Analisando_a_Violência_no_Rio_de_Janeiro.ipynb

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    "colab": {
      "name": "Analisando a Violência no Rio de Janeiro.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/JoaoGustavo29/Portfolio_DataScience/blob/master/Analisando_a_Viol%C3%AAncia_no_Rio_de_Janeiro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eFQZydM9O9J"
      },
      "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 [sigmoidal.ai](https://sigmoidal.ai)*\n",
        "\n",
        "---\n",
        "\n",
        "# Analisando a Violência no Rio de Janeiro\n",
        "\n",
        "<center><img alt=\"Dublin\" width=\"75%\" src=\"https://raw.githubusercontent.com/JoaoGustavo29/Relatorios_imgs/main/Camada%201%20(3).png\"></center>\n",
        "\n",
        "Sabe-se que o Rio de Janeiro é conhecido como a **\"Cidade Maravilhosa\"**, sendo conhecida por ser um dos maiores pontos turisticos do País. \n",
        "\n",
        "Apesar das maravilhas do Rio De janeiro, o estado tem altos indices de **violência e tráfico**, problemas os quais a polícia tem dificuldade em vencer. Segundo o resumo [Violência no Rio de Janeiro - Desafio do Estado é vencer o tráfico](https://vestibular.uol.com.br/resumo-das-disciplinas/atualidades/violencia-no-rio-de-janeiro-desafio-do-estado-e-vencer-o-trafico.htm), o tráfico por si só não justifica o alto índice de criminalidade. \n",
        "\n",
        "Praticamente todas as grandes metrópoles do mundo possuem comércio ilegal de drogas. Estima-se que o mercado consumidor de cocaína em Nova York, por exemplo, seja **duas vezes maior** que o Rio.\n",
        "\n",
        "Entretanto, o Rio é uma das poucas cidades do mundo que presencia cenas de guerra nas ruas quase que diariamente. O motivo são as facções armadas que travam lutas pelo controle de territórios, favorecidas por uma rede de corrupção e pelo descaso histórico do poder público em relação às favelas nos morros cariocas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLI0gJtzDGuQ"
      },
      "source": [
        "# Rio de Janeiro \n",
        "\n",
        "O Rio de Janeiro é uma grande cidade brasileira à beira-mar, famosa pelas praias de **Copacabana** e **Ipanema**, pela estátua de 38 *metros* de altura do **Cristo Redentor**, no topo do Corcovado, e pelo Pão de Açúcar, um pico de granito com teleféricos até seu cume. A cidade também é conhecida pelo empolgante **Carnaval**, com carros alegóricos, fantasias extravagantes e sambistas, é considerado o maior do mundo. Tão marcantes quanto Cristo ou o Pão de Açúcar na paisagem carioca, as favelas se espalham pelos morros do Rio. Segundo dados do [Censo2010](https://censo2010.ibge.gov.br/), existem 160 bairros urbanizados e 763 favelas, nas quais vivem mais de 1 milhão e 300 mil pessoas, quase um quarto da população municipal.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zxQHhIUDF_2"
      },
      "source": [
        "## Obtenção dos Dados\n",
        "\n",
        "Os dados usados foram obtidos a partir do site do [ISP Dados.RJ](http://www.ispdados.rj.gov.br/estatistica.html) (Instituto de Segurança Pública do Rio de Janeiro).\n",
        "\n",
        "O *dataset* que será utilizado na análise é um arquivo`.csv`\n",
        "\n",
        "* `DOMensalEstadoDesde1991.csv` - *Estatísticas de segurança: série histórica mensal no estado desde 01/1991*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vh4at0l90nr"
      },
      "source": [
        "### Importando os dados\n",
        "\n",
        "Aqui iremos importar tanto os dados, assim como também as bibliotecas necessárias para a realização da análise."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYLNme_OCX_R"
      },
      "source": [
        "# importar pacotes nescessários\r\n",
        "import pandas as pd\r\n",
        "\r\n",
        "# importar arquivo DOMensalEstadoDesde1991.csv para um DataFrame\r\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/JoaoGustavo29/datasets/main/DOMensalEstadoDesde1991.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb5By2nIMS6m"
      },
      "source": [
        "## Análise Inicial dos Dados\n",
        "\n",
        "Aqui é onde buscamos passar o maior entendimento possível, assim o leitor pode se sentir a vontade quando estiver lendo e absorver com excelência as informações transmitidas.\n",
        "\n",
        "Um ótimo começo é checarmos como é o nosso *DataFrame*, sendo assim, vamos dar uma olhada em como estao distribuídos nossos dados.\n",
        "\n",
        "Com apenas algumas linhas de código foi possível notar que os dados estão distribuídos em:\n",
        "\n",
        "* 361 entradas \n",
        "* 56 variáveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WUFnuLdj6zZ",
        "outputId": "b3de7a9a-9355-4f0d-cd77-4fd2a67e2827"
      },
      "source": [
        "# Identificar volume de Dados no DataFrame\r\n",
        "print('Entradas:\\t{}'.format(df.shape[0]))\r\n",
        "print('Variáveis:\\t{}'.format(df.shape[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entradas:\t361\n",
            "Variáveis:\t56\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-E6WY0HYAh0"
      },
      "source": [
        "## Colunas\r\n",
        "\r\n",
        "Vamos dar uma olhada no nome de cada uma das variáveis e construir um dicionário a respeito das variáveis que podem causar confusão na hora de serem interpretadas para entendermos o DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7T4AltCj6sd",
        "outputId": "4bdeb06c-6f25-4070-f341-75f162d5faab"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ano', 'mes', 'hom_doloso', 'lesao_corp_morte', 'latrocinio', 'cvli',\n",
              "       'hom_por_interv_policial', 'letalidade_violenta', 'tentat_hom',\n",
              "       'lesao_corp_dolosa', 'estupro', 'hom_culposo', 'lesao_corp_culposa',\n",
              "       'roubo_transeunte', 'roubo_celular', 'roubo_em_coletivo', 'roubo_rua',\n",
              "       'roubo_veiculo', 'roubo_carga', 'roubo_comercio', 'roubo_residencia',\n",
              "       'roubo_banco', 'roubo_cx_eletronico', 'roubo_conducao_saque',\n",
              "       'roubo_apos_saque', 'roubo_bicicleta', 'outros_roubos', 'total_roubos',\n",
              "       'furto_veiculos', '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",
              "       'registro_ocorrencias', 'fase'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rgw4sl8PYaJl"
      },
      "source": [
        "**Dicionário de Variáveis**\r\n",
        "\r\n",
        "* `ano` - Ano da comunicação da ocorrência\r\n",
        "* `mes` - Mês da comunicação da ocorrência\r\n",
        "* `cvli` - Crimes Violentos Letais Intencionais (lesão corporal seguida de morte)\r\n",
        "* `apf` - Auto de Prisão em Flagrante \r\n",
        "* `aaapai` - Auto de apreensão de Adolescente por Prática de Ato Infracional\r\n",
        "* `cmp` - Cumprimento de Mandado de Prisão\r\n",
        "* `cmba` - Cumprimento de Mandado de Busca e Apreensão\r\n",
        "* `hom` - Homicídio e suas classificações\r\n",
        "\r\n",
        "As outras variáveis presentes no *Dataset* são autoexplicativas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Ux68onE30Zo"
      },
      "source": [
        "### Tipos de Dados\r\n",
        "\r\n",
        "Em seguida, vamos verificar os tipos de dados presentes em cada uma das variáveis presentes no nosso *DataFrame*\r\n",
        "\r\n",
        "Por termos apenas dados numéricos já era esperado que fossem dos tipos int e float."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-tUUtKK3m1C",
        "outputId": "55635ab6-4282-4b9c-9324-68970bd74941"
      },
      "source": [
        "# Checando os tipos de dados das variáveis\r\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ano                             int64\n",
              "mes                             int64\n",
              "hom_doloso                      int64\n",
              "lesao_corp_morte              float64\n",
              "latrocinio                      int64\n",
              "cvli                          float64\n",
              "hom_por_interv_policial       float64\n",
              "letalidade_violenta             int64\n",
              "tentat_hom                      int64\n",
              "lesao_corp_dolosa               int64\n",
              "estupro                       float64\n",
              "hom_culposo                   float64\n",
              "lesao_corp_culposa            float64\n",
              "roubo_transeunte                int64\n",
              "roubo_celular                 float64\n",
              "roubo_em_coletivo               int64\n",
              "roubo_rua                       int64\n",
              "roubo_veiculo                   int64\n",
              "roubo_carga                     int64\n",
              "roubo_comercio                  int64\n",
              "roubo_residencia                int64\n",
              "roubo_banco                     int64\n",
              "roubo_cx_eletronico           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",
              "registro_ocorrencias          float64\n",
              "fase                            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05sOiQ7l3eCH"
      },
      "source": [
        "**Primeiras Entradas**\r\n",
        "\r\n",
        "Em seguida iremos finalmente dar uma olhadas nas 5 primeiras entradas do nosso DataFrame. \r\n",
        "\r\n",
        "É possível notar a falta de valores em algumas colunas,  com esses valores ausentes a visualização e a interpretação dos dados fica comprometida, afinal a forma como seriam apresentadas não estariam de acordo com a realidade.\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "5k4ACN82S-el",
        "outputId": "b85256c2-8c79-4cf2-d2d3-b07834affa23"
      },
      "source": [
        "# Checando as 5 Primeiras Entradas\r\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>cvli</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>letalidade_violenta</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_transeunte</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_rua</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</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>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1991</td>\n",
              "      <td>1</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>15</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>672</td>\n",
              "      <td>162</td>\n",
              "      <td>3051</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>967</td>\n",
              "      <td>NaN</td>\n",
              "      <td>381</td>\n",
              "      <td>1348</td>\n",
              "      <td>1174</td>\n",
              "      <td>63</td>\n",
              "      <td>350</td>\n",
              "      <td>188</td>\n",
              "      <td>37</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",
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              "      <td>NaN</td>\n",
              "      <td>217</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>3</td>\n",
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              "      <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>NaN</td>\n",
              "      <td>749</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>963</td>\n",
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              "      <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",
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              "      <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>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>NaN</td>\n",
              "      <td>738</td>\n",
              "      <td>216</td>\n",
              "      <td>3613</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>963</td>\n",
              "      <td>NaN</td>\n",
              "      <td>422</td>\n",
              "      <td>1385</td>\n",
              "      <td>1265</td>\n",
              "      <td>71</td>\n",
              "      <td>443</td>\n",
              "      <td>191</td>\n",
              "      <td>43</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>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>NaN</td>\n",
              "      <td>654</td>\n",
              "      <td>200</td>\n",
              "      <td>3211</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1186</td>\n",
              "      <td>NaN</td>\n",
              "      <td>354</td>\n",
              "      <td>1540</td>\n",
              "      <td>1415</td>\n",
              "      <td>81</td>\n",
              "      <td>425</td>\n",
              "      <td>199</td>\n",
              "      <td>36</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>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>NaN</td>\n",
              "      <td>670</td>\n",
              "      <td>146</td>\n",
              "      <td>3051</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>931</td>\n",
              "      <td>NaN</td>\n",
              "      <td>335</td>\n",
              "      <td>1266</td>\n",
              "      <td>1449</td>\n",
              "      <td>82</td>\n",
              "      <td>553</td>\n",
              "      <td>231</td>\n",
              "      <td>27</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>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    ano  mes  hom_doloso  ...  pol_civis_mortos_serv  registro_ocorrencias  fase\n",
              "0  1991    1         657  ...                    NaN                   NaN     3\n",
              "1  1991    2         732  ...                    NaN                   NaN     3\n",
              "2  1991    3         713  ...                    NaN                   NaN     3\n",
              "3  1991    4         634  ...                    NaN                   NaN     3\n",
              "4  1991    5         650  ...                    NaN                   NaN     3\n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wP6w_n5a6cJ0"
      },
      "source": [
        "### Valores Ausentes\r\n",
        "\r\n",
        "Tendo em vista essa ausência de valores, vamos checar quais variáveis possuem valores ausentes e a porcentagem em cada uma delas. Podemos notar que `roubo_bicicleta` e `apf` possuem os maiores percentuais de valores ausentes. \r\n",
        "\r\n",
        "* `roubo_bicicleta ` -  possui cerca de 76% de seus valores ausente.\r\n",
        "*  `apf` - possui quase 50% de seus valores ausente.\r\n",
        "\r\n",
        "Não temos confirmação do motivo dos dados estarem ausentes. Podendo ser decorrente de um erro, falta de contabilidade ou outro motivo, apenas sabemos que apesar da falta de preenchimento o crime aconteceu."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGuvlXIJj6aV",
        "outputId": "e81363ff-8487-4693-c590-0b1735d523a6"
      },
      "source": [
        "(df.isnull().sum() / df.shape[0]).sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "furto_bicicleta               0.764543\n",
              "roubo_bicicleta               0.764543\n",
              "posse_drogas                  0.498615\n",
              "cmba                          0.498615\n",
              "cmp                           0.498615\n",
              "aaapai                        0.498615\n",
              "apf                           0.498615\n",
              "apreensao_drogas_sem_autor    0.498615\n",
              "trafico_drogas                0.498615\n",
              "roubo_cx_eletronico           0.398892\n",
              "roubo_apos_saque              0.398892\n",
              "extorsao                      0.398892\n",
              "sequestro_relampago           0.398892\n",
              "estelionato                   0.398892\n",
              "roubo_conducao_saque          0.398892\n",
              "furto_celular                 0.398892\n",
              "furto_coletivo                0.398892\n",
              "estupro                       0.398892\n",
              "pol_militares_mortos_serv     0.398892\n",
              "pol_civis_mortos_serv         0.398892\n",
              "pessoas_desaparecidas         0.365651\n",
              "encontro_ossada               0.365651\n",
              "roubo_celular                 0.299169\n",
              "lesao_corp_culposa            0.265928\n",
              "registro_ocorrencias          0.265928\n",
              "sequestro                     0.265928\n",
              "cvli                          0.265928\n",
              "lesao_corp_morte              0.265928\n",
              "ameaca                        0.265928\n",
              "hom_por_interv_policial       0.232687\n",
              "hom_culposo                   0.232687\n",
              "letalidade_violenta           0.000000\n",
              "tentat_hom                    0.000000\n",
              "latrocinio                    0.000000\n",
              "roubo_em_coletivo             0.000000\n",
              "hom_doloso                    0.000000\n",
              "lesao_corp_dolosa             0.000000\n",
              "mes                           0.000000\n",
              "roubo_transeunte              0.000000\n",
              "fase                          0.000000\n",
              "total_roubos                  0.000000\n",
              "roubo_rua                     0.000000\n",
              "roubo_veiculo                 0.000000\n",
              "roubo_carga                   0.000000\n",
              "roubo_comercio                0.000000\n",
              "roubo_residencia              0.000000\n",
              "roubo_banco                   0.000000\n",
              "outros_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",
              "ano                           0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsrlEXsO8L-r"
      },
      "source": [
        "## **Cópia do DataFrame 2011-2021**\r\n",
        "\r\n",
        "Agora que temos as informações nescessárias a respeito dos valores faltantes, podemos pensar em analisar apenas os últimos 10 anos e comparar alguns pontos com os dados anteriores a esse período, a fim de obtermos uma análise, o mais condizente possível com a realidade atual.\r\n",
        "\r\n",
        "Para isso, vamos criar uma cópia do *DataFrame* `df_clean = df.copy()` e usaremos os dados referentes ao período **2011-2021** `df_clean = df.query('ano >= 2011')`, a respeito de 2021, temos apenas dados do mês de janeiro, devido ao ano não ter acabado.\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8eEemHeuLTPS"
      },
      "source": [
        "# Criando cópia do DataFrame\r\n",
        "df_clean = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtZZFetKSMTk"
      },
      "source": [
        "# Consultando dados referentes a 2011-2021\r\n",
        "df_clean = df.query('ano >= 2011')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DP2Vrix9wEr"
      },
      "source": [
        "**Verificando se Restou Dados Ausentes**\r\n",
        "\r\n",
        "Em seguida, vamos verificar novamente qual o percentual de valores ausentes em cada uma das colunas.\r\n",
        "\r\n",
        "É possível notar que a quantidade de dados ausentes diminuiu drasticamente, tendo apenas duas variáveis com valores ausentes.\r\n",
        "\r\n",
        "* `roubo_bicicleta e furto_bicicleta` - possuem aproximadamente 30% de seus valores ausente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NUWt395YSmOZ",
        "outputId": "e6421184-4c8b-49e2-b39d-b5f24093aef2"
      },
      "source": [
        "# Verificando porcentagem de Dados Ausentes\r\n",
        "df_clean.isnull().sum() / df_clean.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ano                           0.000000\n",
              "mes                           0.000000\n",
              "hom_doloso                    0.000000\n",
              "lesao_corp_morte              0.000000\n",
              "latrocinio                    0.000000\n",
              "cvli                          0.000000\n",
              "hom_por_interv_policial       0.000000\n",
              "letalidade_violenta           0.000000\n",
              "tentat_hom                    0.000000\n",
              "lesao_corp_dolosa             0.000000\n",
              "estupro                       0.000000\n",
              "hom_culposo                   0.000000\n",
              "lesao_corp_culposa            0.000000\n",
              "roubo_transeunte              0.000000\n",
              "roubo_celular                 0.000000\n",
              "roubo_em_coletivo             0.000000\n",
              "roubo_rua                     0.000000\n",
              "roubo_veiculo                 0.000000\n",
              "roubo_carga                   0.000000\n",
              "roubo_comercio                0.000000\n",
              "roubo_residencia              0.000000\n",
              "roubo_banco                   0.000000\n",
              "roubo_cx_eletronico           0.000000\n",
              "roubo_conducao_saque          0.000000\n",
              "roubo_apos_saque              0.000000\n",
              "roubo_bicicleta               0.297521\n",
              "outros_roubos                 0.000000\n",
              "total_roubos                  0.000000\n",
              "furto_veiculos                0.000000\n",
              "furto_transeunte              0.000000\n",
              "furto_coletivo                0.000000\n",
              "furto_celular                 0.000000\n",
              "furto_bicicleta               0.297521\n",
              "outros_furtos                 0.000000\n",
              "total_furtos                  0.000000\n",
              "sequestro                     0.000000\n",
              "extorsao                      0.000000\n",
              "sequestro_relampago           0.000000\n",
              "estelionato                   0.000000\n",
              "apreensao_drogas              0.000000\n",
              "posse_drogas                  0.000000\n",
              "trafico_drogas                0.000000\n",
              "apreensao_drogas_sem_autor    0.000000\n",
              "recuperacao_veiculos          0.000000\n",
              "apf                           0.000000\n",
              "aaapai                        0.000000\n",
              "cmp                           0.000000\n",
              "cmba                          0.000000\n",
              "ameaca                        0.000000\n",
              "pessoas_desaparecidas         0.000000\n",
              "encontro_cadaver              0.000000\n",
              "encontro_ossada               0.000000\n",
              "pol_militares_mortos_serv     0.000000\n",
              "pol_civis_mortos_serv         0.000000\n",
              "registro_ocorrencias          0.000000\n",
              "fase                          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuZSQNlr-VWn"
      },
      "source": [
        "Vamos checar como ficou o nosso *DataFrame* após buscarmos mais recentes.\r\n",
        "\r\n",
        "Com estes valores, é possível fazer uma análise relevante e relacionar com o que acontece hoje, podemos também ter uma visualização mais didática."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "O4b0G9fVeeUS",
        "outputId": "cda02c5e-b03f-41f0-e3fd-82ddba7b959d"
      },
      "source": [
        "# Checando as 5 primeiras entradas após tratamento dos dados\r\n",
        "df_clean.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>cvli</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>letalidade_violenta</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_transeunte</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_rua</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</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>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>425</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9</td>\n",
              "      <td>439.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>500</td>\n",
              "      <td>413</td>\n",
              "      <td>8272</td>\n",
              "      <td>396.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>3923.0</td>\n",
              "      <td>4771</td>\n",
              "      <td>505.0</td>\n",
              "      <td>616</td>\n",
              "      <td>5892</td>\n",
              "      <td>1567</td>\n",
              "      <td>231</td>\n",
              "      <td>409</td>\n",
              "      <td>92</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>865</td>\n",
              "      <td>9203</td>\n",
              "      <td>1423</td>\n",
              "      <td>2822</td>\n",
              "      <td>763.0</td>\n",
              "      <td>2037.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7969</td>\n",
              "      <td>15014</td>\n",
              "      <td>2.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2025.0</td>\n",
              "      <td>1068</td>\n",
              "      <td>428.0</td>\n",
              "      <td>527.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1375</td>\n",
              "      <td>1858.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>867.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>7403.0</td>\n",
              "      <td>488.0</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57852.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>2011</td>\n",
              "      <td>2</td>\n",
              "      <td>368</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7</td>\n",
              "      <td>377.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>416</td>\n",
              "      <td>379</td>\n",
              "      <td>7844</td>\n",
              "      <td>416.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>3727.0</td>\n",
              "      <td>4742</td>\n",
              "      <td>516.0</td>\n",
              "      <td>588</td>\n",
              "      <td>5846</td>\n",
              "      <td>1490</td>\n",
              "      <td>234</td>\n",
              "      <td>453</td>\n",
              "      <td>81</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>835</td>\n",
              "      <td>9137</td>\n",
              "      <td>1227</td>\n",
              "      <td>3130</td>\n",
              "      <td>851.0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7951</td>\n",
              "      <td>15181</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2121.0</td>\n",
              "      <td>1094</td>\n",
              "      <td>430.0</td>\n",
              "      <td>521.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1288</td>\n",
              "      <td>1874.0</td>\n",
              "      <td>405.0</td>\n",
              "      <td>853.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>7172.0</td>\n",
              "      <td>451.0</td>\n",
              "      <td>47</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57840.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>381</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9</td>\n",
              "      <td>396.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>464</td>\n",
              "      <td>395</td>\n",
              "      <td>7143</td>\n",
              "      <td>434.0</td>\n",
              "      <td>243.0</td>\n",
              "      <td>3716.0</td>\n",
              "      <td>5215</td>\n",
              "      <td>515.0</td>\n",
              "      <td>621</td>\n",
              "      <td>6351</td>\n",
              "      <td>1544</td>\n",
              "      <td>226</td>\n",
              "      <td>401</td>\n",
              "      <td>129</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>892</td>\n",
              "      <td>9716</td>\n",
              "      <td>1354</td>\n",
              "      <td>4622</td>\n",
              "      <td>860.0</td>\n",
              "      <td>2215.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8249</td>\n",
              "      <td>17300</td>\n",
              "      <td>0.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2267.0</td>\n",
              "      <td>1283</td>\n",
              "      <td>519.0</td>\n",
              "      <td>644.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1329</td>\n",
              "      <td>2434.0</td>\n",
              "      <td>501.0</td>\n",
              "      <td>1197.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>7072.0</td>\n",
              "      <td>521.0</td>\n",
              "      <td>37</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62560.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>2011</td>\n",
              "      <td>4</td>\n",
              "      <td>403</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6</td>\n",
              "      <td>416.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>484</td>\n",
              "      <td>387</td>\n",
              "      <td>7100</td>\n",
              "      <td>389.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3735.0</td>\n",
              "      <td>4687</td>\n",
              "      <td>495.0</td>\n",
              "      <td>598</td>\n",
              "      <td>5780</td>\n",
              "      <td>1491</td>\n",
              "      <td>239</td>\n",
              "      <td>414</td>\n",
              "      <td>105</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>909</td>\n",
              "      <td>9132</td>\n",
              "      <td>1293</td>\n",
              "      <td>2579</td>\n",
              "      <td>852.0</td>\n",
              "      <td>1933.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8132</td>\n",
              "      <td>14789</td>\n",
              "      <td>1.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2119.0</td>\n",
              "      <td>1370</td>\n",
              "      <td>561.0</td>\n",
              "      <td>692.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>1233</td>\n",
              "      <td>2432.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>1141.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>7313.0</td>\n",
              "      <td>469.0</td>\n",
              "      <td>60</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58691.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>2011</td>\n",
              "      <td>5</td>\n",
              "      <td>368</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10</td>\n",
              "      <td>381.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>455</td>\n",
              "      <td>353</td>\n",
              "      <td>6423</td>\n",
              "      <td>417.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>3796.0</td>\n",
              "      <td>4640</td>\n",
              "      <td>496.0</td>\n",
              "      <td>559</td>\n",
              "      <td>5695</td>\n",
              "      <td>1462</td>\n",
              "      <td>273</td>\n",
              "      <td>408</td>\n",
              "      <td>88</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>811</td>\n",
              "      <td>8923</td>\n",
              "      <td>1360</td>\n",
              "      <td>2689</td>\n",
              "      <td>939.0</td>\n",
              "      <td>2054.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8195</td>\n",
              "      <td>15237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2636.0</td>\n",
              "      <td>1409</td>\n",
              "      <td>521.0</td>\n",
              "      <td>745.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>1277</td>\n",
              "      <td>2370.0</td>\n",
              "      <td>556.0</td>\n",
              "      <td>1176.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>6859.0</td>\n",
              "      <td>452.0</td>\n",
              "      <td>46</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>59364.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ano  mes  hom_doloso  ...  pol_civis_mortos_serv  registro_ocorrencias  fase\n",
              "240  2011    1         425  ...                    0.0               57852.0     3\n",
              "241  2011    2         368  ...                    0.0               57840.0     3\n",
              "242  2011    3         381  ...                    2.0               62560.0     3\n",
              "243  2011    4         403  ...                    0.0               58691.0     3\n",
              "244  2011    5         368  ...                    0.0               59364.0     3\n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G6dnsNYm-41g"
      },
      "source": [
        "### Informações Estatísticas da Violência no Rio de Janeiro\n",
        "\n",
        "Para termos um resumo geral das informações estatísticas do nosso *dataset* vamos usar a função `df.describe()`, com ela temos a média de cada um das váriaveis, o desvio padrão, os valores máximos e mínimos, etc.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "alYb3PGezbqo",
        "outputId": "73a41deb-d4a3-48db-a265-e944f3953b02"
      },
      "source": [
        "df_clean.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>cvli</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>letalidade_violenta</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_transeunte</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_rua</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</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>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>121.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2015.545455</td>\n",
              "      <td>6.454545</td>\n",
              "      <td>375.917355</td>\n",
              "      <td>3.338843</td>\n",
              "      <td>12.892562</td>\n",
              "      <td>392.148760</td>\n",
              "      <td>77.479339</td>\n",
              "      <td>469.628099</td>\n",
              "      <td>451.884298</td>\n",
              "      <td>6118.404959</td>\n",
              "      <td>438.231405</td>\n",
              "      <td>172.809917</td>\n",
              "      <td>3011.694215</td>\n",
              "      <td>5848.033058</td>\n",
              "      <td>1240.685950</td>\n",
              "      <td>850.157025</td>\n",
              "      <td>7938.876033</td>\n",
              "      <td>2877.628099</td>\n",
              "      <td>544.115702</td>\n",
              "      <td>497.702479</td>\n",
              "      <td>101.173554</td>\n",
              "      <td>1.900826</td>\n",
              "      <td>3.661157</td>\n",
              "      <td>7.537190</td>\n",
              "      <td>106.355372</td>\n",
              "      <td>12.258824</td>\n",
              "      <td>1529.818182</td>\n",
              "      <td>13617.380165</td>\n",
              "      <td>1339.545455</td>\n",
              "      <td>2291.330579</td>\n",
              "      <td>759.404959</td>\n",
              "      <td>1433.338843</td>\n",
              "      <td>133.400000</td>\n",
              "      <td>7577.801653</td>\n",
              "      <td>13495.132231</td>\n",
              "      <td>0.487603</td>\n",
              "      <td>141.528926</td>\n",
              "      <td>9.752066</td>\n",
              "      <td>3009.677686</td>\n",
              "      <td>1863.867769</td>\n",
              "      <td>757.859504</td>\n",
              "      <td>953.495868</td>\n",
              "      <td>189.595041</td>\n",
              "      <td>2072.000000</td>\n",
              "      <td>2739.719008</td>\n",
              "      <td>670.644628</td>\n",
              "      <td>1412.958678</td>\n",
              "      <td>106.090909</td>\n",
              "      <td>5718.851240</td>\n",
              "      <td>443.396694</td>\n",
              "      <td>37.380165</td>\n",
              "      <td>3.082645</td>\n",
              "      <td>1.743802</td>\n",
              "      <td>0.247934</td>\n",
              "      <td>62249.578512</td>\n",
              "      <td>2.966942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.915476</td>\n",
              "      <td>3.488075</td>\n",
              "      <td>60.533130</td>\n",
              "      <td>2.010612</td>\n",
              "      <td>5.555480</td>\n",
              "      <td>64.349911</td>\n",
              "      <td>45.096212</td>\n",
              "      <td>79.465835</td>\n",
              "      <td>82.945885</td>\n",
              "      <td>1290.528410</td>\n",
              "      <td>56.062578</td>\n",
              "      <td>27.269432</td>\n",
              "      <td>919.691586</td>\n",
              "      <td>1605.089992</td>\n",
              "      <td>758.616713</td>\n",
              "      <td>386.482578</td>\n",
              "      <td>2536.404481</td>\n",
              "      <td>1021.637461</td>\n",
              "      <td>247.907718</td>\n",
              "      <td>132.408940</td>\n",
              "      <td>22.040371</td>\n",
              "      <td>1.434137</td>\n",
              "      <td>2.534935</td>\n",
              "      <td>3.775008</td>\n",
              "      <td>43.236724</td>\n",
              "      <td>8.361125</td>\n",
              "      <td>669.941129</td>\n",
              "      <td>4319.270417</td>\n",
              "      <td>136.714118</td>\n",
              "      <td>931.304769</td>\n",
              "      <td>200.726828</td>\n",
              "      <td>336.947240</td>\n",
              "      <td>78.179159</td>\n",
              "      <td>1082.986216</td>\n",
              "      <td>2364.348927</td>\n",
              "      <td>0.931984</td>\n",
              "      <td>30.599312</td>\n",
              "      <td>4.724548</td>\n",
              "      <td>623.798568</td>\n",
              "      <td>332.587355</td>\n",
              "      <td>176.312190</td>\n",
              "      <td>173.141518</td>\n",
              "      <td>38.775331</td>\n",
              "      <td>669.565307</td>\n",
              "      <td>336.007297</td>\n",
              "      <td>210.853459</td>\n",
              "      <td>312.845633</td>\n",
              "      <td>75.927378</td>\n",
              "      <td>1342.998366</td>\n",
              "      <td>87.197236</td>\n",
              "      <td>11.837269</td>\n",
              "      <td>2.253097</td>\n",
              "      <td>1.573366</td>\n",
              "      <td>0.520913</td>\n",
              "      <td>7403.395556</td>\n",
              "      <td>0.179531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2011.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>239.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>249.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>2429.000000</td>\n",
              "      <td>241.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>584.000000</td>\n",
              "      <td>2260.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>3298.000000</td>\n",
              "      <td>1439.000000</td>\n",
              "      <td>221.000000</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>782.000000</td>\n",
              "      <td>7315.000000</td>\n",
              "      <td>904.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>305.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3089.000000</td>\n",
              "      <td>5722.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>717.000000</td>\n",
              "      <td>1068.000000</td>\n",
              "      <td>372.000000</td>\n",
              "      <td>521.000000</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>991.000000</td>\n",
              "      <td>1858.000000</td>\n",
              "      <td>308.000000</td>\n",
              "      <td>544.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1409.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31231.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>331.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>343.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>414.000000</td>\n",
              "      <td>380.000000</td>\n",
              "      <td>5299.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>2350.000000</td>\n",
              "      <td>4562.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>537.000000</td>\n",
              "      <td>5720.000000</td>\n",
              "      <td>2026.000000</td>\n",
              "      <td>328.000000</td>\n",
              "      <td>408.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>989.000000</td>\n",
              "      <td>9714.000000</td>\n",
              "      <td>1266.000000</td>\n",
              "      <td>1597.000000</td>\n",
              "      <td>682.000000</td>\n",
              "      <td>1302.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>7184.000000</td>\n",
              "      <td>12475.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2673.000000</td>\n",
              "      <td>1706.000000</td>\n",
              "      <td>638.000000</td>\n",
              "      <td>842.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>1483.000000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>1233.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>4706.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>59844.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>374.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>468.000000</td>\n",
              "      <td>457.000000</td>\n",
              "      <td>6249.000000</td>\n",
              "      <td>433.000000</td>\n",
              "      <td>171.000000</td>\n",
              "      <td>3056.000000</td>\n",
              "      <td>5692.000000</td>\n",
              "      <td>1103.000000</td>\n",
              "      <td>662.000000</td>\n",
              "      <td>7677.000000</td>\n",
              "      <td>2644.000000</td>\n",
              "      <td>520.000000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1253.000000</td>\n",
              "      <td>12827.000000</td>\n",
              "      <td>1360.000000</td>\n",
              "      <td>2378.000000</td>\n",
              "      <td>829.000000</td>\n",
              "      <td>1436.000000</td>\n",
              "      <td>158.000000</td>\n",
              "      <td>7720.000000</td>\n",
              "      <td>13956.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2960.000000</td>\n",
              "      <td>1862.000000</td>\n",
              "      <td>735.000000</td>\n",
              "      <td>985.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1951.000000</td>\n",
              "      <td>2833.000000</td>\n",
              "      <td>617.000000</td>\n",
              "      <td>1425.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>5902.000000</td>\n",
              "      <td>451.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>63881.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>414.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>432.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>526.000000</td>\n",
              "      <td>518.000000</td>\n",
              "      <td>7100.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>3821.000000</td>\n",
              "      <td>7196.000000</td>\n",
              "      <td>2044.000000</td>\n",
              "      <td>1194.000000</td>\n",
              "      <td>10135.000000</td>\n",
              "      <td>3554.000000</td>\n",
              "      <td>710.000000</td>\n",
              "      <td>609.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>1971.000000</td>\n",
              "      <td>17217.000000</td>\n",
              "      <td>1426.000000</td>\n",
              "      <td>2771.000000</td>\n",
              "      <td>897.000000</td>\n",
              "      <td>1592.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>8249.000000</td>\n",
              "      <td>14789.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>3245.000000</td>\n",
              "      <td>2090.000000</td>\n",
              "      <td>869.000000</td>\n",
              "      <td>1077.000000</td>\n",
              "      <td>213.000000</td>\n",
              "      <td>2470.000000</td>\n",
              "      <td>2968.000000</td>\n",
              "      <td>842.000000</td>\n",
              "      <td>1635.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>6843.000000</td>\n",
              "      <td>501.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>67080.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2021.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>510.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>534.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>655.000000</td>\n",
              "      <td>645.000000</td>\n",
              "      <td>9050.000000</td>\n",
              "      <td>561.000000</td>\n",
              "      <td>245.000000</td>\n",
              "      <td>4313.000000</td>\n",
              "      <td>9669.000000</td>\n",
              "      <td>2626.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "      <td>13833.000000</td>\n",
              "      <td>5358.000000</td>\n",
              "      <td>1329.000000</td>\n",
              "      <td>774.000000</td>\n",
              "      <td>148.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>198.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>3661.000000</td>\n",
              "      <td>23203.000000</td>\n",
              "      <td>1640.000000</td>\n",
              "      <td>5368.000000</td>\n",
              "      <td>1036.000000</td>\n",
              "      <td>2270.000000</td>\n",
              "      <td>278.000000</td>\n",
              "      <td>9880.000000</td>\n",
              "      <td>18003.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>6058.000000</td>\n",
              "      <td>2543.000000</td>\n",
              "      <td>1160.000000</td>\n",
              "      <td>1282.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>3695.000000</td>\n",
              "      <td>3425.000000</td>\n",
              "      <td>1114.000000</td>\n",
              "      <td>2314.000000</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>9034.000000</td>\n",
              "      <td>628.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>75403.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               ano         mes  ...  registro_ocorrencias        fase\n",
              "count   121.000000  121.000000  ...            121.000000  121.000000\n",
              "mean   2015.545455    6.454545  ...          62249.578512    2.966942\n",
              "std       2.915476    3.488075  ...           7403.395556    0.179531\n",
              "min    2011.000000    1.000000  ...          31231.000000    2.000000\n",
              "25%    2013.000000    3.000000  ...          59844.000000    3.000000\n",
              "50%    2016.000000    6.000000  ...          63881.000000    3.000000\n",
              "75%    2018.000000    9.000000  ...          67080.000000    3.000000\n",
              "max    2021.000000   12.000000  ...          75403.000000    3.000000\n",
              "\n",
              "[8 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HudqBFdDwjN"
      },
      "source": [
        "**Média das variáveis `roubo_veiculo`, `furto_veiculos` e `recuperacao_veiculos`:**\r\n",
        "\r\n",
        "Podemos ver a média de cada uma das variáveis e termos alguns insights, vamos dar uma olhada:\r\n",
        "\r\n",
        "  * `roubo_veiculo` - Em média, aproximadamente 2878 veículos foram roubados\r\n",
        "\r\n",
        "  * `furto_veiculos` - Em média, aproximadamente 1340 veículos foram furtados\r\n",
        "\r\n",
        "  * `recuperacao_veiculos` - Em média, aproximadamente 2072 veículos foram recuperados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rptnCZw9zhy7",
        "outputId": "c0714334-4b33-48a3-b20d-89eae7ae153f"
      },
      "source": [
        "# Verificando a média de veículos roubados\r\n",
        "df_clean.roubo_veiculo.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2877.628099173554"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gw90c81Uzoy9",
        "outputId": "66c59d24-1409-41db-ab27-195d85790132"
      },
      "source": [
        "# Verificando a média de veículos furtados\r\n",
        "df_clean.furto_veiculos.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1339.5454545454545"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_nNoCWfazwF7",
        "outputId": "31af1350-9e0e-41be-bb65-75dd7e71b3e9"
      },
      "source": [
        "# Verificando a média de veículos recuperados\r\n",
        "df_clean.recuperacao_veiculos.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2072.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oop5PzUQFO74"
      },
      "source": [
        "**Porcentagem de Carros Recuperados em Relação aos Carros Roubados e Carros Furtados**\r\n",
        "\r\n",
        "É possível calcularmos a porcentagem de carros recuperados em relação aos carros roubado e carros furtados:\r\n",
        "\r\n",
        "* A porcentagem de veículos recuperados é aproximadamente 50%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkFtlZLJU4wJ",
        "outputId": "1d579a89-188d-4943-cd8d-a2d67d3d9fa9"
      },
      "source": [
        "# Calculando a porcentagem de carros recuperados em relação a carros roubados e carros furtados\r\n",
        "df_clean.recuperacao_veiculos.sum() / (df_clean.roubo_veiculo.sum() + df_clean.furto_veiculos.sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.49132433693006555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7zm4qQJFzbz"
      },
      "source": [
        "**Valores Máximo e Mínimo para a Variável `hom_doloso`**\r\n",
        "\r\n",
        "Podemos também encontrar o mês e o ano onde teve a maior e a menor quantidade de casos de um determinado crime, vamos verificar a respeito da variável `hom_doloso`\r\n",
        "\r\n",
        "**Valor Máximo para a Variável `hom_doloso` 2011-2021**\r\n",
        "\r\n",
        "* `Março de 2014` - Foi o mês com a maior quantidade de casos de homicídio doloso, foram 510 ocorrências no total.\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "SnWqYE-MFzy5",
        "outputId": "0fc81613-833e-4700-8148-8678a749b452"
      },
      "source": [
        "# Localizando o valor máximo da variável 'hom_doloso'\r\n",
        "df_clean.loc[df_clean['hom_doloso'] == df_clean.hom_doloso.max()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>cvli</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>letalidade_violenta</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_transeunte</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_rua</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</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>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>2014</td>\n",
              "      <td>3</td>\n",
              "      <td>510</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10</td>\n",
              "      <td>525.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>571</td>\n",
              "      <td>544</td>\n",
              "      <td>8357</td>\n",
              "      <td>479.0</td>\n",
              "      <td>214.0</td>\n",
              "      <td>3938.0</td>\n",
              "      <td>7014</td>\n",
              "      <td>555.0</td>\n",
              "      <td>639</td>\n",
              "      <td>8208</td>\n",
              "      <td>2947</td>\n",
              "      <td>401</td>\n",
              "      <td>773</td>\n",
              "      <td>125</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1141</td>\n",
              "      <td>13711</td>\n",
              "      <td>1640</td>\n",
              "      <td>4564</td>\n",
              "      <td>974.0</td>\n",
              "      <td>1577.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9248</td>\n",
              "      <td>18003</td>\n",
              "      <td>1.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2745.0</td>\n",
              "      <td>2046</td>\n",
              "      <td>871.0</td>\n",
              "      <td>1002.0</td>\n",
              "      <td>228.0</td>\n",
              "      <td>2355</td>\n",
              "      <td>3154.0</td>\n",
              "      <td>1077.0</td>\n",
              "      <td>1533.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>8154.0</td>\n",
              "      <td>563.0</td>\n",
              "      <td>59</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72915.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ano  mes  hom_doloso  ...  pol_civis_mortos_serv  registro_ocorrencias  fase\n",
              "278  2014    3         510  ...                    0.0               72915.0     3\n",
              "\n",
              "[1 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5sDodz5SobS"
      },
      "source": [
        "**Valor Máximo no Dataset para a Variável `hom_doloso` 1991-2021**\r\n",
        "\r\n",
        "Com fins de comparação, a maior quantidade de casos de homicídio doloso presente no nosso *dataset* :\r\n",
        "\r\n",
        "* `Janeiro de 1995` - Foi o mês com a maior quantidade de casos de homicídio doloso, foram 831 ocorrências no total. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "YTQTSdv1SbIg",
        "outputId": "f26e0055-c858-4477-b8d8-56f3b1fac78e"
      },
      "source": [
        "# Localizando o valor máximo da variável 'hom_doloso' \r\n",
        "df.loc[df['hom_doloso'] == df.hom_doloso.max()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
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              "      <th>latrocinio</th>\n",
              "      <th>cvli</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>letalidade_violenta</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_transeunte</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_rua</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</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>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1995</td>\n",
              "      <td>1</td>\n",
              "      <td>831</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>848</td>\n",
              "      <td>166</td>\n",
              "      <td>3626</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1056</td>\n",
              "      <td>NaN</td>\n",
              "      <td>377</td>\n",
              "      <td>1433</td>\n",
              "      <td>2060</td>\n",
              "      <td>188</td>\n",
              "      <td>323</td>\n",
              "      <td>174</td>\n",
              "      <td>38</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>134</td>\n",
              "      <td>4350</td>\n",
              "      <td>1924</td>\n",
              "      <td>520</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2941</td>\n",
              "      <td>5385</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>147</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1435</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>98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     ano  mes  hom_doloso  ...  pol_civis_mortos_serv  registro_ocorrencias  fase\n",
              "48  1995    1         831  ...                    NaN                   NaN     3\n",
              "\n",
              "[1 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnGwcIjRX7KN"
      },
      "source": [
        "**Valor Mínimo para a Variável `hom_doloso`**\r\n",
        "\r\n",
        "* `Setembro de 2020` -  Foi o mês com a menor quantidade de casos de homicídio doloso, 239 ocorrências no total. \r\n",
        "\r\n",
        "Se levarmos em consideração que a maior ocorrência foi 6 anos antes da menor, podemos notar que ainda temos uma alta quantidade de casos.\r\n",
        "\r\n",
        "Essa é a menor quantidade de casos desde 1991.\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "KLysEAyUIQXV",
        "outputId": "58f4fe83-075c-462c-f4a1-01daca56063a"
      },
      "source": [
        "# Localizando o valor mínimo da variável 'hom_doloso'\r\n",
        "df_clean.loc[df_clean['hom_doloso'] == df_clean.hom_doloso.min()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>cvli</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>letalidade_violenta</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_transeunte</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_rua</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</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>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>2020</td>\n",
              "      <td>9</td>\n",
              "      <td>239</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7</td>\n",
              "      <td>249.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>303</td>\n",
              "      <td>343</td>\n",
              "      <td>4560</td>\n",
              "      <td>481.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1675.0</td>\n",
              "      <td>3567</td>\n",
              "      <td>1319.0</td>\n",
              "      <td>582</td>\n",
              "      <td>5468</td>\n",
              "      <td>1742</td>\n",
              "      <td>323</td>\n",
              "      <td>294</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1146</td>\n",
              "      <td>9124</td>\n",
              "      <td>1065</td>\n",
              "      <td>698</td>\n",
              "      <td>379.0</td>\n",
              "      <td>823.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>5888</td>\n",
              "      <td>8985</td>\n",
              "      <td>1.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4086.0</td>\n",
              "      <td>1984</td>\n",
              "      <td>974.0</td>\n",
              "      <td>890.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1173</td>\n",
              "      <td>2497.0</td>\n",
              "      <td>455.0</td>\n",
              "      <td>1059.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>4094.0</td>\n",
              "      <td>297.0</td>\n",
              "      <td>22</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48732.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ano  mes  hom_doloso  ...  pol_civis_mortos_serv  registro_ocorrencias  fase\n",
              "356  2020    9         239  ...                    0.0               48732.0     3\n",
              "\n",
              "[1 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WK0NBQZASKC"
      },
      "source": [
        "### Visualização de Dados\n",
        "\n",
        "Após o tratamento dos dados, a análise dos dados e os insigths que obtivemos, podemos plotar gráficos para visualizarmos os dados dispostos de outra maneira."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzSSVCvLGN2J"
      },
      "source": [
        "**Histograma de `hom_doloso`**\r\n",
        "\r\n",
        "o histograma plotado nos mostra que existem extremos quando se trata da quantidade de casos, mas em sua maioria a ocorrência fica entre 340 e 400 casos por mês."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyIEuBIeoocn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "5ce91f4f-0121-4e47-ef1d-992a327139cc"
      },
      "source": [
        "df_clean['hom_doloso'].hist();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chnT-udIGX1I"
      },
      "source": [
        "**Gráfico de linhas para a variável `roubo_em_coletivo`**\r\n",
        "\r\n",
        "O gráfico de linhas nos mostra que o roubo em coletivo esta aumentando desde 2012, quando os números eram próximos de 345 a partir de 2015 houve um aumento de aproximadamente 372%, uma vez que a quantidade de roubos em coletivo ultrapassou 1600 casos em **Agosto de 2017**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "VtjmFMWxo8i4",
        "outputId": "12b17018-72a6-4c73-e4b4-a867851489f6"
      },
      "source": [
        "df_clean['roubo_em_coletivo'].plot();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWbZxsfhngc9"
      },
      "source": [
        "**Valor Mínimo para a Variável `roubo_em_coletivo` 2011-2021**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "AnXEwjREnf09",
        "outputId": "36dc0c59-ca96-4456-893b-5d1e4569d4be"
      },
      "source": [
        "# Localizando o número minimo de roubos em coletivo\r\n",
        "df_clean.loc[df_clean['roubo_em_coletivo'] == df_clean.roubo_em_coletivo.min()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>cvli</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>letalidade_violenta</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_transeunte</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_rua</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</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>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>2012</td>\n",
              "      <td>8</td>\n",
              "      <td>294</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11</td>\n",
              "      <td>306.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>350</td>\n",
              "      <td>349</td>\n",
              "      <td>7328</td>\n",
              "      <td>515.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>4221.0</td>\n",
              "      <td>4041</td>\n",
              "      <td>371.0</td>\n",
              "      <td>345</td>\n",
              "      <td>4757</td>\n",
              "      <td>1751</td>\n",
              "      <td>328</td>\n",
              "      <td>422</td>\n",
              "      <td>100</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>919</td>\n",
              "      <td>8456</td>\n",
              "      <td>1309</td>\n",
              "      <td>2552</td>\n",
              "      <td>888.0</td>\n",
              "      <td>1647.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8332</td>\n",
              "      <td>14728</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3248.0</td>\n",
              "      <td>1644</td>\n",
              "      <td>665.0</td>\n",
              "      <td>813.0</td>\n",
              "      <td>206.0</td>\n",
              "      <td>1395</td>\n",
              "      <td>2361.0</td>\n",
              "      <td>627.0</td>\n",
              "      <td>1515.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>7025.0</td>\n",
              "      <td>517.0</td>\n",
              "      <td>51</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60926.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ano  mes  hom_doloso  ...  pol_civis_mortos_serv  registro_ocorrencias  fase\n",
              "259  2012    8         294  ...                    0.0               60926.0     3\n",
              "\n",
              "[1 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yEVI9OnKmui-"
      },
      "source": [
        "**Valor Máximo para a Variável `roubo_em_coletivo` 2011-2021**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "7c-QbAwwkzZ2",
        "outputId": "cc4f88eb-6bae-4116-a8bb-948761b5b400"
      },
      "source": [
        "# Localizando o número máximo de roubos em coletivo\r\n",
        "df_clean.loc[df_clean['roubo_em_coletivo'] == df_clean.roubo_em_coletivo.max()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>cvli</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>letalidade_violenta</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_transeunte</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_rua</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</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>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>2017</td>\n",
              "      <td>8</td>\n",
              "      <td>397</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16</td>\n",
              "      <td>417.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>487</td>\n",
              "      <td>528</td>\n",
              "      <td>5230</td>\n",
              "      <td>433.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>2576.0</td>\n",
              "      <td>8815</td>\n",
              "      <td>2417.0</td>\n",
              "      <td>1628</td>\n",
              "      <td>12860</td>\n",
              "      <td>4613</td>\n",
              "      <td>843</td>\n",
              "      <td>649</td>\n",
              "      <td>111</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2638</td>\n",
              "      <td>21806</td>\n",
              "      <td>1318</td>\n",
              "      <td>1754</td>\n",
              "      <td>757.0</td>\n",
              "      <td>1340.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>7768</td>\n",
              "      <td>13120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2988.0</td>\n",
              "      <td>1887</td>\n",
              "      <td>725.0</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>3494</td>\n",
              "      <td>2833.0</td>\n",
              "      <td>623.0</td>\n",
              "      <td>1818.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>4701.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>42</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>70380.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ano  mes  hom_doloso  ...  pol_civis_mortos_serv  registro_ocorrencias  fase\n",
              "319  2017    8         397  ...                    1.0               70380.0     3\n",
              "\n",
              "[1 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCsiXvM3BcsO"
      },
      "source": [
        "## Conclusão\n",
        "\n",
        "Após a análise, pode-se observar que em **10 anos** alguns dados mudaram, mas infelizmente existem setores que ainda são alarmantes, uma vez que seus números ainda são muito altos. Existem também, dados onde houve a diminuição dos casos, mas após um curto período de tempo voltaram a subir e continuam preocupantes.\n",
        "\n",
        "Entretanto, cabe ao Governo e a Força Policial do Rio De Janeiro tornar a cidade mais segura e  fazer com que o lar da estátua do **Cristo Redentor** cada vez mais seja atrativo até mesmo para as pessoas que conhecem o Rio como um lugar violento, seja por meio de mais patrulhas, mais homens nas ruas e forças tarefas para combater as facções armadas, a população precisa disso, o País nescessita que a **\"Cidade Maravilhosa\"** seja realmente maravilhosa.\n",
        "\n",
        "Consequentemente, o turismo pode aumentar e o medo diminuir, isso seria ótimo para o Estado, uma vez que tem passado por dificuldades, devido aos últimos anos, resolvendo esse problema talvez ocorra um efeito dominó e não apenas o setor da segurança melhore, mas outros também."
      ]
    }
  ]
}