <td>en</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Australia</td>\n",
              "      <td>AU</td>\n",
              "      <td>144.961030</td>\n",
              "      <td>-37.816360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-23 00:21:14</td>\n",
              "      <td>Para voltar tudo ao normal, você precisa fazer...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio das Ostras</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-41.937900</td>\n",
              "      <td>-22.522600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>2020-05-31 12:24:15</td>\n",
              "      <td>Sair de casa só naquele estilo! 😎😷😅 #mascara #...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio Claro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-47.550000</td>\n",
              "      <td>-22.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>2020-04-03 13:24:12</td>\n",
              "      <td>Informação é o melhor remédio contra o Coronav...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Londrina</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-51.150000</td>\n",
              "      <td>-23.300000</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>392</th>\n",
              "      <td>2020-05-22 15:37:07</td>\n",
              "      <td>No blog @voandobaixo tem um texto sobre o reto...</td>\n",
              "      <td>2</td>\n",
              "      <td>pt</td>\n",
              "      <td>Darlington</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-79.910371</td>\n",
              "      <td>34.294532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>2020-03-27 23:23:18</td>\n",
              "      <td>A cidade de Nova York parece um campo de guerr...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-74.006400</td>\n",
              "      <td>40.714200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>2020-03-22 17:41:58</td>\n",
              "      <td>#coronavirus #covid19 #covid_19 🦠 😷 #quarantin...</td>\n",
              "      <td>0</td>\n",
              "      <td>it</td>\n",
              "      <td>Montpelier</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-72.598119</td>\n",
              "      <td>44.152532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-03-27 22:59:12</td>\n",
              "      <td>O Papa Francisco rezando sozinho na imensa pra...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Vatican City</td>\n",
              "      <td>Vatican City</td>\n",
              "      <td>VA</td>\n",
              "      <td>12.457018</td>\n",
              "      <td>41.902473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>2020-03-19 12:30:16</td>\n",
              "      <td>Meu #tbt de isolamento social 🏕🌵 #namibia #afr...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Namibia</td>\n",
              "      <td>Namibia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>-23.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ...  longitude\n",
              "86   2020-03-19 20:45:14  ... -37.818769\n",
              "354  2020-02-21 10:34:40  ... -37.816360\n",
              "0    2020-05-23 00:21:14  ... -22.522600\n",
              "321  2020-05-31 12:24:15  ... -22.400000\n",
              "320  2020-04-03 13:24:12  ... -23.300000\n",
              "..                   ...  ...        ...\n",
              "392  2020-05-22 15:37:07  ...  34.294532\n",
              "123  2020-03-27 23:23:18  ...  40.714200\n",
              "203  2020-03-22 17:41:58  ...  44.152532\n",
              "5    2020-03-27 22:59:12  ...  41.902473\n",
              "399  2020-03-19 12:30:16  ... -23.000000\n",
              "\n",
              "[500 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iXAv3Llq2Vu",
        "colab_type": "text"
      },
      "source": [
        "### A ordenação padrão é de forma ascendente. Porém, é possível ordenar de maneira descendente"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLa5mqqMrF__",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "90e22e58-8b1f-43fb-e96d-1b18c39505b4"
      },
      "source": [
        "dados.sort_values(by='sigla', ascending=False)"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-03-27 22:59:12</td>\n",
              "      <td>O Papa Francisco rezando sozinho na imensa pra...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Vatican City</td>\n",
              "      <td>Vatican City</td>\n",
              "      <td>VA</td>\n",
              "      <td>12.457018</td>\n",
              "      <td>41.902473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>2020-03-22 23:18:53</td>\n",
              "      <td>#protectallworkers #workersfirst #stayathome #...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-87.632496</td>\n",
              "      <td>41.883222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2020-05-22 21:44:49</td>\n",
              "      <td>A cidade de Nova York, nos Estados Unidos, vai...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-118.244500</td>\n",
              "      <td>34.056400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2020-03-03 00:43:28</td>\n",
              "      <td>A Intelprise alerta os folioes pos-carnavalesc...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>North Miami Beach</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-80.163740</td>\n",
              "      <td>25.946560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2020-03-19 20:58:11</td>\n",
              "      <td>🥴🤣😭☠️ #corona #covid_19 #socialdistancing #nof...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Colorado Springs</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-104.820700</td>\n",
              "      <td>38.830100</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>186</th>\n",
              "      <td>2020-05-19 19:09:57</td>\n",
              "      <td>Nesse tempo de isolamento social,não se isole ...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-43.373770</td>\n",
              "      <td>-22.931997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2020-04-06 18:41:09</td>\n",
              "      <td>Só queria estar despreocupado com meu amigo En...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Foz do Iguaçu</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-54.586814</td>\n",
              "      <td>-25.413745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>2020-02-21 10:34:40</td>\n",
              "      <td>Australia’s first dedicated e-sports bar @ GG ...</td>\n",
              "      <td>0</td>\n",
              "      <td>en</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Australia</td>\n",
              "      <td>AU</td>\n",
              "      <td>144.961030</td>\n",
              "      <td>-37.816360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>2020-03-19 20:45:14</td>\n",
              "      <td>As pesquisas não param! 💻 A equipe do Lab404 s...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Australia</td>\n",
              "      <td>AU</td>\n",
              "      <td>144.960154</td>\n",
              "      <td>-37.818769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>2020-03-19 12:30:16</td>\n",
              "      <td>Meu #tbt de isolamento social 🏕🌵 #namibia #afr...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Namibia</td>\n",
              "      <td>Namibia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>-23.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ...  longitude\n",
              "5    2020-03-27 22:59:12  ...  41.902473\n",
              "391  2020-03-22 23:18:53  ...  41.883222\n",
              "75   2020-05-22 21:44:49  ...  34.056400\n",
              "101  2020-03-03 00:43:28  ...  25.946560\n",
              "72   2020-03-19 20:58:11  ...  38.830100\n",
              "..                   ...  ...        ...\n",
              "186  2020-05-19 19:09:57  ... -22.931997\n",
              "249  2020-04-06 18:41:09  ... -25.413745\n",
              "354  2020-02-21 10:34:40  ... -37.816360\n",
              "86   2020-03-19 20:45:14  ... -37.818769\n",
              "399  2020-03-19 12:30:16  ... -23.000000\n",
              "\n",
              "[500 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2aXoZKOrbhz",
        "colab_type": "text"
      },
      "source": [
        "### A ordenação por índices é feita através da função `sort_index()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "404_fU4hrlz9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "602ddcf6-765c-4525-b730-ad509b3ae09d"
      },
      "source": [
        "dados.sort_index()"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-23 00:21:14</td>\n",
              "      <td>Para voltar tudo ao normal, você precisa fazer...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio das Ostras</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-41.937900</td>\n",
              "      <td>-22.522600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-22 22:57:51</td>\n",
              "      <td>14.245 – O que é a hidroxicloroquina? https://...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.674739</td>\n",
              "      <td>-23.606067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-14 00:11:33</td>\n",
              "      <td>Quarta morte em Lar de Estarreja associada à C...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-25 20:45:43</td>\n",
              "      <td>COVID-19 |  Hospital municipal Tide Setúbal, n...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-15 10:34:39</td>\n",
              "      <td>#cenasdocotidiano #santos #distanciamentosocia...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Santos</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.293700</td>\n",
              "      <td>-23.975947</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>495</th>\n",
              "      <td>2020-04-07 16:14:13</td>\n",
              "      <td>O isolamento social também serve para mostrar ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-67.859657</td>\n",
              "      <td>-9.932246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>2020-03-26 18:02:28</td>\n",
              "      <td>Enquanto o mundo está atento ao covid 19 é o B...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-43.358100</td>\n",
              "      <td>-22.942800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2020-03-18 05:28:16</td>\n",
              "      <td>#covid_19 #stayhome @ Toronto, Ontario https:/...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Toronto</td>\n",
              "      <td>Canada</td>\n",
              "      <td>CA</td>\n",
              "      <td>-79.387200</td>\n",
              "      <td>43.648000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>2020-03-03 21:27:11</td>\n",
              "      <td>Um vira-lata sem Coronavirus #joeviralata #dog...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>2020-01-21 13:18:03</td>\n",
              "      <td>Portugal já está a preparar-se para responder ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ...  longitude\n",
              "0    2020-05-23 00:21:14  ... -22.522600\n",
              "1    2020-03-22 22:57:51  ... -23.606067\n",
              "2    2020-04-14 00:11:33  ...  38.747518\n",
              "3    2020-05-25 20:45:43  ... -23.550000\n",
              "4    2020-04-15 10:34:39  ... -23.975947\n",
              "..                   ...  ...        ...\n",
              "495  2020-04-07 16:14:13  ...  -9.932246\n",
              "496  2020-03-26 18:02:28  ... -22.942800\n",
              "497  2020-03-18 05:28:16  ...  43.648000\n",
              "498  2020-03-03 21:27:11  ... -23.550000\n",
              "499  2020-01-21 13:18:03  ...  38.747518\n",
              "\n",
              "[500 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYPDhhmjsC__",
        "colab_type": "text"
      },
      "source": [
        "### A ordenação ainda pode ser feita por mais de um campo ao mesmo tempo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0B-r00UesLpL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "25bab3ed-dbeb-4f8b-b8aa-612f586354cc"
      },
      "source": [
        "dados.sort_values(by=['pais', 'lugar'])"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>2020-03-19 20:45:14</td>\n",
              "      <td>As pesquisas não param! 💻 A equipe do Lab404 s...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Australia</td>\n",
              "      <td>AU</td>\n",
              "      <td>144.960154</td>\n",
              "      <td>-37.818769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>2020-02-21 10:34:40</td>\n",
              "      <td>Australia’s first dedicated e-sports bar @ GG ...</td>\n",
              "      <td>0</td>\n",
              "      <td>en</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Australia</td>\n",
              "      <td>AU</td>\n",
              "      <td>144.961030</td>\n",
              "      <td>-37.816360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>2020-04-09 21:28:32</td>\n",
              "      <td>FAÇA O SEU CONSÓRCIO, FINANCIAMENTO OU O SEU S...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Abre Campo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-42.476260</td>\n",
              "      <td>-20.298590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>2020-04-14 12:54:32</td>\n",
              "      <td>#covid #angradosreis #covid_19 #coronavirusbra...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Angra dos Reis</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-44.305500</td>\n",
              "      <td>-22.997500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>2020-05-31 16:51:03</td>\n",
              "      <td>Existe vida mais barata , mas também existe vi...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Angra dos Reis</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-44.338708</td>\n",
              "      <td>-22.940978</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>55</th>\n",
              "      <td>2020-03-18 03:22:12</td>\n",
              "      <td>Distânciamento social pode salvar os avos, os ...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Miami</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-80.192000</td>\n",
              "      <td>25.775200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>2020-03-22 17:41:58</td>\n",
              "      <td>#coronavirus #covid19 #covid_19 🦠 😷 #quarantin...</td>\n",
              "      <td>0</td>\n",
              "      <td>it</td>\n",
              "      <td>Montpelier</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-72.598119</td>\n",
              "      <td>44.152532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2020-03-03 00:43:28</td>\n",
              "      <td>A Intelprise alerta os folioes pos-carnavalesc...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>North Miami Beach</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-80.163740</td>\n",
              "      <td>25.946560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>2020-03-27 02:07:47</td>\n",
              "      <td>Nos vemos uma ou duas vezes por ano, quando dá...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Scott Lake</td>\n",
              "      <td>United States</td>\n",
              "      <td>US</td>\n",
              "      <td>-80.237185</td>\n",
              "      <td>25.957486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-03-27 22:59:12</td>\n",
              "      <td>O Papa Francisco rezando sozinho na imensa pra...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Vatican City</td>\n",
              "      <td>Vatican City</td>\n",
              "      <td>VA</td>\n",
              "      <td>12.457018</td>\n",
              "      <td>41.902473</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ...  longitude\n",
              "86   2020-03-19 20:45:14  ... -37.818769\n",
              "354  2020-02-21 10:34:40  ... -37.816360\n",
              "153  2020-04-09 21:28:32  ... -20.298590\n",
              "353  2020-04-14 12:54:32  ... -22.997500\n",
              "390  2020-05-31 16:51:03  ... -22.940978\n",
              "..                   ...  ...        ...\n",
              "55   2020-03-18 03:22:12  ...  25.775200\n",
              "203  2020-03-22 17:41:58  ...  44.152532\n",
              "101  2020-03-03 00:43:28  ...  25.946560\n",
              "71   2020-03-27 02:07:47  ...  25.957486\n",
              "5    2020-03-27 22:59:12  ...  41.902473\n",
              "\n",
              "[500 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FuS22THQtT1Q",
        "colab_type": "text"
      },
      "source": [
        "# Tipo de dados em `DataFrame` e `Series`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfQky6ajtmpo",
        "colab_type": "text"
      },
      "source": [
        "### Para verificar o tipo de um dado em Python nós usamos o comando `type`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIn8rb3mty_B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6d58111a-f249-4f7c-a980-13ef8d85d665"
      },
      "source": [
        "type(dados)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vh9owK-1uBoT",
        "colab_type": "text"
      },
      "source": [
        "### Para verificarmos o tipo de dados de uma coluna de um `DataFrame` ou `Series` em Python nós usamos o comando `dtype`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Vuyc5oVuDKQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "651aa6b3-71bc-4f07-e4e7-43502510d972"
      },
      "source": [
        "dados.retweet.dtype"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rV66uwznuT-5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7d82d73b-b3aa-4ab7-fa3e-aa6ac8564f0b"
      },
      "source": [
        "dados.idioma.dtype"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BT0LCmhRuwpm",
        "colab_type": "text"
      },
      "source": [
        "### Nós podemos ainda verificar o tipo de dados de todas as colunas de um `DataFrame`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7mEffTjue_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "1665cbf7-a50a-4e6a-c530-4f904ca997b2"
      },
      "source": [
        "dados.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "data          object\n",
              "texto         object\n",
              "retweet        int64\n",
              "idioma        object\n",
              "lugar         object\n",
              "pais          object\n",
              "sigla         object\n",
              "latitude     float64\n",
              "longitude    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0g2uespwF-c",
        "colab_type": "text"
      },
      "source": [
        "### É possível ainda converter o tipo de uma determinada coluna para outro tipo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3WfnAWRNnOdW",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1gT9hKswGLe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "b8653011-fce3-499f-feb4-2099e78fcd16"
      },
      "source": [
        "dados.retweet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      1\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "495    1\n",
              "496    0\n",
              "497    0\n",
              "498    0\n",
              "499    1\n",
              "Name: retweet, Length: 500, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PN-AlkHynQJ9",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXjvwtsRwKhD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "af3c9119-6b5c-4794-c5c0-c2de72fb7b26"
      },
      "source": [
        "dados.retweet.astype('float64')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0.0\n",
              "1      0.0\n",
              "2      1.0\n",
              "3      0.0\n",
              "4      0.0\n",
              "      ... \n",
              "495    1.0\n",
              "496    0.0\n",
              "497    0.0\n",
              "498    0.0\n",
              "499    1.0\n",
              "Name: retweet, Length: 500, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvCgVBURw7e3",
        "colab_type": "text"
      },
      "source": [
        "### Seria possível converter uma coluna para qualquer outro tipo de dados?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avknbHNawkuI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "574287a4-f0e3-46e8-9d2c-95779302f920"
      },
      "source": [
        "dados.sigla.astype('float64')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-73-ec41f8a3569e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdados\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigla\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float64'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5696\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5697\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5698\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5699\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, filter, **kwargs)\u001b[0m\n\u001b[1;32m    440\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    443\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    623\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    895\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m         \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 897\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    898\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'BR'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ap3tFHSmns1P",
        "colab_type": "text"
      },
      "source": [
        "### Resumo dos tipos em Pandas:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aq1KxlLTon0a",
        "colab_type": "text"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1l2SH97T_0Lu7VIInFzkc31HUbSep37g6)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZfqRb_NxXdG",
        "colab_type": "text"
      },
      "source": [
        "### Lidando com dados ausentes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qwuogd6Rx8Hh",
        "colab_type": "text"
      },
      "source": [
        "Regras gerais:\n",
        "\n",
        "*   Os dados ausentes são presentados pelo valor `NaN` que representa Not a Number.\n",
        "*   Por razões técnicas, os valores `NaN` são sempre do tipo `float64`.\n",
        "*   Pandas possui alguns métodos específicos para lidar com dados ausentes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KC5yERWwzOdP",
        "colab_type": "text"
      },
      "source": [
        "### O comando `pd.isnull()` é utilizado para selecionar as entradas ausentes\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gf09OWktytIV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "41424552-c2ac-4561-b578-37f96b195309"
      },
      "source": [
        "dados[pd.isnull(dados.sigla)]"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>2020-03-19 12:30:16</td>\n",
              "      <td>Meu #tbt de isolamento social 🏕🌵 #namibia #afr...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Namibia</td>\n",
              "      <td>Namibia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>-23.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ... longitude\n",
              "399  2020-03-19 12:30:16  ...     -23.0\n",
              "\n",
              "[1 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbxpZzCLz31J",
        "colab_type": "text"
      },
      "source": [
        "### Substituição de valores ausentes\n",
        "\n",
        "### A função `fillna()` é usada para substituir valores ausentes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRMHftBc0LH6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "8b41004b-23b6-4058-cc0a-ff076a8c55a7"
      },
      "source": [
        "dados.sigla.fillna(\"PADRAO\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      BR\n",
              "1      BR\n",
              "2      PT\n",
              "3      BR\n",
              "4      BR\n",
              "       ..\n",
              "495    BR\n",
              "496    BR\n",
              "497    CA\n",
              "498    BR\n",
              "499    PT\n",
              "Name: sigla, Length: 500, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcUA4oPq1SkX",
        "colab_type": "text"
      },
      "source": [
        "### É possível ainda fazer a substituição de um valor específico por outro através do comando `replace`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWwGDI441C-k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "5130b0e1-6c7e-4bd9-f267-46746f3ccf07"
      },
      "source": [
        "dados.sigla.replace('BR', 'BZ')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      BZ\n",
              "1      BZ\n",
              "2      PT\n",
              "3      BZ\n",
              "4      BZ\n",
              "       ..\n",
              "495    BZ\n",
              "496    BZ\n",
              "497    CA\n",
              "498    BZ\n",
              "499    PT\n",
              "Name: sigla, Length: 500, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMUR6bjg2ErV",
        "colab_type": "text"
      },
      "source": [
        "# Formatação dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUFwPfJo2x5G",
        "colab_type": "text"
      },
      "source": [
        "### Regras gerais:\n",
        "\n",
        "*   É comum ocorrer a situação onde os dados não estão com nomes apropriados.\n",
        "*   Neste caso, podemos usar um conjunto de funções de Pandas para formatar `DataFrame` ou `Series`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vkj26tyW3Xzv",
        "colab_type": "text"
      },
      "source": [
        "### É possível renomear as colunas e índices através do comando `rename()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nosjwRUq3aGF",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "05d2aafe-c203-4c1b-ddc6-cdba34af1230"
      },
      "source": [
        "#@title\n",
        "dados.rename(columns={'sigla': 'codigo'})"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>codigo</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-23 00:21:14</td>\n",
              "      <td>Para voltar tudo ao normal, você precisa fazer...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio das Ostras</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-41.937900</td>\n",
              "      <td>-22.522600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-22 22:57:51</td>\n",
              "      <td>14.245 – O que é a hidroxicloroquina? https://...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.674739</td>\n",
              "      <td>-23.606067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-14 00:11:33</td>\n",
              "      <td>Quarta morte em Lar de Estarreja associada à C...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-25 20:45:43</td>\n",
              "      <td>COVID-19 |  Hospital municipal Tide Setúbal, n...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-15 10:34:39</td>\n",
              "      <td>#cenasdocotidiano #santos #distanciamentosocia...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Santos</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.293700</td>\n",
              "      <td>-23.975947</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>495</th>\n",
              "      <td>2020-04-07 16:14:13</td>\n",
              "      <td>O isolamento social também serve para mostrar ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-67.859657</td>\n",
              "      <td>-9.932246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>2020-03-26 18:02:28</td>\n",
              "      <td>Enquanto o mundo está atento ao covid 19 é o B...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-43.358100</td>\n",
              "      <td>-22.942800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2020-03-18 05:28:16</td>\n",
              "      <td>#covid_19 #stayhome @ Toronto, Ontario https:/...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Toronto</td>\n",
              "      <td>Canada</td>\n",
              "      <td>CA</td>\n",
              "      <td>-79.387200</td>\n",
              "      <td>43.648000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>2020-03-03 21:27:11</td>\n",
              "      <td>Um vira-lata sem Coronavirus #joeviralata #dog...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>2020-01-21 13:18:03</td>\n",
              "      <td>Portugal já está a preparar-se para responder ...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    data  ...  longitude\n",
              "0    2020-05-23 00:21:14  ... -22.522600\n",
              "1    2020-03-22 22:57:51  ... -23.606067\n",
              "2    2020-04-14 00:11:33  ...  38.747518\n",
              "3    2020-05-25 20:45:43  ... -23.550000\n",
              "4    2020-04-15 10:34:39  ... -23.975947\n",
              "..                   ...  ...        ...\n",
              "495  2020-04-07 16:14:13  ...  -9.932246\n",
              "496  2020-03-26 18:02:28  ... -22.942800\n",
              "497  2020-03-18 05:28:16  ...  43.648000\n",
              "498  2020-03-03 21:27:11  ... -23.550000\n",
              "499  2020-01-21 13:18:03  ...  38.747518\n",
              "\n",
              "[500 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OeQKZ3JcMcY2",
        "colab_type": "text"
      },
      "source": [
        "# Plotar Mapas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QoY16iOFN9om",
        "colab_type": "text"
      },
      "source": [
        "### Projeto GeoPandas\n",
        "\n",
        "*   Projeto código aberto para lidar com dados espaciais em Python.\n",
        "*   Versão atual é a 0.8.0.\n",
        "*   Permite a realização de operações espaciais."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbSLG4heMupm",
        "colab_type": "text"
      },
      "source": [
        "### Instalar a biblioteca"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQ-_i9JNMil9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "outputId": "69f21c1f-55c8-4f16-ba6f-45ef50eae548"
      },
      "source": [
        "!pip install geopandas"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting geopandas\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f7/a4/e66aafbefcbb717813bf3a355c8c4fc3ed04ea1dd7feb2920f2f4f868921/geopandas-0.8.1-py2.py3-none-any.whl (962kB)\n",
            "\u001b[K     |████████████████████████████████| 972kB 1.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.7.0)\n",
            "Requirement already satisfied: pandas>=0.23.0 in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.0.5)\n",
            "Collecting pyproj>=2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/c3/071e080230ac4b6c64f1a2e2f9161c9737a2bc7b683d2c90b024825000c0/pyproj-2.6.1.post1-cp36-cp36m-manylinux2010_x86_64.whl (10.9MB)\n",
            "\u001b[K     |████████████████████████████████| 10.9MB 362kB/s \n",
            "\u001b[?25hCollecting fiona\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ec/20/4e63bc5c6e62df889297b382c3ccd4a7a488b00946aaaf81a118158c6f09/Fiona-1.8.13.post1-cp36-cp36m-manylinux1_x86_64.whl (14.7MB)\n",
            "\u001b[K     |████████████████████████████████| 14.7MB 316kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (1.18.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (2.8.1)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (19.3.0)\n",
            "Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (7.1.2)\n",
            "Collecting munch\n",
            "  Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
            "Collecting cligj>=0.5\n",
            "  Downloading https://files.pythonhosted.org/packages/e4/be/30a58b4b0733850280d01f8bd132591b4668ed5c7046761098d665ac2174/cligj-0.5.0-py3-none-any.whl\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e9/da/824b92d9942f4e472702488857914bdd50f73021efea15b4cad9aca8ecef/click_plugins-1.1.1-py2.py3-none-any.whl\n",
            "Installing collected packages: pyproj, munch, cligj, click-plugins, fiona, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.5.0 fiona-1.8.13.post1 geopandas-0.8.1 munch-2.5.0 pyproj-2.6.1.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FmS4QcZYNPfx",
        "colab_type": "text"
      },
      "source": [
        "### Importar as bibliotecas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3xWCWlyNSam",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from shapely.geometry import Point\n",
        "import geopandas as gpd\n",
        "from geopandas import GeoDataFrame"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "daNVJpEJMxat",
        "colab_type": "text"
      },
      "source": [
        "### Carregar o arquivo com os dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhydj7j7M038",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dados = pd.read_csv(\"http://tiagodemelo.info/datasets/dados-curso.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TZO6kZOM7HB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "8fd3817d-d5a2-4f64-ddbd-2d50c17003f6"
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": null,
      "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>data</th>\n",
              "      <th>texto</th>\n",
              "      <th>retweet</th>\n",
              "      <th>idioma</th>\n",
              "      <th>lugar</th>\n",
              "      <th>pais</th>\n",
              "      <th>sigla</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-23 00:21:14</td>\n",
              "      <td>Para voltar tudo ao normal, você precisa fazer...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Rio das Ostras</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-41.937900</td>\n",
              "      <td>-22.522600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-22 22:57:51</td>\n",
              "      <td>14.245 – O que é a hidroxicloroquina? https://...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.674739</td>\n",
              "      <td>-23.606067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-14 00:11:33</td>\n",
              "      <td>Quarta morte em Lar de Estarreja associada à C...</td>\n",
              "      <td>1</td>\n",
              "      <td>pt</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>PT</td>\n",
              "      <td>-9.099043</td>\n",
              "      <td>38.747518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-25 20:45:43</td>\n",
              "      <td>COVID-19 |  Hospital municipal Tide Setúbal, n...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.633300</td>\n",
              "      <td>-23.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-15 10:34:39</td>\n",
              "      <td>#cenasdocotidiano #santos #distanciamentosocia...</td>\n",
              "      <td>0</td>\n",
              "      <td>pt</td>\n",
              "      <td>Santos</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BR</td>\n",
              "      <td>-46.293700</td>\n",
              "      <td>-23.975947</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  data  ...  longitude\n",
              "0  2020-05-23 00:21:14  ... -22.522600\n",
              "1  2020-03-22 22:57:51  ... -23.606067\n",
              "2  2020-04-14 00:11:33  ...  38.747518\n",
              "3  2020-05-25 20:45:43  ... -23.550000\n",
              "4  2020-04-15 10:34:39  ... -23.975947\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pP7RtLcwNCGr",
        "colab_type": "text"
      },
      "source": [
        "### Plotar a posição dos usuários no mapa"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URZXqSpRNFwd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "f2bbaa0f-2b2e-4904-9831-9110d4c84ed6"
      },
      "source": [
        "geometry = [Point(xy) for xy in zip(dados['latitude'], dados['longitude'])]\n",
        "gdf = GeoDataFrame(dados, geometry=geometry)   \n",
        "\n",
        "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
        "gdf.plot(ax=world.plot(figsize=(10, 6)), marker='o', color='red', markersize=15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-Gu92rtEJ1K",
        "colab_type": "text"
      },
      "source": [
        "#Visualização dos Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vqG0-TAERyD",
        "colab_type": "text"
      },
      "source": [
        "##MatPlotLib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxAtiyI1FBUf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame({\n",
        "    'nome':['João','Maria','Pedro','José','Lucas','Luíza','Francisco'],\n",
        "    'idade':[23,78,22,19,45,33,20],\n",
        "    'genero':['M','F','M','M','M','F','M'],\n",
        "    'estado':['Amazonas','Pará','Amazonas','Pará','Amazonas','Ceará','Ceará'],\n",
        "    'num_filhos':[2,0,0,3,2,1,4],\n",
        "    'num_animais':[5,1,0,5,2,2,3]\n",
        "})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmDcBLtSsQBx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2dab9d43-93f3-4f4c-8931-1a9f9edfb539"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>nome</th>\n",
              "      <th>idade</th>\n",
              "      <th>genero</th>\n",
              "      <th>estado</th>\n",
              "      <th>num_filhos</th>\n",
              "      <th>num_animais</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>João</td>\n",
              "      <td>23</td>\n",
              "      <td>M</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Maria</td>\n",
              "      <td>78</td>\n",
              "      <td>F</td>\n",
              "      <td>Pará</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pedro</td>\n",
              "      <td>22</td>\n",
              "      <td>M</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>José</td>\n",
              "      <td>19</td>\n",
              "      <td>M</td>\n",
              "      <td>Pará</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Lucas</td>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    nome  idade genero    estado  num_filhos  num_animais\n",
              "0   João     23      M  Amazonas           2            5\n",
              "1  Maria     78      F      Pará           0            1\n",
              "2  Pedro     22      M  Amazonas           0            0\n",
              "3   José     19      M      Pará           3            5\n",
              "4  Lucas     45      M  Amazonas           2            2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1gp40EI8Mvdq",
        "colab_type": "text"
      },
      "source": [
        "### Scatter Plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2kZD0eJBI9Lv",
        "colab_type": "text"
      },
      "source": [
        "É o gráfico de dispersão."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7a-wGhUJBRL",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RL_SbOcXMxNA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "56fc9c61-18b9-4372-a10d-1955423546bf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "df.plot(kind='scatter', x='num_filhos',y='num_animais')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHiC1nOvH3Gl",
        "colab_type": "text"
      },
      "source": [
        "### Configurações diversas dos gráficos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vjUEKIbIKKI",
        "colab_type": "text"
      },
      "source": [
        "##### Cores\n",
        "\n",
        "\n",
        "*   O atributo `color` ou `c` é usado para definir as cores.\n",
        "*   As cores estão em inglês.\n",
        "*   [Lista completa de cores](https://matplotlib.org/3.1.0/gallery/color/named_colors.html)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yy4i0a-KJVOF",
        "colab_type": "text"
      },
      "source": [
        "##### Transparência\n",
        "\n",
        "\n",
        "*   O atributo `alpha` define a transparência dos objetos.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vg3JBD-K5PY",
        "colab_type": "text"
      },
      "source": [
        "##### Marcadores\n",
        "\n",
        "\n",
        "*   É possível alterar o marcador através do atributo `marker`.\n",
        "*   [Lista completa de marcadores](https://matplotlib.org/api/markers_api.html)\n",
        "*   É possível também alterar o tamanho do marcador através do atributo `s`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ul0rkJ8WJ0EK",
        "colab_type": "text"
      },
      "source": [
        "##### Rótulos (labels) dos eixos\n",
        "\n",
        "\n",
        "*   O comando `xlabel` é usado para renomear o eixo X do gráfico.\n",
        "*   O comando `ylabel` é usado para renomear o eixo Y do gráfico.\n",
        "*   Esses comandos permitem alterar o tamanho da fonte através do atributo `fontsize`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JjUAKo5JG_A",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYL4hoZUJIUc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "704517d7-7600-4c3f-8d88-394ae0da89c6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "df.plot(kind='scatter', x='num_filhos', y='num_animais', color='red', alpha=0.2, marker='*', s=90)\n",
        "plt.xlabel('Número de Filhos', fontsize=12)\n",
        "plt.ylabel('Número de Animais', fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kFl_Dyi8NAPj",
        "colab_type": "text"
      },
      "source": [
        "### Bar Plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fUZlfsDMJ0p",
        "colab_type": "text"
      },
      "source": [
        "É o gráfico de barras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxCPmDI-MOha",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcPzmCoLNEkg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "3cf7bc72-b9cf-4513-fd6e-7b61449afef4"
      },
      "source": [
        "df.plot(kind='bar',x='nome',y='idade')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe8771d0358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhuWvHxeM42g",
        "colab_type": "text"
      },
      "source": [
        "##### Outras configurações:\n",
        "\n",
        "*   É possível usar os mesmos atributos do gráfico anterior.\n",
        "*   O atributo `label` permite renomear a legenda.\n",
        "*   O atributo `loc` permite mudar a posição da legenda no gráfico.\n",
        "*   [Lista completa com os posicionamentos da legenda.](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmLrme9OMTDZ",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3I7ZPfKzMVMj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "fe303ac8-b420-437a-ab88-3733e5d40e53"
      },
      "source": [
        "df.plot(kind='bar', x='nome', y='idade', color = 'red', label='Age')\n",
        "plt.legend(loc='upper left')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe8771d0908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "wCN1KaQbNgTG",
        "colab_type": "text"
      },
      "source": [
        "### Line Plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tN5walicNKdM",
        "colab_type": "text"
      },
      "source": [
        "É o gráfico de linhas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxkQ5Hh8NNmT",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MW2cjb0XNhle",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "001fcf15-cb43-45d1-a96a-d9fcca9970b0"
      },
      "source": [
        "# gca stands for 'get current axis'\n",
        "ax = plt.gca()\n",
        "\n",
        "df.plot(kind='line',x='nome',y='num_filhos',ax=ax)\n",
        "df.plot(kind='line',x='nome',y='num_animais', color='red', ax=ax)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbdWjWSPObia",
        "colab_type": "text"
      },
      "source": [
        "### Gráfico de barras agrupado"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YD0FIqsjPDt_",
        "colab_type": "text"
      },
      "source": [
        "É possível agrupar os dados no gráfico de barras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0V_4AhWPHow",
        "colab_type": "text"
      },
      "source": [
        "Exemplo 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwj0cMqFOeHj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "02511b92-e28a-4aab-bc6e-94103fd25759"
      },
      "source": [
        "df.groupby('estado')['nome'].nunique().plot(kind='bar')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oiuaBGOoQFA5",
        "colab_type": "text"
      },
      "source": [
        "### Pie (gráfico de pizza)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xR_bj243PLhW",
        "colab_type": "text"
      },
      "source": [
        "É o gráfico de pizza."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4VQjx4ZuPf36",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "a23b6644-ffd5-49dc-ccf1-a691b2840991"
      },
      "source": [
        "df.plot.pie(y='num_animais')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe872aa0780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liSLFH0YOExo",
        "colab_type": "text"
      },
      "source": [
        "### Salvar o arquivo\n",
        "\n",
        "Usar o comando `plt.savefig(<nome do arquivo>)`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrPI9XqTORca",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "aca9b95f-3e51-4398-dd4e-bb855dea3e03"
      },
      "source": [
        "df.plot(kind='bar', x='nome', y='idade')\n",
        "plt.savefig('grafico.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}