Analisando_a_Violência_no_Rio_de_Janeiro.ipynb

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  "metadata": {
    "colab": {
      "name": "Analisando a Violência no Rio de Janeiro.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eFQZydM9O9J",
        "colab_type": "text"
      },
      "source": [
        "<img alt=\"Colaboratory logo\" width=\"15%\" src=\"https://raw.githubusercontent.com/carlosfab/escola-data-science/master/img/novo_logo_bg_claro.png\">\n",
        "\n",
        "#### **Data Science na Prática 2.0**\n",
        "*by Leonardo Cotta*\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pywtVZgmVzJf",
        "colab_type": "text"
      },
      "source": [
        "<img alt=\"Rio De Janeiro by Free Pik\" width=\"100%\" src=\"https://raw.githubusercontent.com/LeonardoCDP/Data-Science/master/Imagens/Rio_Janeiro.jpg\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLI0gJtzDGuQ",
        "colab_type": "text"
      },
      "source": [
        "# Analisando a Violência no Rio de Janeiro\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZOUYwUO3XUh",
        "colab_type": "text"
      },
      "source": [
        "**Rio de Janeiro** um dos cartões postais do **Brasil** com vários pontos turísticos e suas belas praias, com uma população estimada de 17.264.943 pessoas tendo a densidade demográfica de 365,23 habitantes por kilomêtro quadrado abragindo a área de 43.750,527 Km² com um total de 6.725.882 veículos registrados segundo o IBGE (2019).\n",
        " \n",
        "Infelizmente como nem tudo é um mar de rosas iremos analisar os índices de violência disponibilizados pelo **Governo do Estado do Rio de Janeiro**. \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zxQHhIUDF_2",
        "colab_type": "text"
      },
      "source": [
        "## Obtenção dos Dados\n",
        "\n",
        "Utilizaremos a Base de dados sobre a criminalidade no **Rio de Janeiro** disponibilizada pela **Polícia Militar do Rio de Janeiro**\n",
        "\n",
        "A base de dados original encontra-se neste link para consulta-la [Clique Aqui](http://www.ispdados.rj.gov.br/estatistica.html).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vh4at0l90nr",
        "colab_type": "text"
      },
      "source": [
        "### Importando os dados\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYLNme_OCX_R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importando as bibliotecas que iremos utilizar\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns;\n",
        "\n",
        "# importando os dados e criando o DataFrame\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/carlosfab/dsnp2/master/datasets/violencia_rio.csv')"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb5By2nIMS6m",
        "colab_type": "text"
      },
      "source": [
        "## Análise Inicial dos Dados\n",
        "\n",
        "Iremos realizar uma análise exploratória no dados disponibilizados pelo **Governo do Estado do Rio de Janeiro** da base de dados da **Polícia Militar do Estado do Rio de Janeiro** sobre o índice de criminalidade.\n",
        "\n",
        "A base de dados utilizada encontra-se neste link para acessá-la [Clique Aqui](http://www.ispdados.rj.gov.br/estatistica.html).\n",
        "\n",
        "E disponbilizado um painel interativo no site do **Governo do Estado do Rio de Janeiro** para acessá-lo [Clique aqui](http://www.ispvisualizacao.rj.gov.br/index.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fr5y7La6-aKM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "98d78cf2-79f8-4fd3-e551-c28998ce1eac"
      },
      "source": [
        "# analisando o tamanho do DataFrame\n",
        "print('Colunas e Linhas no DataFrame: ')\n",
        "print(f'{df.shape[1]} Colunas')\n",
        "print(f'{df.shape[0]} Linhas')"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Colunas e Linhas no DataFrame: \n",
            "56 Colunas\n",
            "344 Linhas\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eOkcUwF_-yyL",
        "colab_type": "text"
      },
      "source": [
        "Nosso DataFrame possui 344 Linhas e 56 Colunas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BV_6ACn-9u5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "68ca8135-02fb-44d0-9f70-912bb3297dc8"
      },
      "source": [
        "# imprimindo o nome das colunas presente no DataFrame\n",
        "print('Nome das Colunas Presente no DataFrame: ')\n",
        "df.columns"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nome das Colunas Presente no DataFrame: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['vano', 'mes', 'hom_doloso', 'lesao_corp_morte', 'latrocinio',\n",
              "       'hom_por_interv_policial', 'tentat_hom', 'lesao_corp_dolosa', 'estupro',\n",
              "       'hom_culposo', 'lesao_corp_culposa', 'roubo_comercio',\n",
              "       'roubo_residencia', 'roubo_veiculo', 'roubo_carga', 'roubo_transeunte',\n",
              "       'roubo_em_coletivo', 'roubo_banco', 'roubo_cx_eletronico',\n",
              "       'roubo_celular', 'roubo_conducao_saque', 'roubo_apos_saque',\n",
              "       'roubo_bicicleta', 'outros_roubos', 'total_roubos', 'furto_veiculos',\n",
              "       'furto_transeunte', 'furto_coletivo', 'furto_celular',\n",
              "       'furto_bicicleta', 'outros_furtos', 'total_furtos', 'sequestro',\n",
              "       'extorsao', 'sequestro_relampago', 'estelionato', 'apreensao_drogas',\n",
              "       'posse_drogas', 'trafico_drogas', 'apreensao_drogas_sem_autor',\n",
              "       'recuperacao_veiculos', 'apf', 'aaapai', 'cmp', 'cmba', 'ameaca',\n",
              "       'pessoas_desaparecidas', 'encontro_cadaver', 'encontro_ossada',\n",
              "       'pol_militares_mortos_serv', 'pol_civis_mortos_serv',\n",
              "       'indicador_letalidade', 'indicador_roubo_rua', 'indicador_roubo_veic',\n",
              "       'registro_ocorrencias', 'fase'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpNSmLi5_gKt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e1b1790f-0808-4ed5-d897-f803a04fec9a"
      },
      "source": [
        "# verificando os tipos das colunas presente no DataFrame\n",
        "print('Nome da coluna        \\t        Tipo')\n",
        "df.dtypes"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nome da coluna        \t        Tipo\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vano                            int64\n",
              "mes                             int64\n",
              "hom_doloso                      int64\n",
              "lesao_corp_morte              float64\n",
              "latrocinio                      int64\n",
              "hom_por_interv_policial       float64\n",
              "tentat_hom                      int64\n",
              "lesao_corp_dolosa               int64\n",
              "estupro                       float64\n",
              "hom_culposo                   float64\n",
              "lesao_corp_culposa            float64\n",
              "roubo_comercio                  int64\n",
              "roubo_residencia                int64\n",
              "roubo_veiculo                   int64\n",
              "roubo_carga                     int64\n",
              "roubo_transeunte                int64\n",
              "roubo_em_coletivo               int64\n",
              "roubo_banco                     int64\n",
              "roubo_cx_eletronico           float64\n",
              "roubo_celular                 float64\n",
              "roubo_conducao_saque          float64\n",
              "roubo_apos_saque              float64\n",
              "roubo_bicicleta               float64\n",
              "outros_roubos                   int64\n",
              "total_roubos                    int64\n",
              "furto_veiculos                  int64\n",
              "furto_transeunte                int64\n",
              "furto_coletivo                float64\n",
              "furto_celular                 float64\n",
              "furto_bicicleta               float64\n",
              "outros_furtos                   int64\n",
              "total_furtos                    int64\n",
              "sequestro                     float64\n",
              "extorsao                      float64\n",
              "sequestro_relampago           float64\n",
              "estelionato                   float64\n",
              "apreensao_drogas                int64\n",
              "posse_drogas                  float64\n",
              "trafico_drogas                float64\n",
              "apreensao_drogas_sem_autor    float64\n",
              "recuperacao_veiculos            int64\n",
              "apf                           float64\n",
              "aaapai                        float64\n",
              "cmp                           float64\n",
              "cmba                          float64\n",
              "ameaca                        float64\n",
              "pessoas_desaparecidas         float64\n",
              "encontro_cadaver                int64\n",
              "encontro_ossada               float64\n",
              "pol_militares_mortos_serv     float64\n",
              "pol_civis_mortos_serv         float64\n",
              "indicador_letalidade            int64\n",
              "indicador_roubo_rua             int64\n",
              "indicador_roubo_veic            int64\n",
              "registro_ocorrencias          float64\n",
              "fase                            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6SML1HqBCFU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "179973b8-6307-46e8-b976-f9b7e9557672"
      },
      "source": [
        "# verificando a quantidade de cada tipo de coluna presente no DataFrame\n",
        "print('Tipo   \\t   Quantidade')\n",
        "df.dtypes.value_counts()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tipo   \t   Quantidade\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float64    30\n",
              "int64      26\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Hck5xqWAdf_",
        "colab_type": "text"
      },
      "source": [
        "O DataFrame possui 30 colunas do tipo **Decimal** e 26 colunas do tipo **Inteiro**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAtrz-oaBT4S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "980d3f3c-69c3-40c2-c60b-98bff4153a26"
      },
      "source": [
        "# visualizando as 5 primeiras linhas do DataFrame\n",
        "df.head()"
      ],
      "execution_count": 89,
      "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>vano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>tentat_hom</th>\n",
              "      <th>lesao_corp_dolosa</th>\n",
              "      <th>estupro</th>\n",
              "      <th>hom_culposo</th>\n",
              "      <th>lesao_corp_culposa</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_transeunte</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_conducao_saque</th>\n",
              "      <th>roubo_apos_saque</th>\n",
              "      <th>roubo_bicicleta</th>\n",
              "      <th>outros_roubos</th>\n",
              "      <th>total_roubos</th>\n",
              "      <th>furto_veiculos</th>\n",
              "      <th>furto_transeunte</th>\n",
              "      <th>furto_coletivo</th>\n",
              "      <th>furto_celular</th>\n",
              "      <th>furto_bicicleta</th>\n",
              "      <th>outros_furtos</th>\n",
              "      <th>total_furtos</th>\n",
              "      <th>sequestro</th>\n",
              "      <th>extorsao</th>\n",
              "      <th>sequestro_relampago</th>\n",
              "      <th>estelionato</th>\n",
              "      <th>apreensao_drogas</th>\n",
              "      <th>posse_drogas</th>\n",
              "      <th>trafico_drogas</th>\n",
              "      <th>apreensao_drogas_sem_autor</th>\n",
              "      <th>recuperacao_veiculos</th>\n",
              "      <th>apf</th>\n",
              "      <th>aaapai</th>\n",
              "      <th>cmp</th>\n",
              "      <th>cmba</th>\n",
              "      <th>ameaca</th>\n",
              "      <th>pessoas_desaparecidas</th>\n",
              "      <th>encontro_cadaver</th>\n",
              "      <th>encontro_ossada</th>\n",
              "      <th>pol_militares_mortos_serv</th>\n",
              "      <th>pol_civis_mortos_serv</th>\n",
              "      <th>indicador_letalidade</th>\n",
              "      <th>indicador_roubo_rua</th>\n",
              "      <th>indicador_roubo_veic</th>\n",
              "      <th>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1991</td>\n",
              "      <td>1</td>\n",
              "      <td>657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>162</td>\n",
              "      <td>3051</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>350</td>\n",
              "      <td>188</td>\n",
              "      <td>1174</td>\n",
              "      <td>63</td>\n",
              "      <td>967</td>\n",
              "      <td>381</td>\n",
              "      <td>37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>158</td>\n",
              "      <td>3318</td>\n",
              "      <td>2009</td>\n",
              "      <td>1147</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3561</td>\n",
              "      <td>6717</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>133</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>872</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>217</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>672</td>\n",
              "      <td>1348</td>\n",
              "      <td>1174</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1991</td>\n",
              "      <td>2</td>\n",
              "      <td>732</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>175</td>\n",
              "      <td>3421</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>357</td>\n",
              "      <td>171</td>\n",
              "      <td>1097</td>\n",
              "      <td>64</td>\n",
              "      <td>963</td>\n",
              "      <td>432</td>\n",
              "      <td>35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>142</td>\n",
              "      <td>3261</td>\n",
              "      <td>1850</td>\n",
              "      <td>1066</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3309</td>\n",
              "      <td>6225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>127</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>751</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>209</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>749</td>\n",
              "      <td>1395</td>\n",
              "      <td>1097</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1991</td>\n",
              "      <td>3</td>\n",
              "      <td>713</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>216</td>\n",
              "      <td>3613</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>443</td>\n",
              "      <td>191</td>\n",
              "      <td>1265</td>\n",
              "      <td>71</td>\n",
              "      <td>963</td>\n",
              "      <td>422</td>\n",
              "      <td>43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>131</td>\n",
              "      <td>3529</td>\n",
              "      <td>2084</td>\n",
              "      <td>1062</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3573</td>\n",
              "      <td>6719</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>188</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>738</td>\n",
              "      <td>1385</td>\n",
              "      <td>1265</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1991</td>\n",
              "      <td>4</td>\n",
              "      <td>634</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>200</td>\n",
              "      <td>3211</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>425</td>\n",
              "      <td>199</td>\n",
              "      <td>1415</td>\n",
              "      <td>81</td>\n",
              "      <td>1186</td>\n",
              "      <td>354</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>3808</td>\n",
              "      <td>2085</td>\n",
              "      <td>1195</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3290</td>\n",
              "      <td>6570</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>133</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>787</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>140</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>654</td>\n",
              "      <td>1540</td>\n",
              "      <td>1415</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1991</td>\n",
              "      <td>5</td>\n",
              "      <td>650</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>146</td>\n",
              "      <td>3051</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>553</td>\n",
              "      <td>231</td>\n",
              "      <td>1449</td>\n",
              "      <td>82</td>\n",
              "      <td>931</td>\n",
              "      <td>335</td>\n",
              "      <td>27</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>147</td>\n",
              "      <td>3755</td>\n",
              "      <td>2185</td>\n",
              "      <td>723</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3546</td>\n",
              "      <td>6454</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>163</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>845</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>670</td>\n",
              "      <td>1266</td>\n",
              "      <td>1449</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vano  mes  hom_doloso  ...  indicador_roubo_veic  registro_ocorrencias  fase\n",
              "0  1991    1         657  ...                  1174                   NaN     3\n",
              "1  1991    2         732  ...                  1097                   NaN     3\n",
              "2  1991    3         713  ...                  1265                   NaN     3\n",
              "3  1991    4         634  ...                  1415                   NaN     3\n",
              "4  1991    5         650  ...                  1449                   NaN     3\n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYTvAmL5v5Me",
        "colab_type": "text"
      },
      "source": [
        "Podemos notar a presença de valores ausentes no DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BBKJOMaBfRx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "56879071-060a-4f0b-ccbd-8c35fa096f43"
      },
      "source": [
        "# verificando a presença e porcentagem de dados ausentes no DataFrame\n",
        "print('Colunas         \\t      Porcentagem')\n",
        "(df.isnull().sum() / df.shape[0]).sort_values(ascending=False)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Colunas         \t      Porcentagem\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "roubo_bicicleta               0.802326\n",
              "furto_bicicleta               0.802326\n",
              "apreensao_drogas_sem_autor    0.523256\n",
              "posse_drogas                  0.523256\n",
              "apf                           0.523256\n",
              "aaapai                        0.523256\n",
              "cmp                           0.523256\n",
              "trafico_drogas                0.523256\n",
              "cmba                          0.523256\n",
              "sequestro_relampago           0.418605\n",
              "furto_coletivo                0.418605\n",
              "extorsao                      0.418605\n",
              "furto_celular                 0.418605\n",
              "roubo_apos_saque              0.418605\n",
              "roubo_conducao_saque          0.418605\n",
              "roubo_cx_eletronico           0.418605\n",
              "estupro                       0.418605\n",
              "estelionato                   0.418605\n",
              "pol_civis_mortos_serv         0.418605\n",
              "pol_militares_mortos_serv     0.418605\n",
              "pessoas_desaparecidas         0.383721\n",
              "encontro_ossada               0.383721\n",
              "roubo_celular                 0.313953\n",
              "lesao_corp_culposa            0.279070\n",
              "ameaca                        0.279070\n",
              "sequestro                     0.279070\n",
              "lesao_corp_morte              0.279070\n",
              "registro_ocorrencias          0.279070\n",
              "hom_por_interv_policial       0.244186\n",
              "hom_culposo                   0.244186\n",
              "tentat_hom                    0.000000\n",
              "roubo_comercio                0.000000\n",
              "roubo_residencia              0.000000\n",
              "latrocinio                    0.000000\n",
              "lesao_corp_dolosa             0.000000\n",
              "hom_doloso                    0.000000\n",
              "roubo_transeunte              0.000000\n",
              "mes                           0.000000\n",
              "roubo_veiculo                 0.000000\n",
              "roubo_carga                   0.000000\n",
              "fase                          0.000000\n",
              "roubo_em_coletivo             0.000000\n",
              "roubo_banco                   0.000000\n",
              "outros_roubos                 0.000000\n",
              "total_roubos                  0.000000\n",
              "furto_veiculos                0.000000\n",
              "furto_transeunte              0.000000\n",
              "outros_furtos                 0.000000\n",
              "total_furtos                  0.000000\n",
              "apreensao_drogas              0.000000\n",
              "recuperacao_veiculos          0.000000\n",
              "encontro_cadaver              0.000000\n",
              "indicador_letalidade          0.000000\n",
              "indicador_roubo_rua           0.000000\n",
              "indicador_roubo_veic          0.000000\n",
              "vano                          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAGB7xrFD1fI",
        "colab_type": "text"
      },
      "source": [
        "O Nosso DataFrame possui muitas colunas com dados ausentes ou nulos sento elas:\n",
        "* roubo_bicicleta e furto_bicicleta  com 80% dos dados ausentes\n",
        "* apreensao_drogas_sem_autor, posse_drogas, apf, aaapai,cmp, trafico_drogas, cmba com 52% dos dados ausentes\n",
        "* sequestro_relampago, furto_coletivo, extorsao,furto_celular, roubo_apos_saque, roubo_conducao_saque, roubo_cx_eletronico, estupro, estelionato, pol_civis_mortos_serv, pol_militares_mortos_serv dos dados ausentes 41%\n",
        "* pessoas_desaparecidas e encontro_ossada com 38% dos dados ausentes\n",
        "* roubo_celular com 31% dos dados ausentes\n",
        "* lesao_corp_culposa, ameaca, sequestro, lesao_corp_morte, registro_ocorrencias com 27% dos dados ausentes\n",
        "* hom_por_interv_policial e hom_culposo com 24% dos dados ausentes\n",
        "\n",
        "Iremos remover as colunas com dados ausentes do DateFrame pois utilizaremos os índices completos para uma melhor análise exploratória.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7CdROjLGF0b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# removendo colunas com dados ausentes ou nulos\n",
        "df_clear = df.dropna(axis=1)"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7156zOgzHcL9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "c1f4315e-f917-435c-9b04-15e4b4f34b23"
      },
      "source": [
        "# verificando se a colunas com valores ausentes foram removidas\n",
        "print('Colunas \\t Número de dados ausentes')\n",
        "df_clear.isnull().sum()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Colunas \t Número de dados ausentes\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vano                    0\n",
              "mes                     0\n",
              "hom_doloso              0\n",
              "latrocinio              0\n",
              "tentat_hom              0\n",
              "lesao_corp_dolosa       0\n",
              "roubo_comercio          0\n",
              "roubo_residencia        0\n",
              "roubo_veiculo           0\n",
              "roubo_carga             0\n",
              "roubo_transeunte        0\n",
              "roubo_em_coletivo       0\n",
              "roubo_banco             0\n",
              "outros_roubos           0\n",
              "total_roubos            0\n",
              "furto_veiculos          0\n",
              "furto_transeunte        0\n",
              "outros_furtos           0\n",
              "total_furtos            0\n",
              "apreensao_drogas        0\n",
              "recuperacao_veiculos    0\n",
              "encontro_cadaver        0\n",
              "indicador_letalidade    0\n",
              "indicador_roubo_rua     0\n",
              "indicador_roubo_veic    0\n",
              "fase                    0\n",
              "data                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yQneJQRQD7h",
        "colab_type": "text"
      },
      "source": [
        "Agora com o DataFrame sem valores nulos ou ausentes iremos proseguir com a análise."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G6dnsNYm-41g",
        "colab_type": "text"
      },
      "source": [
        "## Informações Estatísticas da Violência no Rio de Janeiro\n",
        "\n",
        "Analisaremos os índices de roubo, furto e recuperação de veículos os índices de homicídios dolosos no estado, descobrindo o média de cada índice, a porcentagem de veículos recuperados em relação a quantidade de veículos roubados e furtados e o maior e menor número registrados de homicídios ocorridos no período(mensal).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qy2KsrBPTrDC",
        "colab_type": "text"
      },
      "source": [
        "Visualizando o resumo estatístico de cada coluna obtendo os valores totais do período."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PowxGYZwQUJg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "2586e2e9-89eb-41e4-809f-8a886395d03a"
      },
      "source": [
        "# visualizando o resumo estatístico do DataFrame\n",
        "df_clear.describe()"
      ],
      "execution_count": 93,
      "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>vano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>tentat_hom</th>\n",
              "      <th>lesao_corp_dolosa</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_transeunte</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>outros_roubos</th>\n",
              "      <th>total_roubos</th>\n",
              "      <th>furto_veiculos</th>\n",
              "      <th>furto_transeunte</th>\n",
              "      <th>outros_furtos</th>\n",
              "      <th>total_furtos</th>\n",
              "      <th>apreensao_drogas</th>\n",
              "      <th>recuperacao_veiculos</th>\n",
              "      <th>encontro_cadaver</th>\n",
              "      <th>indicador_letalidade</th>\n",
              "      <th>indicador_roubo_rua</th>\n",
              "      <th>indicador_roubo_veic</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2004.837209</td>\n",
              "      <td>6.453488</td>\n",
              "      <td>504.555233</td>\n",
              "      <td>15.668605</td>\n",
              "      <td>318.267442</td>\n",
              "      <td>5492.799419</td>\n",
              "      <td>455.005814</td>\n",
              "      <td>133.284884</td>\n",
              "      <td>2448.697674</td>\n",
              "      <td>340.322674</td>\n",
              "      <td>3418.520349</td>\n",
              "      <td>659.002907</td>\n",
              "      <td>9.375000</td>\n",
              "      <td>1211.223837</td>\n",
              "      <td>9428.119186</td>\n",
              "      <td>1674.671512</td>\n",
              "      <td>1518.921512</td>\n",
              "      <td>5994.328488</td>\n",
              "      <td>10433.177326</td>\n",
              "      <td>1071.930233</td>\n",
              "      <td>1772.238372</td>\n",
              "      <td>66.261628</td>\n",
              "      <td>577.691860</td>\n",
              "      <td>4734.738372</td>\n",
              "      <td>2448.697674</td>\n",
              "      <td>2.994186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.289036</td>\n",
              "      <td>3.447759</td>\n",
              "      <td>113.774481</td>\n",
              "      <td>5.861427</td>\n",
              "      <td>127.606645</td>\n",
              "      <td>1514.220579</td>\n",
              "      <td>119.765076</td>\n",
              "      <td>44.317963</td>\n",
              "      <td>789.689559</td>\n",
              "      <td>223.855243</td>\n",
              "      <td>2459.017549</td>\n",
              "      <td>297.622342</td>\n",
              "      <td>10.564894</td>\n",
              "      <td>800.576723</td>\n",
              "      <td>4578.013053</td>\n",
              "      <td>294.852020</td>\n",
              "      <td>971.853102</td>\n",
              "      <td>1964.767779</td>\n",
              "      <td>3696.725756</td>\n",
              "      <td>643.381991</td>\n",
              "      <td>656.761843</td>\n",
              "      <td>34.464519</td>\n",
              "      <td>104.973929</td>\n",
              "      <td>3204.054864</td>\n",
              "      <td>789.689559</td>\n",
              "      <td>0.076138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1991.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>2350.000000</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>1097.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>688.000000</td>\n",
              "      <td>281.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>3261.000000</td>\n",
              "      <td>1159.000000</td>\n",
              "      <td>322.000000</td>\n",
              "      <td>2322.000000</td>\n",
              "      <td>4691.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>729.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>1086.000000</td>\n",
              "      <td>1097.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1998.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>413.750000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>4067.000000</td>\n",
              "      <td>365.750000</td>\n",
              "      <td>103.750000</td>\n",
              "      <td>1889.750000</td>\n",
              "      <td>208.250000</td>\n",
              "      <td>1053.000000</td>\n",
              "      <td>430.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>811.750000</td>\n",
              "      <td>5107.750000</td>\n",
              "      <td>1430.500000</td>\n",
              "      <td>732.000000</td>\n",
              "      <td>3978.000000</td>\n",
              "      <td>6700.250000</td>\n",
              "      <td>714.500000</td>\n",
              "      <td>1276.750000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>1557.000000</td>\n",
              "      <td>1889.750000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2005.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>507.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>318.500000</td>\n",
              "      <td>5830.500000</td>\n",
              "      <td>433.500000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>2322.000000</td>\n",
              "      <td>285.500000</td>\n",
              "      <td>2788.500000</td>\n",
              "      <td>592.000000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>1154.000000</td>\n",
              "      <td>9243.500000</td>\n",
              "      <td>1637.500000</td>\n",
              "      <td>1076.500000</td>\n",
              "      <td>6559.000000</td>\n",
              "      <td>10448.500000</td>\n",
              "      <td>979.500000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>60.500000</td>\n",
              "      <td>590.000000</td>\n",
              "      <td>4190.000000</td>\n",
              "      <td>2322.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2012.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>577.250000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>398.250000</td>\n",
              "      <td>6582.500000</td>\n",
              "      <td>536.250000</td>\n",
              "      <td>151.250000</td>\n",
              "      <td>2778.000000</td>\n",
              "      <td>401.250000</td>\n",
              "      <td>5385.250000</td>\n",
              "      <td>784.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>1635.000000</td>\n",
              "      <td>11590.500000</td>\n",
              "      <td>1833.250000</td>\n",
              "      <td>2334.250000</td>\n",
              "      <td>7601.500000</td>\n",
              "      <td>13877.500000</td>\n",
              "      <td>1546.000000</td>\n",
              "      <td>2202.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>649.000000</td>\n",
              "      <td>6872.750000</td>\n",
              "      <td>2778.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>831.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>645.000000</td>\n",
              "      <td>9050.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>5358.000000</td>\n",
              "      <td>1329.000000</td>\n",
              "      <td>9669.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>3661.000000</td>\n",
              "      <td>23203.000000</td>\n",
              "      <td>2630.000000</td>\n",
              "      <td>5368.000000</td>\n",
              "      <td>9880.000000</td>\n",
              "      <td>18003.000000</td>\n",
              "      <td>2543.000000</td>\n",
              "      <td>3695.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>848.000000</td>\n",
              "      <td>13833.000000</td>\n",
              "      <td>5358.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              vano         mes  ...  indicador_roubo_veic        fase\n",
              "count   344.000000  344.000000  ...            344.000000  344.000000\n",
              "mean   2004.837209    6.453488  ...           2448.697674    2.994186\n",
              "std       8.289036    3.447759  ...            789.689559    0.076138\n",
              "min    1991.000000    1.000000  ...           1097.000000    2.000000\n",
              "25%    1998.000000    3.000000  ...           1889.750000    3.000000\n",
              "50%    2005.000000    6.000000  ...           2322.000000    3.000000\n",
              "75%    2012.000000    9.000000  ...           2778.000000    3.000000\n",
              "max    2019.000000   12.000000  ...           5358.000000    3.000000\n",
              "\n",
              "[8 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IP9glGXgQ62R",
        "colab_type": "text"
      },
      "source": [
        "Iremos analisar a média dos veículos roubados, furtados e recuperados no **Estado do Rio de Janeiro**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOVTjE-TQ3kq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "88372419-91d1-4561-bba1-68767751fa39"
      },
      "source": [
        "# visualizando a média de veiculos Roubados, furtados e recuperados\n",
        "print(f'Média de Veículos Roubados: {df_clear.roubo_veiculo.mean():.2f} veículos')\n",
        "print(f'Média de Veículos Furtados: {df_clear.furto_veiculos.mean():.2f} veículos')\n",
        "print(f'Média de Veículos Recuperados: {df_clear.recuperacao_veiculos.mean():.2f} veículos')\n",
        "\n"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Média de Veículos Roubados: 2448.70 veículos\n",
            "Média de Veículos Furtados: 1674.67 veículos\n",
            "Média de Veículos Recuperados: 1772.24 veículos\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oH0Pqxe5QvOv",
        "colab_type": "text"
      },
      "source": [
        "Calculando a porcentagem de veículos recuperados em relação a porcentagem de veículos roubados mais veículos furtados utilizando o número total de cada coluna.\n",
        "* $\\frac{\\text{veiculos recuperados}}{\\text{veiculos roubados} + \\text{veiculos furtados}}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_Rj7D8WPG_b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8694469f-56c5-4f1a-e43e-858956a9ef3a"
      },
      "source": [
        "# visualizando a porcentagem de veículos recuperados em relação a quantidade veículos roubados e furtados.\n",
        "roubo = df_clear.roubo_veiculo.sum()\n",
        "furto = df_clear.furto_veiculos.sum()\n",
        "recuperados = df_clear.recuperacao_veiculos.sum()\n",
        "\n",
        "print('A porcentegem de veículos recuperados em relação a \\nquantidade de veículos roubados '\n",
        "f'e furtados e de: {recuperados / (roubo + furto):.2f}%')"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A porcentegem de veículos recuperados em relação a \n",
            "quantidade de veículos roubados e furtados e de: 0.43%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsVrf3KDS-3R",
        "colab_type": "text"
      },
      "source": [
        "Em todo o período analisado foram recuperados 43% do total dos veículos roubados e furtados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvvZwyQAUE8Z",
        "colab_type": "text"
      },
      "source": [
        "Analisando o maior e menor número de homicídios dolosos cometidos em todo o período."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNWcxGuqUQZI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "f977081e-6e32-4ff7-c318-f67e9d277c5f"
      },
      "source": [
        "# visualizando o maior e o menor numero de homicídios dolosos\n",
        "print('Maior número e menor número de Homicídios Dolosos registrados no período presentes no DataFrame:')\n",
        "print(f'O maior número e: {df_clear.hom_doloso.max()} ')\n",
        "print(f'O menor número de homicídios dolosos e: {df_clear.hom_doloso.min()}')"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maior número e menor número de Homicídios Dolosos registrados no período presentes no DataFrame:\n",
            "O maior número e: 831 \n",
            "O menor número de homicídios dolosos e: 272\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WK0NBQZASKC",
        "colab_type": "text"
      },
      "source": [
        "## Visualização de Dados\n",
        "\n",
        "Iremos análisar os graficos:\n",
        "* Índice de **Homicídios Dolosos**\n",
        "* Índice de **Roubo em Coletivos**\n",
        "* Índices de **Roubo, Furto e Recupeção de Veículos**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vk-QBHFxHMDn",
        "colab_type": "text"
      },
      "source": [
        "Juntando os dados da coluna **vano** a **mes** para melhorar a precisão da análise."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLGjl7hNc7I9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "outputId": "5b431b28-2c9e-4eee-ba25-4b30c9f39415"
      },
      "source": [
        "# Criando um coluna data e juntando os dados da coluna ano e adicionando ao DataFrame\n",
        "df_clear['data'] = df.vano.astype(str) + \"/\" + df.mes.astype(str);"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSuZgcbQdd6A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "outputId": "418304e0-1a96-4d31-917c-953649c940ee"
      },
      "source": [
        "# modificando o tipo da coluna data para o formato date time\n",
        "df_clear.data = pd.to_datetime(df_clear.data, format='%Y-%m-%d');"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:5303: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5ebe_DMHjZE",
        "colab_type": "text"
      },
      "source": [
        "Criando um data frame com a somatória dos **homicídios dolosos** registrados em cada ano."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGOKsCzokRtP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "386bc644-b6dd-46ec-d4aa-7a6819c44eb9"
      },
      "source": [
        "# criando as listas para receber o ano e a somatória\n",
        "lista_ano = []\n",
        "lista_soma_ano = []\n",
        "# criando um validador\n",
        "num = 0\n",
        "# lendo a coluna vano separando o ano realizando a somatória e carregando a lista\n",
        "for c in df_clear.vano:\n",
        "  if c > num:\n",
        "    num = c\n",
        "    lista_ano.append(c)\n",
        "    lista_soma_ano.append(df.hom_doloso[df_clear.vano == c].sum())\n",
        "# visualizando os dados das listas\n",
        "print(lista_ano)\n",
        "print(lista_soma_ano)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]\n",
            "[7518, 7635, 7720, 8408, 8438, 7259, 6923, 5726, 5888, 6287, 6163, 6885, 6624, 6438, 6620, 6323, 6133, 5717, 5793, 4767, 4279, 4081, 4745, 4942, 4200, 5042, 5346, 4950, 2717]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Rh3s3QQ_6cr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2780a0cd-223a-4e9b-db49-a47b7ab0e344"
      },
      "source": [
        "# criando um dicionário para receber os dados das listas\n",
        "dic_som_ano = {}\n",
        "# carregando os dados no dicionário\n",
        "dic_som_ano['ano'] = lista_ano\n",
        "dic_som_ano['hom_doloso'] = lista_soma_ano\n",
        "# criando o DataFrame com o ano e a somatória, carregando os dados do dicionário\n",
        "df_som_hom_ano = pd.DataFrame(dic_som_ano)\n",
        "# visualizando as 5 primeiras linhas do DataFrame\n",
        "df_som_hom_ano.head()"
      ],
      "execution_count": 100,
      "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>ano</th>\n",
              "      <th>hom_doloso</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1991</td>\n",
              "      <td>7518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1992</td>\n",
              "      <td>7635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1993</td>\n",
              "      <td>7720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1994</td>\n",
              "      <td>8408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1995</td>\n",
              "      <td>8438</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    ano  hom_doloso\n",
              "0  1991        7518\n",
              "1  1992        7635\n",
              "2  1993        7720\n",
              "3  1994        8408\n",
              "4  1995        8438"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X9DX-GVMUs78",
        "colab_type": "text"
      },
      "source": [
        "Plontando um histograma para análise dos **Homicídios Dolosos**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PIUjON6VGVA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "5438c66e-0ec3-47d6-b029-364523cffca7"
      },
      "source": [
        "# visualizando o histograma da coluna de homicídios dolosos\n",
        "fig, (axis1) = plt.subplots(figsize=(20,5))\n",
        "#Plotando o histograma\n",
        "sns.barplot(x='ano', y='hom_doloso', data=df_som_hom_ano, ax=axis1);"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8Mcnx4+MLSV67iFAAAAAArC87fXe5JPfr7ifPzL+wqi6cdyAAAAAA1p+1nMn0b1X1yO0zVfWIJP82/0gAAAAArDdrOZPpZ5OcOY7DVEmuT/KsRYQCAAAAYH3Z6SJTd1+Y5MFVdbdx/gsLSwUAAADAunKrRaaq+pUdtCdJuvsVc84EAAAAwDqzM2cy3XX8+YAkP5DknHH+R5N8cBGhAAAAAFhfbrXI1N0vTJKq+uskh3X3F8f530zyzoWmAwAAAGBdWMvd5fZN8rWZ+a+NbQAAAADczq3l7nKvS/LBqnrrOH9ckj+ZeyIAAAAA1p213F3uJVX1l0l+cGx6dnd/ZPvyqtqnu2+Yd0AAAAAAVt9azmRKd384yYd3sPi8JIdNTgQAADAn/+2t1y5t37/6Y9+xtH0DLMNaxmS6NTXHvgAAAABYR9Z0JtOt6Dn2BQAAAOvCJ37/M0vZ7/1+wb24WC3zPJMJAAAAgNup3XK5XFXtUVUfqap3jPMHV9UHqmprVb2hqvYc2+80zm8dlx8008cLxvbLqupxc8wNAAAAwERrKjJV1T5V9aCqOmz7Y2bxkd9i0+cmuXRm/mVJXtnd909yQ5ITx/YTk9wwtr9yXC9V9cAkJyT53iRHJ3lNVe2xluwAAAAALM5Oj8lUVS9K8qwkn8hN4y91ksckSXdfv4PtDkjyhCQvSfIrVVXjNj85rnJmkt9McmqSY8fpJHlTkj8Y1z82yVnd/dUkn6yqrUkOT/IPO5sfAABgVZz15n9Z2r5PePK9lrZv4LZtLQN/H5/kft39tTXu4/eS/FqSu47z90zyue6+cZy/Ksn+4/T+Sa5Mku6+sao+P66/f5LzZ/qc3QYAAACAJVvL5XIXJ9l7LZ1X1ROTXNfdF6wp1S6qqpOqaktVbdm2bdvu2CUAAAAAWduZTL+d5CNVdXGSr25v7O4nfYttHpHkSVV1TJI7J7lbklcl2buqNoxnMx2Q5Opx/auTHJjkqqrakOTuST47077d7Db/obtPS3JakmzatKlvvhwAAACAxVhLkenMDANxfyzJ/96ZDbr7BUlekCRV9egkz+vun6qqNyZ5SpKzkmxO8rZxk3PG+X8Yl7+3u7uqzkny51X1iiT3SXJIkg+uITsAAAAAC7SWItOXu/vVc9rvryc5q6penOQjSU4f209P8qfjwN7XZ7ijXLr7kqo6O8nHk9yY5OTu/vqcsgAAAAAw0VqKTH9TVb+d4Wyj2cvlPrwzG3f3+5O8f5y+IsPd4W6+zleSPHUH278kwx3qAAAAAFgxaykyPWT8ecRMWyd5zPziAAAAALAe7XSRqbt/eJFBAAAAAFi/7rCzK1bV3avqFVW1ZXz8blXdfZHhAAAAAFgfdrrIlOSMJF9Mcvz4+EKS1y4iFAAAAADry1rGZLpfdz95Zv6FVXXhvAMBAAAAsP6s5Uymf6uqR26fqapHJPm3+UcCAAAAYL1Zy5lMP5vkzJlxmG5Isnn+kQAAAABYb9ZSZLo0ycuT3C/J3kk+n+S4JBctIBcAAAAA68haikxvS/K5JB9OcvVi4gAAAACwHq2lyHRAdx+9sCQAAAAArFtrGfj776vq+xeWBAAAAIB161bPZKqqjyXpcd1nV9UVSb6apJJ0dz9osREBAAAAWHU7c7ncExeeAgAAAIB17VaLTN396d0RBAAAAID1ay1jMgEAAADALVJkAgAAAGAyRSYAAAAAJlNkAgAAAGAyRSYAAAAAJlNkAgAAAGAyRSYAAAAAJlNkAgAAAGCyhRaZqurOVfXBqvpoVV1SVS8c2w+uqg9U1daqekNV7Tm232mc3zouP2imrxeM7ZdV1eMWmRsAAACAtVn0mUxfTfKY7n5wkkOTHF1VRyR5WZJXdvf9k9yQ5MRx/ROT3DC2v3JcL1X1wCQnJPneJEcneU1V7bHg7AAAAADspIUWmXrwpXH2juOjkzwmyZvG9jOTHDdOHzvOZ1x+ZFXV2H5Wd3+1uz+ZZGuSwxeZHQAAAICdt/Axmapqj6q6MMl1Sc5N8okkn+vuG8dVrkqy/zi9f5Irk2Rc/vkk95xtv4VtAAAAAFiyhReZuvvr3X1okgMynH30PYvaV1WdVFVbqmrLtm3bFrUbAAAAAG5mt91drrs/l+R9SR6eZO+q2jAuOiDJ1eP01UkOTJJx+d2TfHa2/Ra2md3Had29qbs3bdy4cSH/DgAAAAC+2aLvLrexqvYep78tyWOTXJqh2PSUcbXNSd42Tp8zzmdc/t7u7rH9hPHucwcnOSTJBxeZHQAAAICdt+HWV5lkvyRnjneCu0OSs7v7HVX18SRnVdWLk3wkyenj+qcn+dOq2prk+gx3lEt3X1JVZyf5eJIbk5zc3V9fcHYAAAAAdtJCi0zdfVGSh9xC+xW5hbvDdfdXkjx1B329JMlL5p0RAAAAgOl225hMAAAAANx2KTIBAAAAMJkiEwAAAACTLXrgbwAAAAAmuu4P37GU/d775Cfu9LrOZAIAAABgMkUmAAAAACZTZAIAAABgMkUmAAAAACZTZAIAAABgMkUmAAAAACZTZAIAAABgMkUmAAAAACZTZAIAAABgMkUmAAAAACZTZAIAAABgMkUmAAAAACZTZAIAAABgMkUmAAAAACZTZAIAAABgMkUmAAAAACZTZAIAAABgMkUmAAAAACZbaJGpqg6sqvdV1cer6pKqeu7Yfo+qOreqLh9/7jO2V1W9uqq2VtVFVXXYTF+bx/Uvr6rNi8wNAAAAwNos+kymG5P8n939wCRHJDm5qh6Y5PlJzuvuQ5KcN84nyeOTHDI+TkpyajIUpZKckuRhSQ5Pcsr2whQAAAAAy7fQIlN3X9PdHx6nv5jk0iT7Jzk2yZnjamcmOW6cPjbJ63pwfpK9q2q/JI9Lcm53X9/dNyQ5N8nRi8wOAAAAwM7bbWMyVdVBSR6S5ANJ9u3ua8ZF1ybZd5zeP8mVM5tdNbbtqP3m+zipqrZU1ZZt27bNNT8AAAAAO7ZbikxV9e1J3pzkl7r7C7PLuruT9Dz2092ndfem7t60cePGeXQJAAAAwE5YeJGpqu6YocD0Z939lrH5M+NlcBl/Xje2X53kwJnNDxjbdtQOAAAAwApY9N3lKsnpSS7t7lfMLDonyfY7xG1O8raZ9meOd5k7Isnnx8vq3pPkqKraZxzw+6ixDQAAAIAVsGHB/T8iyTOSfKyqLhzb/q8kL01ydlWdmOTTSY4fl70ryTFJtib5cpJnJ0l3X19VL0ryoXG93+ru6xecHQAAAICdtNAiU3f/bZLaweIjb2H9TnLyDvo6I8kZ80sHAAAAwLzstrvLAQAAAHDbpcgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMttAiU1WdUVXXVdXFM233qKpzq+ry8ec+Y3tV1auramtVXVRVh81ss3lc//Kq2rzIzAAAAACs3aLPZPqTJEffrO35Sc7r7kOSnDfOJ8njkxwyPk5KcmoyFKWSnJLkYUkOT3LK9sIUAAAAAKthoUWm7v7rJNffrPnYJGeO02cmOW6m/XU9OD/J3lW1X5LHJTm3u6/v7huSnJtvLlwBAAAAsETLGJNp3+6+Zpy+Nsm+4/T+Sa6cWe+qsW1H7QAAAACsiKUO/N3dnaTn1V9VnVRVW6pqy7Zt2+bVLQAAAAC3YhlFps+Ml8Fl/Hnd2H51kgNn1jtgbNtR+zfp7tO6e1N3b9q4cePcgwMAAABwy5ZRZDonyfY7xG1O8raZ9meOd5k7Isnnx8vq3pPkqKraZxzw+6ixDQAAAIAVsWGRnVfV65M8Osm9quqqDHeJe2mSs6vqxCSfTnL8uPq7khyTZGuSLyd5dpJ09/VV9aIkHxrX+63uvvlg4gAAAAAs0UKLTN39tB0sOvIW1u0kJ++gnzOSnDHHaAAAAADM0VIH/gYAAADgtkGRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJFJkAAAAAmEyRCQAAAIDJ1lWRqaqOrqrLqmprVT1/2XkAAAAAGKybIlNV7ZHkD5M8PskDkzytqh643FQAAAAAJOuoyJTk8CRbu/uK7v5akrOSHLvkTAAAAABkfRWZ9k9y5cz8VWMbAAAAAEtW3b3sDDulqp6S5Oju/ulx/hlJHtbdPz+zzklJThpnH5Dksjnt/l5J/mVOfc3bqmZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mZb1VzJ6mabZ677dvfGW1qwYU472B2uTnLgzPwBY9t/6O7Tkpw27x1X1Zbu3jTvfudhVbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbOtaq5kdbPtrlzr6XK5DyU5pKoOrqo9k5yQ5JwlZwIAAAAg6+hMpu6+sap+Psl7kuyR5IzuvmTJsQAAAADIOioyJUl3vyvJu5aw67lfgjdHq5ptVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttVXMlq5ttt+RaNwN/AwAAALC61tOYTAAAAACsqNttkamqzqiq66rq4pm2B1fVP1TVx6rq7VV1t7F9z6p67dj+0ap69Mw2L6mqK6vqS6uSq6ruUlXvrKp/rKpLquqlq5JtXPbuse2SqvqjqtpjFXLNbHvObF+rkK2q3l9Vl1XVhePj3iuSa8+qOq2q/ml8vT15Sq55Zauqu848VxdW1b9U1e8tO9e47Glj+0Xj78K9puSac7afGHNdUlUvm0OuA6vqfVX18bHP547t96iqc6vq8vHnPmN7VdWrq2rrmOOwmb42j+tfXlWbVyjXu6vqc1X1jimZ5p2tqg4d//8vGdt/YkVy3beqPjz+Xl5SVT8zJdc8s830d7equqqq/mBVclXV1+um97PJNz2Zc7bvrKq/qqpLx/4OWnauqvrh+sZjwFeq6rhdf8bm/py9fOzj0nGdWpFcL6uqi8fHpPeMXcz2PTW8b321qp53s76OruFz0Naqev4K5fqm4+8qZNtRPyuQ685V9cG66XvAC6fkmme2mf72qKqP1MTj+pxfZ5+q4XPbhVW1ZUquBWTbu6reVMP3gEur6uHLzlVVD6hvPAZ8oap+aVdzzTPbuOyXxz4urqrXV9WdVyTXc8dMl0x9vtLdt8tHkkclOSzJxTNtH0ryQ+P0c5K8aJw+Oclrx+l7J7kgyR3G+SOS7JfkS6uSK8ldkvzw2L5nkr9J8vhVyDbO3238WUnenOSEVcg1tv14kj+f7WsVsiV5f5JNK/j6f2GSF4/Td0hyr1XJdrM+L0jyqGXnyjAO3nXbn6ckL0/ym6vwnCW5Z5J/TrJxXHZmkiMn5tovyWHj9F2T/FOSB47/7ueP7c9P8rJx+pgkf5nhveGIJB8Y2++R5Irx5z7j9D7LzjUuOzLJjyZ5x9T/xzk/Z9+d5JBx+j5Jrkmy9wrk2jPJncbpb0/yqST3WYXnbKa/V2U4DvzBquTKnD5jLCjb+5M8dub/9C6rkGumz3skuX5Krjn/DvznJH+X4SY2eyT5hySPXoFcT0hybobj1F4ZjiF3283P2b2T/ECSlyR53kw/eyT5RJLvyvAe8tEkD1x2rnHZNx1/V+Q5u8V+ViBXJfn2cfqOST6Q5IhVeM5m+vuVDMeAScf1Ob/OPpU5fMZeULYzk/z0OL1ndu9njW/5fzmus0eSa63sgVoAAAtvSURBVJPcdxWesyT7J/lkkm8b589O8qwVyPV9SS7OUEfYkOT/S3L/Xc41rxfrenwkOSjf+KXs88l/jFN1YJKPj9N/mOQZM+udl+Twm/U1tw+A88w1tr8qyX9ZtWwZDi5vT/ITq5Arw4fjvx1/MefyYWGO2d6fORaZ5pjryiR7zTPXAl5n3z3mrGXnGl/z25LcN8MHrT9KctIqPGcZDjjnzbQ/I8lr5vz/+rYkj01yWZL9xrb9klw2Tv/3JE+bWf+ycfnTkvz3mfZvWG9ZuWbmH505FZnmnW2m/aMZi06rkis3FTYnFZnmmS3JQ5OcleRZmVhkmnOuuRaZ5pUtw/Hyb1ct1836OCnJn61KtiQPz1Dc/7YMH+a3JPlPK5DrV5P8xkz76UmO353P2cx6v5lv/PLz8CTvmZl/QZIXLDvXTPtBmePnxnlmu3k/q5RrfP1/OMnDVuU5S3JAhs9Fj8mcj+sTc30qcywyzStbkrtnKJhM/oy9wNfZUUn+blWyZSgyXZnhDyAbkrwjyVErkOupSU6fmf+NJL+2qzlut5fL7cAlSY4dp5+a4YtZMnw4f1JVbaiqgzN8CD3wFrZfuVxVtXeGv7Kft0rZquo9Gc7o+GKSN61Irhcl+d0kX15AnqnZkuS14ymfv1G166fWzyvX+NpKkhfVcPnLG6tq3wXkWnO2m217QpI39PiOucxc3f3vSX42yceS/K8MX9BOX0CuNWdLsjXJA6rqoKrakOS4zPF9roZLaB6S4a+W+3b3NeOia5Nsf91sP/Bud9XYtqP2ZedaqHllq6rDM/x18ROrkGs8tfuicfnLuvt/zSPX1GxVdYcMx4BvunximbnG6TtX1ZaqOr8mXvY152zfneRzVfWWGi4v+W818RL4OeWadUKS188j0zyydfc/JHlfhrMLr8lQPLl02bkyHBuOrmHIhXsl+eHs/mPAjiz7GLAU88p2s36WnquGy9EuzPA94NzunkuueWRL8ntJfi3J/55Xpjnl6iR/VVUXVNVJK5Tt4Ax/PH3teAz446raawVyzVrWMeAWdffVSX4nwx/Zrkny+e7+q2XnynAW0w9W1T2r6i4Zznrd5WOAItM3ek6Sn6uqCzKcbva1sf2MDAe0LRnefP4+yddXPdf4RfH1SV7d3VesUrbuflyGquqdMvy1YKm5qurQJPfr7rcuIMukbOOyn+ru70/yg+PjGSuQa0OGv/j8fXcfluGU/99ZQK5dyTZr7geXXc1VVXfMUGR6SIZLmC7K8BfZpWfr7hvGbG/IcIntpzKn97mq+vYMl8b+Und/YXbZWPxbRAFw3eZK5petqvZL8qdJnt3dkz80zyNXd1/Z3Q9Kcv8km+dVnJ5Dtp9L8q7uvmoeeeaYKxlO89+U5CeT/F5V3W9Fsm3IcEx6XoazIb8rw1lgy861vZ/9knx/kvdMzTSvbFV1/yT/KcPxc/8kj6mqH1x2rvFLzrsyHBNen+GY7hiwJHP8HdhhP8vK1d1f7+5DM/wOHF5V3zc11zyyVdUTk1zX3RfMI8+8co0eOX7WfnySk6vqUSuSbUOGy0VP7e6HJPnXDJdmLTvX9n72TPKkJG+cmmle2caxkY7NUKC7T5K9qurpy841/rHjZUn+Ksm7k1yYCccARaYZ3f2P3X1Udz80wwH2E2P7jd39y919aHcfm2TvDNc7rnqu05Jc3t2TBjxeULZ091cynNJ37M37XUKuhyfZVFWfynDJ3HdX1fvnnWsXs22veqe7v5jhOvHDVyDXZzOc9fWWsYs3ZjjQzN2uvs6q6sFJNsz7A8OEXIeOyz8xvuGfnWF8jlXIlu5+e3c/rLsfnuE028nvc2Nh7c0ZLlfZ/lr5zPjlb/uXwOvG9qvzjX81OWBs21H7snMtxLyy1TDY+zuT/N/dff6q5NquhzOYLs5QpFiFbA9P8vPjceB3kjyzJt44Y17P2cwx4IoMl08/ZEquOWa7KsmF3X1Fd9+Y5C8y8Tgw59fZ8Une2sNZpJPNKduPJTm/u7/U3V/KMD7SLg+UO8dc6e6XjMeGx2a4pHt3HwN2ZNnHgN1qXtl20M/Sc23X3Z/LcFbf0SuS7REZzvT+VIbLph9TVf9jBXLNHgOuS/LWzOF7wJyyXZXkqr7pbLQ3ZfceA27N45N8uLs/MyXTnLP9SJJPdve28dj0lkz8LjDH19np3f3Q7n5Ukhsy4RigyDSjxjt21XDK/H/NMF5Kajh1eK9x+rFJbuzuj69yrqp6cYbrZKeNDD/nbFX17TMv+A0ZBpr8x2Xn6u5Tu/s+3X1Qkkcm+afufvS8c+1Kthoua7rX2H7HJE/M8MVsqbnGIsnbM4xHkwwDIC/k92LC7+bTsrizmHYl19VJHlhVG8cuHptkLpdJzCHb7Db7ZDir448nZqgMlwNe2t2vmFl0TpLN4/TmDMXm7e3PrMERGU4hvibDWQhHVdU+Y7ajMuHMhDnmmrt5Zavhr3dvTfK67p58SfIccx1QVd829rlPhvfby1YhW3f/VHd/53gceF6G526X/yI7x+dsn6q609jnvTJ8EZr0XjvH34EPJdl75j3tMVOyLeB3c27HgDlm++ckPzQe2++Y5Icy4Tgwx9fZHlV1z7HPByV5UIa/aO+yXci2Ix9KckhVHTy+t50w9rHsXHM3r2zfop9l59pY43AL47HgsZn4PWBe2br7Bd19wHgMOCHJe7t7l88wmeNztldV3XX7dIbPQJO+B8zxObs2yZVV9YCxadJ3gQX8bi7zGLAj/5zkiPHzeGV4znbnMeBb9bX9e8B35qabYe2aXsAgXevhkeEFd02Sf89QhT0xyXMzVOz+KclLk/8YNPegDB+CL80w0vp9Z/p5+bj9/x5//uayc2X4C0+P7ReOj59ehecsw/WgH8pwmdDFSX4/w5kmS/+/nOnvoMzvLiHzeM72yjBI6EUZxtl5VZI9lp1rXHbfJH89ZjsvyXeuwnM209cVSb5nVf4vx2U/M7ZflKFId88Vyvb6DB8OPp6Jd30c+3tkhveii3LTe9ExGQZ8Pi/J5WOGe4zrV4aByT+RYdyqTTN9PSfDuFFbM1z6tSq5/ibDeAT/Nj73j1uFbEmePr4eLpx5HLoCuR479vHR8efkge/n+f850+ezMv3ucvN6zv7zOP/R8eeJq/SczfyffizJnyTZc0VyHZShsP9Ndxxd8v/nHhkG3740w3vtK1Yk151z0/v/+ZnwfjEh23dkeB/9QpLPjdPb70h8TIbj2ScynJ25Krm+6fi7Ctl21M8K5HpQko/kpu8B/88qvc5m+nx0pt9dbl7P2XdleP//aIbvAZNe/wv4HTg0wxAMF2U4m3XK3X/nmWuvDFdd3H3q87WAbC/MUFy9OMOQBndakVx/k+EY8NFMvMP09i8dAAAAALDLXC4HAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAAABMpsgEAAAAwGSKTAAAu0FV/UVVXVBVl1TVSWPbl6rqJVX10ao6v6r2HdsPqqr3VtVFVXVeVX3nctMDANw6RSYAgN3jOd390CSbkvxiVd0zyV5Jzu/uByf56yT/ZVz395Oc2d0PSvJnSV69jMAAAGuhyAQAsHv8YlV9NMn5SQ5MckiSryV5x7j8giQHjdMPT/Ln4/SfJnnk7osJALBrNiw7AADAbV1VPTrJjyR5eHd/uaren+TOSf69u3tc7evx2QwAWMecyQQAsHh3T3LDWGD6niRH3Mr6f5/khHH6p5L8zSLDAQDMgyITAMDivTvJhqq6NMlLM1wy9638QpJnV9VFSZ6R5LkLzgcAMFnddIY2AAAAAOwaZzIBAAAAMJkiEwAAAACTKTIBAAAAMJkiEwAAAACTKTIBAAAAMJkiEwAAAACTKTIBAAAAMJkiEwAAAACT/f/57kHm1b2XZgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMfS5MCa8eu2",
        "colab_type": "text"
      },
      "source": [
        "Plotando um gráfico de linhas para análise do índice de **Roubos em Coletivos**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMBk4Fb5keKV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "907f892e-072c-47cc-ee7a-f5839aa9c5bb"
      },
      "source": [
        "# carregando as configuração para plotar o gráfico.\n",
        "fig, (axis1) = plt.subplots(figsize=(20,5))\n",
        "# Plotando o gráfico de linhas\n",
        "sns.lineplot(x='data', y='roubo_em_coletivo', data=df_clear, ax=axis1);"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8cM5PU4XsvP2",
        "colab_type": "text"
      },
      "source": [
        "Nota-se um crescimento no índice de assaltos realizados em coletivos comparando o início e o fim do gráfico no qual o mesmo tem uma leve diminuição no final."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iqt2nWkw9iv0",
        "colab_type": "text"
      },
      "source": [
        "Plotando um gráfico de linhas para análise do índice de **Roubo, Furto e Recuperação de Veículos Roubados**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PU_Gb04M90gJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "d2cfd3b0-5bc8-44c7-802b-291081edced1"
      },
      "source": [
        "# plotando o gráfico de linhas para analise e comparação dos índices\n",
        "df_clear[['roubo_veiculo', 'furto_veiculos', 'recuperacao_veiculos']].plot.line(figsize=(20,5));"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzesuLcHJbqY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a6f24fd8-8720-4352-9ca5-e6212ec34810"
      },
      "source": [
        "# carregando as configuração para plotar o grafico.\n",
        "fig, (axis1, axis2, axis3) = plt.subplots(1,3, figsize=(20,5))\n",
        "# plotando um grafico de linhas separadamente para cada índice.\n",
        "sns.lineplot(x='data', y='roubo_veiculo', data=df_clear, ax=axis1)\n",
        "sns.lineplot(x='data', y='furto_veiculos', data=df_clear, ax=axis2)\n",
        "sns.lineplot(x='data', y='recuperacao_veiculos', data=df_clear, ax=axis3);"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCsiXvM3BcsO",
        "colab_type": "text"
      },
      "source": [
        "## Conclusão\n",
        "O número de homicídios dolosos registrados no ano de 2019 são os menores comparados com os anos anteriores chegando a máxima de 831 e a mínima de 272 registros mês em todo o período de 1991 a 2019, chegando a registrar 7518 casos no ano de 1991 sofrendo uma queda considerável em 2018  registrando 4950 casos caindo ainda mais em 2019 chegando a 2717 casos registrados de Janeiro a Agosto.\n",
        "\n",
        "O número de roubos em coletivos sofreu um crescimento exponencial desde 1991 a 2018, no ano de 2019 sofreu uma leve queda.\n",
        "\n",
        "O roubo de veículos teve um crescimento de 1991 até o ano de 2018 vindo a sofre um leve queda no ano de 2019, o furto de veículos veio sofrendo uma diminuição desde 1991 a 2019, já a recupeção do veículos roubados e furtados cresceu desde 1991 a 2018 sefrendo um leve queda em 2019, neste período foram recuperados o total de 43% do total de veículos roubados e furtados."
      ]
    }
  ]
}