Analise_da_Violencia_no_RJ.ipynb

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      "name": "Analise da Violencia no RJ.ipynb",
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        "<a href=\"https://colab.research.google.com/github/rbolsoni/Portfolio-Data-Science/blob/master/Analise_da_Violencia_no_RJ.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "![alt text](https://miro.medium.com/max/599/1*HZ0pvBO8vrx_bRxiBSu6OQ.png)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "[![author](https://img.shields.io/badge/author-rbolsoni-orange.svg)](www.linkedin.com/in/rafabolsoni) \n",
        "[![author](https://img.shields.io/badge/see%20more-projects-brightgreen.svg)](https://github.com/rbolsoni) \n",
        "\n",
        "\n"
      ]
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    {
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      "source": [
        "# Prelúdio\n",
        "\n",
        "O nome é mencionado oficialmente pela primeira vez quando a segunda expedição exploratória portuguesa, comandada por Gaspar lemos, chegou em Janeiro de 1502, à baía, que o navegador supôs, compreensivelmente, ser a foz de um rio, por conseguinte, dando o nome à região do Rio de Janeiro. O começo da cidade como tal foi no Morro de São Januário, mais tarde conhecido como Morro do Castelo, e depois na Praça Quinze até hoje centro vital do Rio.\n",
        "\n",
        "O Rio de Janeiro desenvolveu-se graças à sua vocação natural como porto. Na mesma época em que ouro foi descoberto no Estado de Minas Gerais, no final do século XVII, o Governador do Brasil foi feito Vice-rei. Salvador era capital da colônia, mas a importância crescente do porto do Rio garantiu a transferência da sede do poder para o sul, para a cidade que se tornaria, e ainda é, o centro intelectual e cultural do país.\n",
        "\n",
        "A cidade havia crescido bastante, no final do século XIX, contava com 800.000 habitantes e os problemas sanitários, de emprego, habitacionais e as constantes epidemias de varíola, tuberculose e febre amarela castigavam a então capital do Brasil.\n"
      ]
    },
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        "id": "UB4h4oDkWcPs",
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      "source": [
        "## Atualmente\n",
        "O estado do Rio de Janeiro tem segundo estimativa do IBGE 16,46 milhões de habitantes, com a região metropolitana contendo mais de 12 milhões de habitantes e a cidade mais importante do estado de mesmo nome, com 6,7 milhões de habitantes e é o maior destino turístico internacional da America do Sul. É um dos principais centros econômicos, culturais e financeiros do país, sendo internacionalmente conhecida por diversos ícones culturais e paisagísticos, como o Pão de Açúcar, o morro do Corcovado com a estátua do Cristo Redentor. As cidades do estado têm importância turística e econômica conforme a região onde estão instaladas. Na região costeira, que é chamada de Costa do Sol, estão Búzios, Cabo Frio, Arraial do Cabo, Rio das Ostras, Maricá e Saquarema. Na Região Serrana estão Petrópolis, Teresópolis e Nova Friburgo. Entre elas, Petrópolis tem uma arquitetura especial e foi escolhida para as férias de Verão de Dom Pedro II."
      ]
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      "source": [
        "## Criminalidade\n",
        "Desde meados dos anos 1990, em decorrência da violência urbana, o Rio vem conquistando espaço na imprensa nacional e (nos últimos anos) internacional. A cidade apresenta índices elevados de criminalidade, em especial, o homicídio.\n",
        "\n",
        "Por conta disso, hoje vamos analisar mais de perto os dados da criminalidade do Rio de Janeiro e tirar *insights* importantes sobre a real situação da região."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "![alt text](https://image.freepik.com/fotos-gratis/grafico-de-dados-mostram-o-grafico-de-icone-de-analise-de-resumo_53876-64935.jpg)\n",
        "\n",
        "\n",
        "# Origem dos dados\n",
        "Todos os dados que são usados aqui foram retirados da banco de dados do Instituto de Segurança Pública do Estado do Rio de Janeiro (ISP-RJ), que faz parte do Departamento de Segurança Pública do Estado do Rio de Janeiro (DSP-RJ). Os dados estão disponíveis no site do [ISPDados](http://www.ispdados.rj.gov.br/estatistica.html) e contém uma série histórica de 29 anos (desde janeiro 1991 á agosto de 2019).\n",
        "\n",
        "Uma grande parte dos bancos de dados públicos de origem estatal no Brasil tem dados faltando por diversos motivos, seja por dificuldade de acesso a tecnologia por parte dos servidores, perda de informações e arquivos físicos, erros de *input*, arquivos corrompidos, demora na digitalização de documentos antigos ou muitos dados antigos, como é o caso agora.\n",
        "\n",
        "\n",
        "\n"
      ]
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      "source": [
        "### Dicionário\n",
        "\n",
        "Podemos ver [aqui](http://www.ispdados.rj.gov.br/Arquivos/DicionarioVariaveisDOMensalEstadoDesde1991.xlsx) o dicionário completo.\n",
        "Abaixo incluirei somente o dicionário dos dados que iremos analisar no momento.\n",
        "\n",
        "* `vano` = ano do registro\n",
        "* `mes` = mês do registro              \n",
        "* `hom_doloso` = homicídio doloso (com intenção de matar)  \n",
        "* `latrocinio` = roubo seguido de morte\n",
        "* `tentat_hom` = tentativa de homicídio\n",
        "* `lesao_corp_dolosa` = lesão corporal dolosa\n",
        "* `roubo_comercio` = roubo a comércio\n",
        "* `roubo_residencia` = rouso a residência\n",
        "* `roubo_veiculo` = roubo a veículo\n",
        "* `roubo_carga` = roubo de carga\n",
        "* `roubo_transeunte` = roubo a pedestre\n",
        "* `roubo_em_coletivo` = roubo em transporte coletivo\n",
        "* `roubo_banco` = roubo em agencia bancária\n",
        "* `outros_roubos` = outros tipos de roubo\n",
        "* `total_roubos` = total de roubos\n",
        "* `furto_veiculos` = furto de veículos\n",
        "* `furto_transeunte` = furto de pedestres\n",
        "* `outros_furtos` = outros tipos de furtos\n",
        "* `total_furtos` = total de furtos\n",
        "* `apreensao_drogas` = apreensão de drogas\n",
        "* `recuperacao_veiculos` = veículos recuperados\n",
        "* `encontro_cadaver` = quantidade de cadáveres encontrados\n",
        "* `indicador_letalidade` = indicador de letalidade\n",
        "* `indicador_roubo_rua` = indicador de roubo na rua\n",
        "* `indicador_roubo_veic` = indicador de roubo a veículos\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69acHnr2RR8f",
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      "source": [
        "# Análise exploratória\n",
        "\n",
        "Iremos utilizar o auxílio da biblioteca [Pandas](https://pandas.pydata.org/), que é uma das melhores bibliotecas para analise de dados da linguagem Python e ajudará a realizar a analise exploratória de maneira mais eficaz. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGI1p14HcGVU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/carlosfab/dsnp2/master/datasets/violencia_rio.csv')\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMLOSeWchAYp",
        "colab_type": "text"
      },
      "source": [
        "## Análise preliminar dos dados\n",
        "\n",
        "Primeiramente vamos buscar algumas prévias para termos melhor ideia do tamanho do nosso *dataframe* e quais informações importantes constam nele. Queremos ver informações como, tipos de dados, nomes de colunas, quantidade de colunas e linhas e informações das primeiras linhas do nosso dataset. No final tambem iremos descobrir qual a % de dados ausentes."
      ]
    },
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        "outputId": "1490e125-14ab-4c22-ba48-d10dc1b6c091"
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      "source": [
        "df.shape"
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        "outputId": "3f84db80-11ac-425f-9797-7c97eb1847a8"
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      "source": [
        "df.columns"
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['vano', 'mes', 'hom_doloso', 'lesao_corp_morte', 'latrocinio',\n",
              "       'hom_por_interv_policial', 'tentat_hom', 'lesao_corp_dolosa', 'estupro',\n",
              "       'hom_culposo', 'lesao_corp_culposa', 'roubo_comercio',\n",
              "       'roubo_residencia', 'roubo_veiculo', 'roubo_carga', 'roubo_transeunte',\n",
              "       'roubo_em_coletivo', 'roubo_banco', 'roubo_cx_eletronico',\n",
              "       'roubo_celular', 'roubo_conducao_saque', 'roubo_apos_saque',\n",
              "       'roubo_bicicleta', 'outros_roubos', 'total_roubos', 'furto_veiculos',\n",
              "       'furto_transeunte', 'furto_coletivo', 'furto_celular',\n",
              "       'furto_bicicleta', 'outros_furtos', 'total_furtos', 'sequestro',\n",
              "       'extorsao', 'sequestro_relampago', 'estelionato', 'apreensao_drogas',\n",
              "       'posse_drogas', 'trafico_drogas', 'apreensao_drogas_sem_autor',\n",
              "       'recuperacao_veiculos', 'apf', 'aaapai', 'cmp', 'cmba', 'ameaca',\n",
              "       'pessoas_desaparecidas', 'encontro_cadaver', 'encontro_ossada',\n",
              "       'pol_militares_mortos_serv', 'pol_civis_mortos_serv',\n",
              "       'indicador_letalidade', 'indicador_roubo_rua', 'indicador_roubo_veic',\n",
              "       'registro_ocorrencias', 'fase'],\n",
              "      dtype='object')"
            ]
          },
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        "df.dtypes"
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            "text/plain": [
              "vano                            int64\n",
              "mes                             int64\n",
              "hom_doloso                      int64\n",
              "lesao_corp_morte              float64\n",
              "latrocinio                      int64\n",
              "hom_por_interv_policial       float64\n",
              "tentat_hom                      int64\n",
              "lesao_corp_dolosa               int64\n",
              "estupro                       float64\n",
              "hom_culposo                   float64\n",
              "lesao_corp_culposa            float64\n",
              "roubo_comercio                  int64\n",
              "roubo_residencia                int64\n",
              "roubo_veiculo                   int64\n",
              "roubo_carga                     int64\n",
              "roubo_transeunte                int64\n",
              "roubo_em_coletivo               int64\n",
              "roubo_banco                     int64\n",
              "roubo_cx_eletronico           float64\n",
              "roubo_celular                 float64\n",
              "roubo_conducao_saque          float64\n",
              "roubo_apos_saque              float64\n",
              "roubo_bicicleta               float64\n",
              "outros_roubos                   int64\n",
              "total_roubos                    int64\n",
              "furto_veiculos                  int64\n",
              "furto_transeunte                int64\n",
              "furto_coletivo                float64\n",
              "furto_celular                 float64\n",
              "furto_bicicleta               float64\n",
              "outros_furtos                   int64\n",
              "total_furtos                    int64\n",
              "sequestro                     float64\n",
              "extorsao                      float64\n",
              "sequestro_relampago           float64\n",
              "estelionato                   float64\n",
              "apreensao_drogas                int64\n",
              "posse_drogas                  float64\n",
              "trafico_drogas                float64\n",
              "apreensao_drogas_sem_autor    float64\n",
              "recuperacao_veiculos            int64\n",
              "apf                           float64\n",
              "aaapai                        float64\n",
              "cmp                           float64\n",
              "cmba                          float64\n",
              "ameaca                        float64\n",
              "pessoas_desaparecidas         float64\n",
              "encontro_cadaver                int64\n",
              "encontro_ossada               float64\n",
              "pol_militares_mortos_serv     float64\n",
              "pol_civis_mortos_serv         float64\n",
              "indicador_letalidade            int64\n",
              "indicador_roubo_rua             int64\n",
              "indicador_roubo_veic            int64\n",
              "registro_ocorrencias          float64\n",
              "fase                            int64\n",
              "dtype: object"
            ]
          },
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          },
          "execution_count": 4
        }
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      "source": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>tentat_hom</th>\n",
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              "      <th>estupro</th>\n",
              "      <th>hom_culposo</th>\n",
              "      <th>lesao_corp_culposa</th>\n",
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              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_veiculo</th>\n",
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              "      <th>roubo_transeunte</th>\n",
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              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_conducao_saque</th>\n",
              "      <th>roubo_apos_saque</th>\n",
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              "      <th>outros_roubos</th>\n",
              "      <th>total_roubos</th>\n",
              "      <th>furto_veiculos</th>\n",
              "      <th>furto_transeunte</th>\n",
              "      <th>furto_coletivo</th>\n",
              "      <th>furto_celular</th>\n",
              "      <th>furto_bicicleta</th>\n",
              "      <th>outros_furtos</th>\n",
              "      <th>total_furtos</th>\n",
              "      <th>sequestro</th>\n",
              "      <th>extorsao</th>\n",
              "      <th>sequestro_relampago</th>\n",
              "      <th>estelionato</th>\n",
              "      <th>apreensao_drogas</th>\n",
              "      <th>posse_drogas</th>\n",
              "      <th>trafico_drogas</th>\n",
              "      <th>apreensao_drogas_sem_autor</th>\n",
              "      <th>recuperacao_veiculos</th>\n",
              "      <th>apf</th>\n",
              "      <th>aaapai</th>\n",
              "      <th>cmp</th>\n",
              "      <th>cmba</th>\n",
              "      <th>ameaca</th>\n",
              "      <th>pessoas_desaparecidas</th>\n",
              "      <th>encontro_cadaver</th>\n",
              "      <th>encontro_ossada</th>\n",
              "      <th>pol_militares_mortos_serv</th>\n",
              "      <th>pol_civis_mortos_serv</th>\n",
              "      <th>indicador_letalidade</th>\n",
              "      <th>indicador_roubo_rua</th>\n",
              "      <th>indicador_roubo_veic</th>\n",
              "      <th>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
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              "      <td>350</td>\n",
              "      <td>188</td>\n",
              "      <td>1174</td>\n",
              "      <td>63</td>\n",
              "      <td>967</td>\n",
              "      <td>381</td>\n",
              "      <td>37</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>158</td>\n",
              "      <td>3318</td>\n",
              "      <td>2009</td>\n",
              "      <td>1147</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3561</td>\n",
              "      <td>6717</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>133</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>872</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>217</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>672</td>\n",
              "      <td>1348</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
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              "      <td>1991</td>\n",
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              "      <td>732</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>175</td>\n",
              "      <td>3421</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>171</td>\n",
              "      <td>1097</td>\n",
              "      <td>64</td>\n",
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              "      <td>432</td>\n",
              "      <td>35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>142</td>\n",
              "      <td>3261</td>\n",
              "      <td>1850</td>\n",
              "      <td>1066</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3309</td>\n",
              "      <td>6225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>127</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>751</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>209</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>749</td>\n",
              "      <td>1395</td>\n",
              "      <td>1097</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1991</td>\n",
              "      <td>3</td>\n",
              "      <td>713</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>216</td>\n",
              "      <td>3613</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>443</td>\n",
              "      <td>191</td>\n",
              "      <td>1265</td>\n",
              "      <td>71</td>\n",
              "      <td>963</td>\n",
              "      <td>422</td>\n",
              "      <td>43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>131</td>\n",
              "      <td>3529</td>\n",
              "      <td>2084</td>\n",
              "      <td>1062</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3573</td>\n",
              "      <td>6719</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>188</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>738</td>\n",
              "      <td>1385</td>\n",
              "      <td>1265</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1991</td>\n",
              "      <td>4</td>\n",
              "      <td>634</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>200</td>\n",
              "      <td>3211</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>425</td>\n",
              "      <td>199</td>\n",
              "      <td>1415</td>\n",
              "      <td>81</td>\n",
              "      <td>1186</td>\n",
              "      <td>354</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>3808</td>\n",
              "      <td>2085</td>\n",
              "      <td>1195</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3290</td>\n",
              "      <td>6570</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>133</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>787</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>140</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>654</td>\n",
              "      <td>1540</td>\n",
              "      <td>1415</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1991</td>\n",
              "      <td>5</td>\n",
              "      <td>650</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>146</td>\n",
              "      <td>3051</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>553</td>\n",
              "      <td>231</td>\n",
              "      <td>1449</td>\n",
              "      <td>82</td>\n",
              "      <td>931</td>\n",
              "      <td>335</td>\n",
              "      <td>27</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>147</td>\n",
              "      <td>3755</td>\n",
              "      <td>2185</td>\n",
              "      <td>723</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3546</td>\n",
              "      <td>6454</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>163</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>845</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>670</td>\n",
              "      <td>1266</td>\n",
              "      <td>1449</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vano  mes  hom_doloso  ...  indicador_roubo_veic  registro_ocorrencias  fase\n",
              "0  1991    1         657  ...                  1174                   NaN     3\n",
              "1  1991    2         732  ...                  1097                   NaN     3\n",
              "2  1991    3         713  ...                  1265                   NaN     3\n",
              "3  1991    4         634  ...                  1415                   NaN     3\n",
              "4  1991    5         650  ...                  1449                   NaN     3\n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJtcXxFFVPsk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "9b7022f4-73ae-4c17-a9f3-e9b1294d2bb9"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>lesao_corp_morte</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>hom_por_interv_policial</th>\n",
              "      <th>tentat_hom</th>\n",
              "      <th>lesao_corp_dolosa</th>\n",
              "      <th>estupro</th>\n",
              "      <th>hom_culposo</th>\n",
              "      <th>lesao_corp_culposa</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_transeunte</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>roubo_cx_eletronico</th>\n",
              "      <th>roubo_celular</th>\n",
              "      <th>roubo_conducao_saque</th>\n",
              "      <th>roubo_apos_saque</th>\n",
              "      <th>roubo_bicicleta</th>\n",
              "      <th>outros_roubos</th>\n",
              "      <th>total_roubos</th>\n",
              "      <th>furto_veiculos</th>\n",
              "      <th>furto_transeunte</th>\n",
              "      <th>furto_coletivo</th>\n",
              "      <th>furto_celular</th>\n",
              "      <th>furto_bicicleta</th>\n",
              "      <th>outros_furtos</th>\n",
              "      <th>total_furtos</th>\n",
              "      <th>sequestro</th>\n",
              "      <th>extorsao</th>\n",
              "      <th>sequestro_relampago</th>\n",
              "      <th>estelionato</th>\n",
              "      <th>apreensao_drogas</th>\n",
              "      <th>posse_drogas</th>\n",
              "      <th>trafico_drogas</th>\n",
              "      <th>apreensao_drogas_sem_autor</th>\n",
              "      <th>recuperacao_veiculos</th>\n",
              "      <th>apf</th>\n",
              "      <th>aaapai</th>\n",
              "      <th>cmp</th>\n",
              "      <th>cmba</th>\n",
              "      <th>ameaca</th>\n",
              "      <th>pessoas_desaparecidas</th>\n",
              "      <th>encontro_cadaver</th>\n",
              "      <th>encontro_ossada</th>\n",
              "      <th>pol_militares_mortos_serv</th>\n",
              "      <th>pol_civis_mortos_serv</th>\n",
              "      <th>indicador_letalidade</th>\n",
              "      <th>indicador_roubo_rua</th>\n",
              "      <th>indicador_roubo_veic</th>\n",
              "      <th>registro_ocorrencias</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>2019</td>\n",
              "      <td>4</td>\n",
              "      <td>360</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11</td>\n",
              "      <td>124.0</td>\n",
              "      <td>466</td>\n",
              "      <td>5573</td>\n",
              "      <td>483.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>2182.0</td>\n",
              "      <td>502</td>\n",
              "      <td>124</td>\n",
              "      <td>3755</td>\n",
              "      <td>667</td>\n",
              "      <td>7031</td>\n",
              "      <td>1585</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2424.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>2245</td>\n",
              "      <td>18454</td>\n",
              "      <td>1244</td>\n",
              "      <td>1519</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1318.0</td>\n",
              "      <td>211.0</td>\n",
              "      <td>7633</td>\n",
              "      <td>12532</td>\n",
              "      <td>0.0</td>\n",
              "      <td>151.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3260.0</td>\n",
              "      <td>1909</td>\n",
              "      <td>745.0</td>\n",
              "      <td>1021.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>2624</td>\n",
              "      <td>3128.0</td>\n",
              "      <td>497.0</td>\n",
              "      <td>1648.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>5310.0</td>\n",
              "      <td>408.0</td>\n",
              "      <td>22</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>496</td>\n",
              "      <td>11040</td>\n",
              "      <td>3755</td>\n",
              "      <td>67797.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>345</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15</td>\n",
              "      <td>172.0</td>\n",
              "      <td>478</td>\n",
              "      <td>4958</td>\n",
              "      <td>465.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>2304.0</td>\n",
              "      <td>514</td>\n",
              "      <td>93</td>\n",
              "      <td>3649</td>\n",
              "      <td>710</td>\n",
              "      <td>7332</td>\n",
              "      <td>1625</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2427.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2216</td>\n",
              "      <td>18673</td>\n",
              "      <td>1480</td>\n",
              "      <td>1475</td>\n",
              "      <td>715.0</td>\n",
              "      <td>1423.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>7711</td>\n",
              "      <td>13024</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3498.0</td>\n",
              "      <td>2034</td>\n",
              "      <td>735.0</td>\n",
              "      <td>1133.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>2450</td>\n",
              "      <td>3169.0</td>\n",
              "      <td>527.0</td>\n",
              "      <td>1771.0</td>\n",
              "      <td>248.0</td>\n",
              "      <td>5068.0</td>\n",
              "      <td>390.0</td>\n",
              "      <td>20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>534</td>\n",
              "      <td>11384</td>\n",
              "      <td>3649</td>\n",
              "      <td>68336.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>2019</td>\n",
              "      <td>6</td>\n",
              "      <td>332</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8</td>\n",
              "      <td>153.0</td>\n",
              "      <td>436</td>\n",
              "      <td>4769</td>\n",
              "      <td>414.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>2481.0</td>\n",
              "      <td>382</td>\n",
              "      <td>85</td>\n",
              "      <td>3115</td>\n",
              "      <td>599</td>\n",
              "      <td>6077</td>\n",
              "      <td>1287</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2187.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1984</td>\n",
              "      <td>15832</td>\n",
              "      <td>1251</td>\n",
              "      <td>1443</td>\n",
              "      <td>630.0</td>\n",
              "      <td>1345.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>7224</td>\n",
              "      <td>12082</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3029.0</td>\n",
              "      <td>1854</td>\n",
              "      <td>763.0</td>\n",
              "      <td>976.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>2041</td>\n",
              "      <td>2848.0</td>\n",
              "      <td>467.0</td>\n",
              "      <td>1585.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>4497.0</td>\n",
              "      <td>403.0</td>\n",
              "      <td>20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>496</td>\n",
              "      <td>9551</td>\n",
              "      <td>3115</td>\n",
              "      <td>61202.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>2019</td>\n",
              "      <td>7</td>\n",
              "      <td>309</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10</td>\n",
              "      <td>194.0</td>\n",
              "      <td>399</td>\n",
              "      <td>4740</td>\n",
              "      <td>402.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>2334.0</td>\n",
              "      <td>386</td>\n",
              "      <td>72</td>\n",
              "      <td>3198</td>\n",
              "      <td>691</td>\n",
              "      <td>6637</td>\n",
              "      <td>1208</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2226.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2110</td>\n",
              "      <td>16654</td>\n",
              "      <td>1353</td>\n",
              "      <td>1570</td>\n",
              "      <td>746.0</td>\n",
              "      <td>1391.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>7632</td>\n",
              "      <td>12922</td>\n",
              "      <td>0.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>3627.0</td>\n",
              "      <td>1861</td>\n",
              "      <td>709.0</td>\n",
              "      <td>996.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>2355</td>\n",
              "      <td>2907.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1856.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>4678.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>32</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>518</td>\n",
              "      <td>10071</td>\n",
              "      <td>3198</td>\n",
              "      <td>65817.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>2019</td>\n",
              "      <td>8</td>\n",
              "      <td>318</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6</td>\n",
              "      <td>170.0</td>\n",
              "      <td>457</td>\n",
              "      <td>4760</td>\n",
              "      <td>460.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>2437.0</td>\n",
              "      <td>433</td>\n",
              "      <td>103</td>\n",
              "      <td>3181</td>\n",
              "      <td>587</td>\n",
              "      <td>6452</td>\n",
              "      <td>1215</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2245.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>1794</td>\n",
              "      <td>16136</td>\n",
              "      <td>1361</td>\n",
              "      <td>1568</td>\n",
              "      <td>674.0</td>\n",
              "      <td>1534.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>7280</td>\n",
              "      <td>12602</td>\n",
              "      <td>0.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>3546.0</td>\n",
              "      <td>1862</td>\n",
              "      <td>668.0</td>\n",
              "      <td>1067.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>2081</td>\n",
              "      <td>3114.0</td>\n",
              "      <td>476.0</td>\n",
              "      <td>2028.0</td>\n",
              "      <td>265.0</td>\n",
              "      <td>4662.0</td>\n",
              "      <td>367.0</td>\n",
              "      <td>27</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>495</td>\n",
              "      <td>9912</td>\n",
              "      <td>3181</td>\n",
              "      <td>65285.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     vano  mes  hom_doloso  ...  indicador_roubo_veic  registro_ocorrencias  fase\n",
              "339  2019    4         360  ...                  3755               67797.0     3\n",
              "340  2019    5         345  ...                  3649               68336.0     3\n",
              "341  2019    6         332  ...                  3115               61202.0     3\n",
              "342  2019    7         309  ...                  3198               65817.0     2\n",
              "343  2019    8         318  ...                  3181               65285.0     2\n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VJPPhuGdpxG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "outputId": "a80b961e-f71b-4995-a1d9-bdd8b54f17a7"
      },
      "source": [
        "df.isnull().sum() / df.shape[0] * 100"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vano                           0.000000\n",
              "mes                            0.000000\n",
              "hom_doloso                     0.000000\n",
              "lesao_corp_morte              27.906977\n",
              "latrocinio                     0.000000\n",
              "hom_por_interv_policial       24.418605\n",
              "tentat_hom                     0.000000\n",
              "lesao_corp_dolosa              0.000000\n",
              "estupro                       41.860465\n",
              "hom_culposo                   24.418605\n",
              "lesao_corp_culposa            27.906977\n",
              "roubo_comercio                 0.000000\n",
              "roubo_residencia               0.000000\n",
              "roubo_veiculo                  0.000000\n",
              "roubo_carga                    0.000000\n",
              "roubo_transeunte               0.000000\n",
              "roubo_em_coletivo              0.000000\n",
              "roubo_banco                    0.000000\n",
              "roubo_cx_eletronico           41.860465\n",
              "roubo_celular                 31.395349\n",
              "roubo_conducao_saque          41.860465\n",
              "roubo_apos_saque              41.860465\n",
              "roubo_bicicleta               80.232558\n",
              "outros_roubos                  0.000000\n",
              "total_roubos                   0.000000\n",
              "furto_veiculos                 0.000000\n",
              "furto_transeunte               0.000000\n",
              "furto_coletivo                41.860465\n",
              "furto_celular                 41.860465\n",
              "furto_bicicleta               80.232558\n",
              "outros_furtos                  0.000000\n",
              "total_furtos                   0.000000\n",
              "sequestro                     27.906977\n",
              "extorsao                      41.860465\n",
              "sequestro_relampago           41.860465\n",
              "estelionato                   41.860465\n",
              "apreensao_drogas               0.000000\n",
              "posse_drogas                  52.325581\n",
              "trafico_drogas                52.325581\n",
              "apreensao_drogas_sem_autor    52.325581\n",
              "recuperacao_veiculos           0.000000\n",
              "apf                           52.325581\n",
              "aaapai                        52.325581\n",
              "cmp                           52.325581\n",
              "cmba                          52.325581\n",
              "ameaca                        27.906977\n",
              "pessoas_desaparecidas         38.372093\n",
              "encontro_cadaver               0.000000\n",
              "encontro_ossada               38.372093\n",
              "pol_militares_mortos_serv     41.860465\n",
              "pol_civis_mortos_serv         41.860465\n",
              "indicador_letalidade           0.000000\n",
              "indicador_roubo_rua            0.000000\n",
              "indicador_roubo_veic           0.000000\n",
              "registro_ocorrencias          27.906977\n",
              "fase                           0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVdqCozChYLZ",
        "colab_type": "text"
      },
      "source": [
        "## Trabalhando o Dataframe\n",
        "\n",
        "Algumas classificações foram sendo incluidas com o passar do tempo. Podemos observar que as colunas `furto_bicicleta` e `roubo_bicicleta` tem mais de 80% dos dados ausentes, pois começaram a ser contabilizadas a partir de 2014. Como queremos fazer a análise dos dados da sequência histórica completa, e essas colunas que foram adicionadas depois, nós iremos retirar do dataframe e vamos deixar para uma análise posterior."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IS1GFafPO0yf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "6ca4f5d6-70f1-4bb6-8fc0-4a03d6eb9343"
      },
      "source": [
        "df_clean = df.drop(['lesao_corp_morte','hom_por_interv_policial','estupro','hom_culposo','lesao_corp_culposa','roubo_cx_eletronico','roubo_celular','roubo_conducao_saque',\n",
        "                    'roubo_apos_saque','roubo_bicicleta','furto_coletivo','furto_celular','furto_bicicleta','sequestro','extorsao','sequestro_relampago','estelionato',\n",
        "                    'posse_drogas','trafico_drogas','apreensao_drogas_sem_autor','apf','aaapai','cmp','cmba','ameaca','pessoas_desaparecidas','encontro_ossada',\n",
        "                    'pol_militares_mortos_serv','pol_civis_mortos_serv','registro_ocorrencias'], axis=1).replace('vano', 'ano')\n",
        "df_clean.isnull().sum() / df.shape[0] * 100"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vano                    0.0\n",
              "mes                     0.0\n",
              "hom_doloso              0.0\n",
              "latrocinio              0.0\n",
              "tentat_hom              0.0\n",
              "lesao_corp_dolosa       0.0\n",
              "roubo_comercio          0.0\n",
              "roubo_residencia        0.0\n",
              "roubo_veiculo           0.0\n",
              "roubo_carga             0.0\n",
              "roubo_transeunte        0.0\n",
              "roubo_em_coletivo       0.0\n",
              "roubo_banco             0.0\n",
              "outros_roubos           0.0\n",
              "total_roubos            0.0\n",
              "furto_veiculos          0.0\n",
              "furto_transeunte        0.0\n",
              "outros_furtos           0.0\n",
              "total_furtos            0.0\n",
              "apreensao_drogas        0.0\n",
              "recuperacao_veiculos    0.0\n",
              "encontro_cadaver        0.0\n",
              "indicador_letalidade    0.0\n",
              "indicador_roubo_rua     0.0\n",
              "indicador_roubo_veic    0.0\n",
              "fase                    0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUx4NO6DVgXv",
        "colab_type": "text"
      },
      "source": [
        "Precisamos transformar as datas que estavam em colunas diferentes para o formato de `datetime` reconhecido pelo Python e na mesma coluna para facilitar a plotagem dos gráficos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfADFKXv9Y9r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_clean['data'] = df_clean.mes.astype(str) + '-' + df_clean.vano.astype(str)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBN3ngFejZZt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_clean['data'] = pd.to_datetime(df_clean['data'], format='%m-%Y')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6pyKrlEZ1QB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_clean.set_index('data', drop=True, inplace=True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zg76hMJZfUI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_clean.reset_index(inplace=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-K4hQA7QdT-0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "5775322e-6c94-405c-8da1-f7b6174c5321"
      },
      "source": [
        "df_clean.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>data</th>\n",
              "      <th>vano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>tentat_hom</th>\n",
              "      <th>lesao_corp_dolosa</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_transeunte</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>outros_roubos</th>\n",
              "      <th>total_roubos</th>\n",
              "      <th>furto_veiculos</th>\n",
              "      <th>furto_transeunte</th>\n",
              "      <th>outros_furtos</th>\n",
              "      <th>total_furtos</th>\n",
              "      <th>apreensao_drogas</th>\n",
              "      <th>recuperacao_veiculos</th>\n",
              "      <th>encontro_cadaver</th>\n",
              "      <th>indicador_letalidade</th>\n",
              "      <th>indicador_roubo_rua</th>\n",
              "      <th>indicador_roubo_veic</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1991-01-01</td>\n",
              "      <td>1991</td>\n",
              "      <td>1</td>\n",
              "      <td>657</td>\n",
              "      <td>15</td>\n",
              "      <td>162</td>\n",
              "      <td>3051</td>\n",
              "      <td>350</td>\n",
              "      <td>188</td>\n",
              "      <td>1174</td>\n",
              "      <td>63</td>\n",
              "      <td>967</td>\n",
              "      <td>381</td>\n",
              "      <td>37</td>\n",
              "      <td>158</td>\n",
              "      <td>3318</td>\n",
              "      <td>2009</td>\n",
              "      <td>1147</td>\n",
              "      <td>3561</td>\n",
              "      <td>6717</td>\n",
              "      <td>133</td>\n",
              "      <td>872</td>\n",
              "      <td>217</td>\n",
              "      <td>672</td>\n",
              "      <td>1348</td>\n",
              "      <td>1174</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1991-02-01</td>\n",
              "      <td>1991</td>\n",
              "      <td>2</td>\n",
              "      <td>732</td>\n",
              "      <td>17</td>\n",
              "      <td>175</td>\n",
              "      <td>3421</td>\n",
              "      <td>357</td>\n",
              "      <td>171</td>\n",
              "      <td>1097</td>\n",
              "      <td>64</td>\n",
              "      <td>963</td>\n",
              "      <td>432</td>\n",
              "      <td>35</td>\n",
              "      <td>142</td>\n",
              "      <td>3261</td>\n",
              "      <td>1850</td>\n",
              "      <td>1066</td>\n",
              "      <td>3309</td>\n",
              "      <td>6225</td>\n",
              "      <td>127</td>\n",
              "      <td>751</td>\n",
              "      <td>209</td>\n",
              "      <td>749</td>\n",
              "      <td>1395</td>\n",
              "      <td>1097</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1991-03-01</td>\n",
              "      <td>1991</td>\n",
              "      <td>3</td>\n",
              "      <td>713</td>\n",
              "      <td>25</td>\n",
              "      <td>216</td>\n",
              "      <td>3613</td>\n",
              "      <td>443</td>\n",
              "      <td>191</td>\n",
              "      <td>1265</td>\n",
              "      <td>71</td>\n",
              "      <td>963</td>\n",
              "      <td>422</td>\n",
              "      <td>43</td>\n",
              "      <td>131</td>\n",
              "      <td>3529</td>\n",
              "      <td>2084</td>\n",
              "      <td>1062</td>\n",
              "      <td>3573</td>\n",
              "      <td>6719</td>\n",
              "      <td>80</td>\n",
              "      <td>789</td>\n",
              "      <td>188</td>\n",
              "      <td>738</td>\n",
              "      <td>1385</td>\n",
              "      <td>1265</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1991-04-01</td>\n",
              "      <td>1991</td>\n",
              "      <td>4</td>\n",
              "      <td>634</td>\n",
              "      <td>20</td>\n",
              "      <td>200</td>\n",
              "      <td>3211</td>\n",
              "      <td>425</td>\n",
              "      <td>199</td>\n",
              "      <td>1415</td>\n",
              "      <td>81</td>\n",
              "      <td>1186</td>\n",
              "      <td>354</td>\n",
              "      <td>36</td>\n",
              "      <td>112</td>\n",
              "      <td>3808</td>\n",
              "      <td>2085</td>\n",
              "      <td>1195</td>\n",
              "      <td>3290</td>\n",
              "      <td>6570</td>\n",
              "      <td>133</td>\n",
              "      <td>787</td>\n",
              "      <td>140</td>\n",
              "      <td>654</td>\n",
              "      <td>1540</td>\n",
              "      <td>1415</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1991-05-01</td>\n",
              "      <td>1991</td>\n",
              "      <td>5</td>\n",
              "      <td>650</td>\n",
              "      <td>20</td>\n",
              "      <td>146</td>\n",
              "      <td>3051</td>\n",
              "      <td>553</td>\n",
              "      <td>231</td>\n",
              "      <td>1449</td>\n",
              "      <td>82</td>\n",
              "      <td>931</td>\n",
              "      <td>335</td>\n",
              "      <td>27</td>\n",
              "      <td>147</td>\n",
              "      <td>3755</td>\n",
              "      <td>2185</td>\n",
              "      <td>723</td>\n",
              "      <td>3546</td>\n",
              "      <td>6454</td>\n",
              "      <td>163</td>\n",
              "      <td>845</td>\n",
              "      <td>78</td>\n",
              "      <td>670</td>\n",
              "      <td>1266</td>\n",
              "      <td>1449</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        data  vano  mes  ...  indicador_roubo_rua  indicador_roubo_veic  fase\n",
              "0 1991-01-01  1991    1  ...                 1348                  1174     3\n",
              "1 1991-02-01  1991    2  ...                 1395                  1097     3\n",
              "2 1991-03-01  1991    3  ...                 1385                  1265     3\n",
              "3 1991-04-01  1991    4  ...                 1540                  1415     3\n",
              "4 1991-05-01  1991    5  ...                 1266                  1449     3\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MX68ujLyhg9B",
        "colab_type": "text"
      },
      "source": [
        "## Informações estatisticas gerais da criminalidade do Rio de Janeiro\n",
        "\n",
        "Agora podemos observar um dataframe somente com dados completos desde 1991 para realizar a análise. Vamos agora explorar os dados para retirar insights importantes.\n",
        "\n",
        "Abaixo iremos ver estatisticas gerais da criminalidade do Rio de Janeiro a partir dos dados que foram limpos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhMudaaOWQAo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "ebc3777c-84d5-4344-966b-52d779885908"
      },
      "source": [
        "df_clean.describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vano</th>\n",
              "      <th>mes</th>\n",
              "      <th>hom_doloso</th>\n",
              "      <th>latrocinio</th>\n",
              "      <th>tentat_hom</th>\n",
              "      <th>lesao_corp_dolosa</th>\n",
              "      <th>roubo_comercio</th>\n",
              "      <th>roubo_residencia</th>\n",
              "      <th>roubo_veiculo</th>\n",
              "      <th>roubo_carga</th>\n",
              "      <th>roubo_transeunte</th>\n",
              "      <th>roubo_em_coletivo</th>\n",
              "      <th>roubo_banco</th>\n",
              "      <th>outros_roubos</th>\n",
              "      <th>total_roubos</th>\n",
              "      <th>furto_veiculos</th>\n",
              "      <th>furto_transeunte</th>\n",
              "      <th>outros_furtos</th>\n",
              "      <th>total_furtos</th>\n",
              "      <th>apreensao_drogas</th>\n",
              "      <th>recuperacao_veiculos</th>\n",
              "      <th>encontro_cadaver</th>\n",
              "      <th>indicador_letalidade</th>\n",
              "      <th>indicador_roubo_rua</th>\n",
              "      <th>indicador_roubo_veic</th>\n",
              "      <th>fase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>344.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2004.837209</td>\n",
              "      <td>6.453488</td>\n",
              "      <td>504.555233</td>\n",
              "      <td>15.668605</td>\n",
              "      <td>318.267442</td>\n",
              "      <td>5492.799419</td>\n",
              "      <td>455.005814</td>\n",
              "      <td>133.284884</td>\n",
              "      <td>2448.697674</td>\n",
              "      <td>340.322674</td>\n",
              "      <td>3418.520349</td>\n",
              "      <td>659.002907</td>\n",
              "      <td>9.375000</td>\n",
              "      <td>1211.223837</td>\n",
              "      <td>9428.119186</td>\n",
              "      <td>1674.671512</td>\n",
              "      <td>1518.921512</td>\n",
              "      <td>5994.328488</td>\n",
              "      <td>10433.177326</td>\n",
              "      <td>1071.930233</td>\n",
              "      <td>1772.238372</td>\n",
              "      <td>66.261628</td>\n",
              "      <td>577.691860</td>\n",
              "      <td>4734.738372</td>\n",
              "      <td>2448.697674</td>\n",
              "      <td>2.994186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.289036</td>\n",
              "      <td>3.447759</td>\n",
              "      <td>113.774481</td>\n",
              "      <td>5.861427</td>\n",
              "      <td>127.606645</td>\n",
              "      <td>1514.220579</td>\n",
              "      <td>119.765076</td>\n",
              "      <td>44.317963</td>\n",
              "      <td>789.689559</td>\n",
              "      <td>223.855243</td>\n",
              "      <td>2459.017549</td>\n",
              "      <td>297.622342</td>\n",
              "      <td>10.564894</td>\n",
              "      <td>800.576723</td>\n",
              "      <td>4578.013053</td>\n",
              "      <td>294.852020</td>\n",
              "      <td>971.853102</td>\n",
              "      <td>1964.767779</td>\n",
              "      <td>3696.725756</td>\n",
              "      <td>643.381991</td>\n",
              "      <td>656.761843</td>\n",
              "      <td>34.464519</td>\n",
              "      <td>104.973929</td>\n",
              "      <td>3204.054864</td>\n",
              "      <td>789.689559</td>\n",
              "      <td>0.076138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1991.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>2350.000000</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>1097.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>688.000000</td>\n",
              "      <td>281.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>3261.000000</td>\n",
              "      <td>1159.000000</td>\n",
              "      <td>322.000000</td>\n",
              "      <td>2322.000000</td>\n",
              "      <td>4691.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>729.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>1086.000000</td>\n",
              "      <td>1097.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1998.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>413.750000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>4067.000000</td>\n",
              "      <td>365.750000</td>\n",
              "      <td>103.750000</td>\n",
              "      <td>1889.750000</td>\n",
              "      <td>208.250000</td>\n",
              "      <td>1053.000000</td>\n",
              "      <td>430.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>811.750000</td>\n",
              "      <td>5107.750000</td>\n",
              "      <td>1430.500000</td>\n",
              "      <td>732.000000</td>\n",
              "      <td>3978.000000</td>\n",
              "      <td>6700.250000</td>\n",
              "      <td>714.500000</td>\n",
              "      <td>1276.750000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>1557.000000</td>\n",
              "      <td>1889.750000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2005.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>507.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>318.500000</td>\n",
              "      <td>5830.500000</td>\n",
              "      <td>433.500000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>2322.000000</td>\n",
              "      <td>285.500000</td>\n",
              "      <td>2788.500000</td>\n",
              "      <td>592.000000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>1154.000000</td>\n",
              "      <td>9243.500000</td>\n",
              "      <td>1637.500000</td>\n",
              "      <td>1076.500000</td>\n",
              "      <td>6559.000000</td>\n",
              "      <td>10448.500000</td>\n",
              "      <td>979.500000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>60.500000</td>\n",
              "      <td>590.000000</td>\n",
              "      <td>4190.000000</td>\n",
              "      <td>2322.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2012.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>577.250000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>398.250000</td>\n",
              "      <td>6582.500000</td>\n",
              "      <td>536.250000</td>\n",
              "      <td>151.250000</td>\n",
              "      <td>2778.000000</td>\n",
              "      <td>401.250000</td>\n",
              "      <td>5385.250000</td>\n",
              "      <td>784.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>1635.000000</td>\n",
              "      <td>11590.500000</td>\n",
              "      <td>1833.250000</td>\n",
              "      <td>2334.250000</td>\n",
              "      <td>7601.500000</td>\n",
              "      <td>13877.500000</td>\n",
              "      <td>1546.000000</td>\n",
              "      <td>2202.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>649.000000</td>\n",
              "      <td>6872.750000</td>\n",
              "      <td>2778.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>831.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>645.000000</td>\n",
              "      <td>9050.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>5358.000000</td>\n",
              "      <td>1329.000000</td>\n",
              "      <td>9669.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>3661.000000</td>\n",
              "      <td>23203.000000</td>\n",
              "      <td>2630.000000</td>\n",
              "      <td>5368.000000</td>\n",
              "      <td>9880.000000</td>\n",
              "      <td>18003.000000</td>\n",
              "      <td>2543.000000</td>\n",
              "      <td>3695.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>848.000000</td>\n",
              "      <td>13833.000000</td>\n",
              "      <td>5358.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              vano         mes  ...  indicador_roubo_veic        fase\n",
              "count   344.000000  344.000000  ...            344.000000  344.000000\n",
              "mean   2004.837209    6.453488  ...           2448.697674    2.994186\n",
              "std       8.289036    3.447759  ...            789.689559    0.076138\n",
              "min    1991.000000    1.000000  ...           1097.000000    2.000000\n",
              "25%    1998.000000    3.000000  ...           1889.750000    3.000000\n",
              "50%    2005.000000    6.000000  ...           2322.000000    3.000000\n",
              "75%    2012.000000    9.000000  ...           2778.000000    3.000000\n",
              "max    2019.000000   12.000000  ...           5358.000000    3.000000\n",
              "\n",
              "[8 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxyH5JaHhyDS",
        "colab_type": "text"
      },
      "source": [
        "## Visualização dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XytYHSVPr7M8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "e8d6eefa-3f7c-488d-9e98-63318855c28d"
      },
      "source": [
        "df_clean.plot(x='data', y=['roubo_comercio','roubo_residencia','roubo_carga','roubo_banco']);"
      ],
      "execution_count": 15,
      "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": "XYA5uMCNMObd",
        "colab_type": "text"
      },
      "source": [
        "Podemos observar nesse gráfico que o roubo de carga tem tido uma taxa de crescimento alta nos ultimos 10 anos. De 2009 até o pico que ocorreu em meados de 2017 o roubo de cargas teve um aumento de mais de 500%. Os dados de 2017 mostram que [a cada 23 minutos, um caminhão de carga é roubado no país](https://oglobo.globo.com/economia/infraestrutura/roubo-de-cargas-aumenta-custo-de-produtos-em-ate-35-21074993#:~:text=O%20estados%20de%20Rio%20de,15%20por%20cada%20cem%20quilos.). Esse aumento de roubo de carga gera um prejuízo enorme para as empresas de transporte, pela perda de mercadorias e pelo aumento nos custos de segurança particular e seguros que acabam gerando um efeito cascata e repassando esse custo para o consumidor. Um estudo sobre o impacto econômico do roubo de cargas, divulgado pela Federação das Indústrias do Estado do Rio de Janeiro (Firjan), mostra que essas ocorrências podem elevar os preços dos produtos em até 35%.\n",
        "\n",
        "![alt text](https://www.imagemhost.com.br/images/2020/06/23/numeros-economia-curvas-desk.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1lmGAJoDh4E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "7fe8e2fc-5a77-414f-f244-d5a4bf2ae9f4"
      },
      "source": [
        "df_clean.plot(x='data', y=['tentat_hom','latrocinio','hom_doloso']);"
      ],
      "execution_count": 16,
      "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": "Pql6_DlzS0xM",
        "colab_type": "text"
      },
      "source": [
        "Nesse gráfico podemos observar que a taxa de homicídio doloso tem diminuído nos ultimos anos, porém a tentativa de homicídio tem aumentado em uma relação quase equivalente. O estado tem uma média de 504,4 homicídios dolosos e de 318,2 tentativas de homicídio todo ano. Só em 2018 morreram 4950 pessoas decorrentes de homicídio doloso que representa 10,12% de todos os homicídios dolosos do Brasil. O Rio de Janeiro obteve em 2018 uma taxa de 28,85 mortes para cada 100 mil habitantes, o que é ligeiramente maior que a taxa nacional de 23,38 mortes para cada 100 mil habitantes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBRSGRxEbGkT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "04d7bceb-c18a-4ab7-b3d1-f6084c0162e1"
      },
      "source": [
        "df_clean.plot(x='data', y=['roubo_veiculo','furto_veiculos','recuperacao_veiculos']);"
      ],
      "execution_count": 18,
      "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": "tfX51Fu0ZwVP",
        "colab_type": "text"
      },
      "source": [
        "O que chama bastante a atenção nesse gráfico é o crescimento da taxa de roubo de veiculos a partir do ano de 2014, onde o pico se deu em 2017 e foram registrados mais de 5 mil roubos de veículos no estado do Rio de Janeiro mas o número vem caindo de lá pra cá e isso tem se refletido nos valores dos seguros de carro no estado.\n",
        "\n",
        "![alt text](https://miro.medium.com/max/448/1*IElYsFSFkUZIsZVQG5rOeQ.jpeg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IP8ddPMh1HB",
        "colab_type": "text"
      },
      "source": [
        "# Conclusão\n",
        "\n",
        "Nota-se que [em 2017 houve greve da polícia civil onde 90% das delegacias do estado do Rio de Janeiro ficaram fechadas](https://www.brasildefato.com.br/2017/02/07/90-das-delegacias-do-rio-de-janeiro-estao-em-greve-afirmam-policiais-civis/) e isso comprometeu alguns os registros desse período, entretanto não foi tão significativo para o dados no geral.\n",
        "\n",
        "Dados do 11º Anuário de Segurança Pública, realizado pelo Fórum Brasileiro de Segurança Pública, revelam que 2016 foi o ano mais violento da história do Brasil: 61,6 mil pessoas foram assassinadas no ano passado.\n",
        "\n",
        "Para se ter uma boa ideia, o número de assassinatos cometidos no Brasil em 2016 foi equivalente às mortes provocadas pelas bombas atômicas que dizimaram a cidade de Nagasaki, em 1945, no Japão.\n",
        "\n",
        "Por incrivel que pareça estado do Rio de Janeiro em 2016 não foi o mais violento do Brasil e ficou em 10º lugar no índice de violencia para cada 100 mil habitantes e o estado de Sergipe é quem encabeça a lista. Apesar disso em números absolutos do Rio fica em segundo lugar 6262 mortes, somente atrás do estado da Bahia com 7110 mortes.\n",
        "\n",
        "Os dados mostram que o estado do Rio de Janeiro segue uma tendência nacional de uma leve queda da criminalidade que podemos observar nos ultimos 2 anos. O Brasil ainda está muito longe dos países mais seguros do mundo mas tem melhorando e seguindo uma tendência de diminuição das taxas de criminalidade com o passar dos anos.\n",
        "\n"
      ]
    }
  ]
}