marcelcaraciolo/aula-06-exercicio-visualizacao.ipynb

## aula-06-exercicio-visualizacao.ipynb
{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/marcelcaraciolo/47bae26f6e7fe651ea530ffd93006546/aula-06-exercicio-visualizacao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-hjqRuzZ--x"
      },
      "source": [
        "# Exercício - Visualização de Dados "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8I11qaOZ--z"
      },
      "source": [
        "### Step 1. Importar as bibliotecas necessárias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "wQrU-RY7Z--0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzyi89nxZ--0"
      },
      "source": [
        "### Step 2. Importar os dados deste [endereço](http://dados.recife.pe.gov.br/dataset/61545107-2e7d-4506-b9fa-f5264a7f6ec9/resource/d153f88e-3c25-422b-8b94-ef8d660bf7bf/download/acidentes-2016.csv) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wIw6IUw0Z--1"
      },
      "source": [
        "### Step 3. Atribua o csv a variável acidentes "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ihw0JkziZ--1",
        "outputId": "2d26ca7f-c968-4704-a09a-70a420f6e773"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   longitude  latitude data de abertura hora de abertura       bairro  \\\n",
              "0 -34.905705 -8.037728       01/01/2016            18:30     JAQUEIRA   \n",
              "1 -34.938068 -8.131177       04/01/2016            16:05       JORDÃO   \n",
              "2 -34.915617 -7.993612       06/01/2016            17:33   DOISUNIDOS   \n",
              "3 -34.896884 -8.118186       07/01/2016            10:29    BOAVIAGEM   \n",
              "4 -34.910203 -8.098421       07/01/2016            13:53  IMBIRIBEIRA   \n",
              "\n",
              "                           endereco                          complemento  \\\n",
              "0                    AV RUI BARBOSA      EM FRENTE AO PARQUE DA JAQUEIRA   \n",
              "1                 RUADRALVAROFERRAZ        TERMINALDOÔNIBUSDEJORDÃOBAIXO   \n",
              "2             RUAVINTEEUMDEJUNHO343  PRÓXIMOAASSEMBLÉIADEDEUSEANTENADAOI   \n",
              "3  AVENGENHEIRODOMINGOSFERREIRA3333            EMFRENTEAGALERIASANTAROSA   \n",
              "4     AVMARECHALMASCARENHASDEMORAES                 EMBAIXODOSEMAFORO288   \n",
              "\n",
              "  tipo de ocorrencia quantidade de vitimas  \\\n",
              "0            COLISÃO                     1   \n",
              "1            COLISÃO                     1   \n",
              "2             CHOQUE                     1   \n",
              "3            COLISÃO                     1   \n",
              "4            COLISÃO                     1   \n",
              "\n",
              "                                           descricao        tipo  \n",
              "0  COLISÃO ENTRE QUATRO AUTO; SEGUINTE DE UM CAPO...  Automóveis  \n",
              "1                                             2AUTOS  Automóveis  \n",
              "2                             CHOQUEDEVEÍCULOCOMMURO  Automóveis  \n",
              "3               COLISÃOCOMVITIMA;ONIBUSEAUTO-PASSEIO  Automóveis  \n",
              "4                          COLISAODEMOTOEDOISAUTOS/V  Automóveis  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>data de abertura</th>\n",
              "      <th>hora de abertura</th>\n",
              "      <th>bairro</th>\n",
              "      <th>endereco</th>\n",
              "      <th>complemento</th>\n",
              "      <th>tipo de ocorrencia</th>\n",
              "      <th>quantidade de vitimas</th>\n",
              "      <th>descricao</th>\n",
              "      <th>tipo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-34.905705</td>\n",
              "      <td>-8.037728</td>\n",
              "      <td>01/01/2016</td>\n",
              "      <td>18:30</td>\n",
              "      <td>JAQUEIRA</td>\n",
              "      <td>AV RUI BARBOSA</td>\n",
              "      <td>EM FRENTE AO PARQUE DA JAQUEIRA</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISÃO ENTRE QUATRO AUTO; SEGUINTE DE UM CAPO...</td>\n",
              "      <td>Automóveis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-34.938068</td>\n",
              "      <td>-8.131177</td>\n",
              "      <td>04/01/2016</td>\n",
              "      <td>16:05</td>\n",
              "      <td>JORDÃO</td>\n",
              "      <td>RUADRALVAROFERRAZ</td>\n",
              "      <td>TERMINALDOÔNIBUSDEJORDÃOBAIXO</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>2AUTOS</td>\n",
              "      <td>Automóveis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-34.915617</td>\n",
              "      <td>-7.993612</td>\n",
              "      <td>06/01/2016</td>\n",
              "      <td>17:33</td>\n",
              "      <td>DOISUNIDOS</td>\n",
              "      <td>RUAVINTEEUMDEJUNHO343</td>\n",
              "      <td>PRÓXIMOAASSEMBLÉIADEDEUSEANTENADAOI</td>\n",
              "      <td>CHOQUE</td>\n",
              "      <td>1</td>\n",
              "      <td>CHOQUEDEVEÍCULOCOMMURO</td>\n",
              "      <td>Automóveis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-34.896884</td>\n",
              "      <td>-8.118186</td>\n",
              "      <td>07/01/2016</td>\n",
              "      <td>10:29</td>\n",
              "      <td>BOAVIAGEM</td>\n",
              "      <td>AVENGENHEIRODOMINGOSFERREIRA3333</td>\n",
              "      <td>EMFRENTEAGALERIASANTAROSA</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISÃOCOMVITIMA;ONIBUSEAUTO-PASSEIO</td>\n",
              "      <td>Automóveis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-34.910203</td>\n",
              "      <td>-8.098421</td>\n",
              "      <td>07/01/2016</td>\n",
              "      <td>13:53</td>\n",
              "      <td>IMBIRIBEIRA</td>\n",
              "      <td>AVMARECHALMASCARENHASDEMORAES</td>\n",
              "      <td>EMBAIXODOSEMAFORO288</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISAODEMOTOEDOISAUTOS/V</td>\n",
              "      <td>Automóveis</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "acidentes = pd.read_csv('http://dados.recife.pe.gov.br/dataset/61545107-2e7d-4506-b9fa-f5264a7f6ec9/resource/d153f88e-3c25-422b-8b94-ef8d660bf7bf/download/acidentes-2016.csv', sep=';')\n",
        "acidentes.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acidentes['quantidade de vitimas'] = acidentes['quantidade de vitimas'].str.replace('F', '0')\n",
        "acidentes['quantidade de vitimas'] = acidentes['quantidade de vitimas'].str.replace(\"'''\", '0')\n",
        "acidentes['quantidade de vitimas'] = acidentes['quantidade de vitimas'].str.replace(\"f\", '0')\n",
        "acidentes['quantidade de vitimas'] = acidentes['quantidade de vitimas'].str.replace(\"-\", '0')\n",
        "acidentes['quantidade de vitimas'] = acidentes['quantidade de vitimas'].astype(int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "8yNeRLsaMyaL",
        "outputId": "af1e98bb-c329-4dec-a410-3896c92c45c9"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-88-7459de368b89>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'0'\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      2\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'''\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"f\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0macidentes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantidade de vitimas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\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/python3.9/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5900\u001b[0m         ):\n\u001b[1;32m   5901\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5902\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   5903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5904\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\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/usr/local/lib/python3.9/dist-packages/pandas/core/accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0maccessor_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    183\u001b[0m         \u001b[0;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# https://www.pydanny.com/cached-property.html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/strings/accessor.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStringDtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inferred_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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    182\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_categorical\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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[1;32m    183\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStringDtype\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/python3.9/dist-packages/pandas/core/strings/accessor.py\u001b[0m in \u001b[0;36m_validate\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minferred_dtype\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mallowed_types\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only use .str accessor with string values!\"\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    236\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minferred_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: Can only use .str accessor with string values!"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1Pj0PxTZ--1"
      },
      "source": [
        "### Step 4. Extraia a proporção dos acidentes em relação ao tipo de ocorrência"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_xLfA4HZ--1",
        "outputId": "642a379c-05bf-461a-e741-68c7c619961f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "COLISÃO                   79.119086\n",
              "ATROPELAMENTO             11.990212\n",
              "COLISÃO COM CICLISTA       3.425775\n",
              "CHOQUE                     2.773246\n",
              "COLISÃOa                   0.652529\n",
              "CAPOTAMENTO                0.407830\n",
              "ENTRADA E SAÍDA            0.326264\n",
              "ENGAVETAMENTO              0.326264\n",
              "TOMBAMENTO                 0.326264\n",
              "ACID. DE PERCURSO          0.163132\n",
              "COLISÃO\\t2016 13 050\\t     0.163132\n",
              "ATROPELAMENTO ANIMAL       0.081566\n",
              "QUEDA DE ÁRVORE            0.081566\n",
              "ATROPELAMENTOa             0.081566\n",
              "FISCALIZAÇÃO               0.081566\n",
              "Name: tipo de ocorrencia, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "acidentes['tipo de ocorrencia'].value_counts(normalize=True) * 100\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIF2knzaZ--1"
      },
      "source": [
        "### Step 5. Observe que há tipo de ocorrências que estão escritas erradas. Agrupe os valores. No fim, serão 12 tipos de ocorrências."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "If1fuk2BZ--2",
        "outputId": "cba7e380-81a4-46c1-f8e4-1cdaf46b2309"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TOTAL 13\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['COLISÃO', 'CHOQUE', 'COLISÃO COM CICLISTA', 'ATROPELAMENTO', nan,\n",
              "       'CAPOTAMENTO', 'ATROPELAMENTO ANIMAL', 'ENTRADA E SAÍDA',\n",
              "       'ENGAVETAMENTO', 'ACID. DE PERCURSO', 'QUEDA DE ÁRVORE',\n",
              "       'TOMBAMENTO', 'FISCALIZAÇÃO'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "acidentes['tipo de ocorrencia'].unique()\n",
        "\n",
        "acidentes['tipo de ocorrencia'] = acidentes['tipo de ocorrencia'].str.replace('COLISÃOa', 'COLISÃO')\n",
        "acidentes['tipo de ocorrencia'] = acidentes['tipo de ocorrencia'].str.replace('ATROPELAMENTOa', 'ATROPELAMENTO')\n",
        "acidentes['tipo de ocorrencia'] = acidentes['tipo de ocorrencia'].str.replace('COLISÃO\\t2016 13 050\\t', 'COLISÃO')\n",
        "\n",
        "print('TOTAL', len(acidentes['tipo de ocorrencia'].unique()))\n",
        "acidentes['tipo de ocorrencia'].unique()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acidentes['tipo de ocorrencia'].value_counts(normalize=True) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdsehvqoejWM",
        "outputId": "e98359a6-6062-42f7-ed1e-854a634b3fdb"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "COLISÃO                 79.934747\n",
              "ATROPELAMENTO           12.071778\n",
              "COLISÃO COM CICLISTA     3.425775\n",
              "CHOQUE                   2.773246\n",
              "CAPOTAMENTO              0.407830\n",
              "ENTRADA E SAÍDA          0.326264\n",
              "ENGAVETAMENTO            0.326264\n",
              "TOMBAMENTO               0.326264\n",
              "ACID. DE PERCURSO        0.163132\n",
              "ATROPELAMENTO ANIMAL     0.081566\n",
              "QUEDA DE ÁRVORE          0.081566\n",
              "FISCALIZAÇÃO             0.081566\n",
              "Name: tipo de ocorrencia, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oO88Ke1vZ--2"
      },
      "source": [
        "### Step 6. Apresente um gráfico de barras contendo as informações acima"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "kMJvIa2SZ--2",
        "outputId": "5b2cd8a2-a54d-4fe0-bac8-7d5b2b8b38ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "acidentes_proporcao = acidentes['tipo de ocorrencia'].value_counts(normalize=True) * 100\n",
        "acidentes_proporcao.plot.bar()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQGfJ1ghZ--2"
      },
      "source": [
        "### Step 7. Apresente um gráfico de pizza que mostre a porcentagem da proporção de acidentes por tipo de ocorrência apenas para os tipos de ocorrência que se repetiram 30 vezes ou mais. Na legenda deve estar o tipo de ocorrência. (Não se preocupar com textos sobrescritos)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "B18zKWIPZ--2",
        "outputId": "df9ea024-1f5e-4fcd-8916-c05cc450f475"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='tipo de ocorrencia'>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "acidentes_proporcao = acidentes['tipo de ocorrencia'].value_counts()\n",
        "acidentes_proporcao[acidentes_proporcao >=30].plot.pie(autopct='%1.1f%%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmrWF7nhZ--2"
      },
      "source": [
        "### Step 6. Distribuição de vítimas analisando o tipo de veículo. Agrupe os valores do mesmo tipo de veículo, alguns podem estar escritos errados. São 6 tipos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HafVGSe1Z--3",
        "outputId": "1080ad38-e054-4180-c177-e106bcdab8e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tipo\n",
              "Motocicleta    886\n",
              "Automóvel      227\n",
              "Pedestre       164\n",
              "Ciclista        63\n",
              "Outros          49\n",
              "Ciclomotor      26\n",
              "Name: quantidade de vitimas, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "acidentes['tipo'].unique()\n",
        "acidentes['tipo'] = acidentes['tipo'].str.replace('Automóveis', 'Automóvel')\n",
        "acidentes['tipo']  = acidentes['tipo'].str.replace('Automoveis', 'Automóvel')\n",
        "acidentes['tipo'].unique()\n",
        "\n",
        "acidentes[['tipo','quantidade de vitimas']].groupby('tipo')['quantidade de vitimas'].sum().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dW3jTdSSZ--3"
      },
      "source": [
        "### Step 7. Encontrado o tipo de veículo com o maior número de vítimas, apresente um gráfico de barras horizontal que mostre os TOP 5 bairros em que aconteceram acidentes com esse tipo de veículo "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "P_N49hN1Z--3",
        "outputId": "f1b6fc8a-7a66-41ab-cdf1-d1f1459f98d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "acidentes_bairro_motocicleta = acidentes[acidentes.tipo == 'Motocicleta']['bairro'].value_counts(normalize=False)\n",
        "acidentes_bairro_motocicleta[:5].plot.barh()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acidentes_bairro_motocicleta[:5]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqGPk7AMkN98",
        "outputId": "e57ad4dc-87bd-4341-d7b3-8036c5a8bbef"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BOA VIAGEM      55\n",
              "SANTO AMARO     47\n",
              "IMBIRIBEIRA     46\n",
              "IBURA           35\n",
              "CASA AMARELA    32\n",
              "Name: bairro, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GwBJoq8Z--3"
      },
      "source": [
        "### Step 8. Em qual mês teve mais vítimas? Quantas vítimas?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcoDEx9MZ--3",
        "outputId": "0a910ad2-2c0f-4c29-d555-f79a23e6874b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mes\n",
              "July        197\n",
              "June        180\n",
              "April       173\n",
              "January     161\n",
              "February    160\n",
              "Name: quantidade de vitimas, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "acidentes['mes'] = pd.to_datetime(acidentes['data de abertura']).dt.month_name()\n",
        "acidentes['data de abertura'] = pd.to_datetime(acidentes['data de abertura'])\n",
        "acidentes_mes_vitimas = acidentes.groupby('mes')['quantidade de vitimas'].sum().sort_values(ascending=False)\n",
        "acidentes_mes_vitimas.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0D4MRI9bZ--3"
      },
      "source": [
        "### Step 9. Insira um gráfico de dispersão que apresente no eixo x os meses e no eixo y o número de vítimas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.dates import MonthLocator, DateFormatter\n",
        "from matplotlib import pyplot as plt\n"
      ],
      "metadata": {
        "id": "Egho4xuKQjRe"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pOSiuuzqtANO"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "mZzJoKfeZ--4",
        "outputId": "0e71aca8-ae43-4dcf-d12d-ceb04f54e1ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "acidentes_mes_vitimas = acidentes.groupby(acidentes['data de abertura'].dt.month)['quantidade de vitimas'].sum()\n",
        "fig, ax = plt.subplots()\n",
        "ax.tick_params(axis=\"x\", labelrotation= 90)\n",
        "\n",
        "plt.scatter(acidentes_mes_vitimas.index, acidentes_mes_vitimas)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k01QDUcJZ--4"
      },
      "source": [
        "### Step 10. Crie seu enunciado e seu gráfico. Atenção: Deve ter manipulação de dados e plotagem de gráfico"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Qual período do dia que possui mais acidentes ? E qual o período do dia que tem mais acidentes com vítimas ?"
      ],
      "metadata": {
        "id": "wg1BiTwHC6Ad"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dividimos os horários em 4 turnos (manhã 6-12, tarde 12-18, noite 18-22, madrugada-23-5)"
      ],
      "metadata": {
        "id": "z8PBMAuhKC8J"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "NO7fpZoWZ--4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "acidentes['hora de abertura'] = acidentes['hora de abertura'].str.replace('<br><', '')\n",
        "\n",
        "acidentes['hora de abertura'] = pd.to_datetime(acidentes['hora de abertura']).dt.hour"
      ],
      "metadata": {
        "id": "4YmDAxTZDPI3"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bins = [0, 5, 12, 18, 22, 23]\n",
        "labels = [\"madrugada\",\"manha\",\"tarde\", 'noite', 'madrugada']\n",
        "acidentes['turno'] = pd.cut(acidentes['hora de abertura'], bins=bins, labels=labels ,ordered=False)\n",
        "\n",
        "acidentes[acidentes['turno'] == 'madrugada']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "0Lwcn4j9DZ0X",
        "outputId": "66c8563b-175d-437f-a076-cd85a1e65f98"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      longitude  latitude data de abertura  hora de abertura        bairro  \\\n",
              "6    -34.897739 -8.057084       2016-08-01               2.0   CAMPOGRANDE   \n",
              "15   -34.936068 -8.091713       2016-01-17               5.0        AREIAS   \n",
              "18   -34.909621 -8.124523       2016-01-21               1.0     BOAVIAGEM   \n",
              "22   -34.876375 -8.064103       2016-01-27               5.0  SANTOANTÔNIO   \n",
              "37   -34.896980 -8.064615       2016-01-01               5.0     PAISSANDU   \n",
              "...         ...       ...              ...               ...           ...   \n",
              "1099 -34.894751 -8.051281       2016-11-07               4.0         DERBY   \n",
              "1100 -34.898032 -8.031506       2016-11-07               5.0     ROSARINHO   \n",
              "1112 -34.908661 -8.040995       2016-07-13              23.0         TORRE   \n",
              "1161 -34.928842 -8.071212       2016-07-24              23.0    SAN MARTIN   \n",
              "1187 -34.930419 -8.051249       2016-07-31               4.0      CORDEIRO   \n",
              "\n",
              "                              endereco  \\\n",
              "6        AVGOVERNADORAGAMENONMAGALHAES   \n",
              "15                      AVDRJOSERUFINO   \n",
              "18                      AVDESJOSENEVES   \n",
              "22                   AVMARTINSDEBARROS   \n",
              "37    AV GOVERNADOR AGAMENON MAGALHAES   \n",
              "...                                ...   \n",
              "1099  AV GOVERNADOR AGAMENON MAGALHAES   \n",
              "1100                     AV NORTE 3388   \n",
              "1112                  RUA MARCOS ANDRE   \n",
              "1161                            ARRUDA   \n",
              "1187                            ARRUDA   \n",
              "\n",
              "                                            complemento tipo de ocorrencia  \\\n",
              "6                          SEMÁFOROEMFRENTEAOPRÉDIODOIC             CHOQUE   \n",
              "15                                        NOSEMAFORO044            COLISÃO   \n",
              "18    PORTRASDOSHOOPINGRECIFE;DENTRODOTUNELSENTIDOPI...            COLISÃO   \n",
              "22                      CRUZAMENTOCOMAAVPRIMEIRODEMARÇO            COLISÃO   \n",
              "37    EM FRENTE AO EDFICIO EMPRESARIAL ISACK NILTON;...             CHOQUE   \n",
              "...                                                 ...                ...   \n",
              "1099         PROX DA PRAÇA AMORIN; AO LADO DO BOM PREÇO            COLISÃO   \n",
              "1100              C/ R. GENERAL ABREU E LIMA (POSTO BR)            COLISÃO   \n",
              "1112  NO CRUZAMENTO COM A RUA JOSE BONIFACI; EM FREN...            COLISÃO   \n",
              "1161                   PROX AO GIRADOURO; PROX DA PRAÇA            COLISÃO   \n",
              "1187  CRUZAMENTO COM RUA CLÁUDIO BOOTERHOOD // APOS ...            COLISÃO   \n",
              "\n",
              "      quantidade de vitimas  \\\n",
              "6                         1   \n",
              "15                        1   \n",
              "18                        1   \n",
              "22                        1   \n",
              "37                        1   \n",
              "...                     ...   \n",
              "1099                      1   \n",
              "1100                      1   \n",
              "1112                      1   \n",
              "1161                      1   \n",
              "1187                      1   \n",
              "\n",
              "                                              descricao         tipo  \\\n",
              "6     CONDUTORPERDEUOCONTROLEDOVEÍCULOESECHOCOUCONTR...    Automóvel   \n",
              "15                          COLISAOENTREUMONIBUSEUMAUTO    Automóvel   \n",
              "18                              COLISAOENTREDOISAUTOC/V    Automóvel   \n",
              "22                 COLISÃOENTREUMAUTOPASSEIOCOMUMONIBUS    Automóvel   \n",
              "37          MOTO CHOCOU-SE CONTRA A CALÇADA; COM VITIMA  Motocicleta   \n",
              "...                                                 ...          ...   \n",
              "1099                           COLISAO ENTRE DOIS AUTOS  Motocicleta   \n",
              "1100  COLISÃO COM VÍTIMA ENTRE AUTO E MOTOCICLETA (8...  Motocicleta   \n",
              "1112                          COLISÃO ENTRE AUTO E MOTO  Motocicleta   \n",
              "1161                          COLISAO ENTRE MOTO E AUTO  Motocicleta   \n",
              "1187                      COLISÃO ENTRE MOTO E AUTO C/V  Motocicleta   \n",
              "\n",
              "           mes      turno  \n",
              "6       August  madrugada  \n",
              "15     January  madrugada  \n",
              "18     January  madrugada  \n",
              "22     January  madrugada  \n",
              "37     January  madrugada  \n",
              "...        ...        ...  \n",
              "1099  November  madrugada  \n",
              "1100  November  madrugada  \n",
              "1112      July  madrugada  \n",
              "1161      July  madrugada  \n",
              "1187      July  madrugada  \n",
              "\n",
              "[98 rows x 13 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>data de abertura</th>\n",
              "      <th>hora de abertura</th>\n",
              "      <th>bairro</th>\n",
              "      <th>endereco</th>\n",
              "      <th>complemento</th>\n",
              "      <th>tipo de ocorrencia</th>\n",
              "      <th>quantidade de vitimas</th>\n",
              "      <th>descricao</th>\n",
              "      <th>tipo</th>\n",
              "      <th>mes</th>\n",
              "      <th>turno</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-34.897739</td>\n",
              "      <td>-8.057084</td>\n",
              "      <td>2016-08-01</td>\n",
              "      <td>2.0</td>\n",
              "      <td>CAMPOGRANDE</td>\n",
              "      <td>AVGOVERNADORAGAMENONMAGALHAES</td>\n",
              "      <td>SEMÁFOROEMFRENTEAOPRÉDIODOIC</td>\n",
              "      <td>CHOQUE</td>\n",
              "      <td>1</td>\n",
              "      <td>CONDUTORPERDEUOCONTROLEDOVEÍCULOESECHOCOUCONTR...</td>\n",
              "      <td>Automóvel</td>\n",
              "      <td>August</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>-34.936068</td>\n",
              "      <td>-8.091713</td>\n",
              "      <td>2016-01-17</td>\n",
              "      <td>5.0</td>\n",
              "      <td>AREIAS</td>\n",
              "      <td>AVDRJOSERUFINO</td>\n",
              "      <td>NOSEMAFORO044</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISAOENTREUMONIBUSEUMAUTO</td>\n",
              "      <td>Automóvel</td>\n",
              "      <td>January</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>-34.909621</td>\n",
              "      <td>-8.124523</td>\n",
              "      <td>2016-01-21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>BOAVIAGEM</td>\n",
              "      <td>AVDESJOSENEVES</td>\n",
              "      <td>PORTRASDOSHOOPINGRECIFE;DENTRODOTUNELSENTIDOPI...</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISAOENTREDOISAUTOC/V</td>\n",
              "      <td>Automóvel</td>\n",
              "      <td>January</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>-34.876375</td>\n",
              "      <td>-8.064103</td>\n",
              "      <td>2016-01-27</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SANTOANTÔNIO</td>\n",
              "      <td>AVMARTINSDEBARROS</td>\n",
              "      <td>CRUZAMENTOCOMAAVPRIMEIRODEMARÇO</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISÃOENTREUMAUTOPASSEIOCOMUMONIBUS</td>\n",
              "      <td>Automóvel</td>\n",
              "      <td>January</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>-34.896980</td>\n",
              "      <td>-8.064615</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>5.0</td>\n",
              "      <td>PAISSANDU</td>\n",
              "      <td>AV GOVERNADOR AGAMENON MAGALHAES</td>\n",
              "      <td>EM FRENTE AO EDFICIO EMPRESARIAL ISACK NILTON;...</td>\n",
              "      <td>CHOQUE</td>\n",
              "      <td>1</td>\n",
              "      <td>MOTO CHOCOU-SE CONTRA A CALÇADA; COM VITIMA</td>\n",
              "      <td>Motocicleta</td>\n",
              "      <td>January</td>\n",
              "      <td>madrugada</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1099</th>\n",
              "      <td>-34.894751</td>\n",
              "      <td>-8.051281</td>\n",
              "      <td>2016-11-07</td>\n",
              "      <td>4.0</td>\n",
              "      <td>DERBY</td>\n",
              "      <td>AV GOVERNADOR AGAMENON MAGALHAES</td>\n",
              "      <td>PROX DA PRAÇA AMORIN; AO LADO DO BOM PREÇO</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISAO ENTRE DOIS AUTOS</td>\n",
              "      <td>Motocicleta</td>\n",
              "      <td>November</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1100</th>\n",
              "      <td>-34.898032</td>\n",
              "      <td>-8.031506</td>\n",
              "      <td>2016-11-07</td>\n",
              "      <td>5.0</td>\n",
              "      <td>ROSARINHO</td>\n",
              "      <td>AV NORTE 3388</td>\n",
              "      <td>C/ R. GENERAL ABREU E LIMA (POSTO BR)</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISÃO COM VÍTIMA ENTRE AUTO E MOTOCICLETA (8...</td>\n",
              "      <td>Motocicleta</td>\n",
              "      <td>November</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1112</th>\n",
              "      <td>-34.908661</td>\n",
              "      <td>-8.040995</td>\n",
              "      <td>2016-07-13</td>\n",
              "      <td>23.0</td>\n",
              "      <td>TORRE</td>\n",
              "      <td>RUA MARCOS ANDRE</td>\n",
              "      <td>NO CRUZAMENTO COM A RUA JOSE BONIFACI; EM FREN...</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISÃO ENTRE AUTO E MOTO</td>\n",
              "      <td>Motocicleta</td>\n",
              "      <td>July</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1161</th>\n",
              "      <td>-34.928842</td>\n",
              "      <td>-8.071212</td>\n",
              "      <td>2016-07-24</td>\n",
              "      <td>23.0</td>\n",
              "      <td>SAN MARTIN</td>\n",
              "      <td>ARRUDA</td>\n",
              "      <td>PROX AO GIRADOURO; PROX DA PRAÇA</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISAO ENTRE MOTO E AUTO</td>\n",
              "      <td>Motocicleta</td>\n",
              "      <td>July</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1187</th>\n",
              "      <td>-34.930419</td>\n",
              "      <td>-8.051249</td>\n",
              "      <td>2016-07-31</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CORDEIRO</td>\n",
              "      <td>ARRUDA</td>\n",
              "      <td>CRUZAMENTO COM RUA CLÁUDIO BOOTERHOOD // APOS ...</td>\n",
              "      <td>COLISÃO</td>\n",
              "      <td>1</td>\n",
              "      <td>COLISÃO ENTRE MOTO E AUTO C/V</td>\n",
              "      <td>Motocicleta</td>\n",
              "      <td>July</td>\n",
              "      <td>madrugada</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>98 rows × 13 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Turno com maior numero de acidentes (Manha - 5-12)\n",
        "acidentes['turno'].value_counts().plot(kind='pie')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5_dVUwR8LdHo",
        "outputId": "d5652eb5-ceb4-46ce-f20d-d93810382e21"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='turno'>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Turno com maior quantidade de vitimas (Tarde)\n",
        "acidentes.groupby(['turno'])['quantidade de vitimas'].sum().plot(kind='pie')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "N0fcvKn2MCJK",
        "outputId": "e5728bc1-da8b-4148-f2ad-44f64089e48c"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='quantidade de vitimas'>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}