Projeto_Analisando_a_Violência_no_Rio_de_Janeiro.ipynb

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   "source": [
    "<center><img src=\"http://sigmoidal.ai/wp-content/uploads/2019/08/logo_color.png\" height=\"40px\"></center>\n",
    "\n",
    "# Analisando a Violência no Rio de Janeiro\n",
    "\n",
    "A violência no Brasil é um problema persistente que atinge direta ou indiretamente a população. O país tem níveis acima da média mundial no que se refere a crimes violentos, com níveis particularmente altos no tocante a violência armada e homicídios. Em 2016, o Brasil alcançou a marca histórica de 62 517 homicídios, segundo informações do Ministério da Saúde. Isso equivale a uma taxa de 30,3 mortes para cada 100 mil habitantes, uma das mais altas taxas de homicídios intencionais do mundo.O limite considerado como suportável pela Organização Mundial da Saúde (OMS) é de 10 homicídios por 100 mil habitantes."
   ]
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  {
   "cell_type": "markdown",
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    "id": "0zxQHhIUDF_2"
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   "source": [
    "## Obtenção dos Dados\n",
    "\n",
    "O Instituto de Segurança Pública (ISP), criado pela Lei nº 3.329, de 28 de Dezembro 1999, é uma autarquia vinculada diretamente à Vice-Governadoria do Estado. Com 20 anos de existência, o ISP conta com grande conhecimento acumulado no desenvolvimento de metodologias de análise de dados relativos à Segurança Pública. Sua missão é produzir informações e disseminar pesquisas e análises com vistas a influenciar e subsidiar a implementação de políticas públicas de segurança e assegurar a participação social na construção dessas políticas.\n",
    "\n",
    "Ética e transparência da informação são parte dos valores do Instituto: além de divulgar os dados relativos à segurança pública, o ISP realiza análises, projetos e pesquisas regularmente. As pesquisas visam a subsidiar a gestão de segurança pública no estado e o debate público sobre o tema, com o tratamento científico de seus principais problemas.\n",
    "\n",
    "O ISP também é o responsável pela Coordenadoria dos Conselhos Comunitários de Segurança, canal de participação social e de comunicação entre a comunidade e os representantes das forças de segurança estaduais em cada região, na busca comum pela redução da violência.\n",
    "\n",
    "http://www.ispdados.rj.gov.br/estatistica.html"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Importando os dados\n",
    "\n",
    "Importe os dados, usando `df = pd.read_csv(\"endereço_do_csv\", sep=\";\")`\n",
    "\n",
    "Os dados podem ser obtidos a partir do seguinte link:https://raw.githubusercontent.com/carlosfab/curso_data_science_na_pratica/master/modulo_02/violencia_rio.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kd9bzmvAE8gr"
   },
   "outputs": [],
   "source": [
    "# importando pacotes necessários\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Importando os dados já trabalhados, com o preenchimento dos campos nulos.\n",
    "* Utilizando o separador ';' \n",
    "* Indexando o dataframe pelas colunas vano e mes (ano e mes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "uCYVLODhktO0",
    "outputId": "b94f064f-bf8b-4300-d374-761ffbea8d8c"
   },
   "outputs": [],
   "source": [
    "# dados tratados com os campos nulos\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/juliosaopedro/Meus_Projetos/master/violencia_rio.csv\", sep=\";\" , index_col=['vano','mes'])"
   ]
  },
  {
   "attachments": {},
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   "metadata": {
    "colab_type": "text",
    "id": "qb5By2nIMS6m"
   },
   "source": [
    "## Análise Inicial dos Dados\n",
    "\n",
    "Descreva e execute as seguintes etapas:\n",
    "\n",
    "* Qual o tamanho do seu DataFrame (`shape`)\n",
    "* Extrair e imprimir os nomes das colunas (`columns`)\n",
    "  * Definição de algumas colunas\n",
    "  \n",
    "    * aaapai - Apreensão de Adolescente por Prática de Ato Infracional para jovens entre 12 e 17 anos    \n",
    "    * apf - Auto de Prisão em Flagrante\n",
    "    * cmp - Cumprimento de Mandado de Prisão\n",
    "    * cmba - Cumprimento de Mandado de Busca e Apreensão  \n",
    "    \n",
    "* Quais os tipos das variáveis (`dtypes`)\n",
    "* Visualizar as 5 primeiras linhas (`head`)\n",
    "* Identifique a porcentagem de valores ausentes das colunas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variáveis: 344\n",
      "Entradas : 54\n"
     ]
    }
   ],
   "source": [
    "# identificar o volume de dados do DataFrame\n",
    "print(\"Variáveis: {}\".format(df.shape[0]))\n",
    "\n",
    "print(\"Entradas : {}\".format(df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
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    },
    "colab_type": "code",
    "id": "vFwenzu7GVyE",
    "outputId": "0c09e867-f331-4fd1-855f-e2bdefca32a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['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')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exibindo as colunas\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QtqgvTyrYbbC"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tipos das colunas\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></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>...</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",
       "    <tr>\n",
       "      <th>vano</th>\n",
       "      <th>mes</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1991</th>\n",
       "      <th>1</th>\n",
       "      <td>657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162</td>\n",
       "      <td>3051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>217</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>672</td>\n",
       "      <td>1348</td>\n",
       "      <td>1174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175</td>\n",
       "      <td>3421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357</td>\n",
       "      <td>...</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>3</th>\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>...</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>4</th>\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>...</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>5</th>\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>...</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",
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       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hom_doloso  lesao_corp_morte  latrocinio  hom_por_interv_policial  \\\n",
       "vano mes                                                                      \n",
       "1991 1           657               NaN          15                      NaN   \n",
       "     2           732               NaN          17                      NaN   \n",
       "     3           713               NaN          25                      NaN   \n",
       "     4           634               NaN          20                      NaN   \n",
       "     5           650               NaN          20                      NaN   \n",
       "\n",
       "          tentat_hom  lesao_corp_dolosa  estupro  hom_culposo  \\\n",
       "vano mes                                                        \n",
       "1991 1           162               3051      NaN          NaN   \n",
       "     2           175               3421      NaN          NaN   \n",
       "     3           216               3613      NaN          NaN   \n",
       "     4           200               3211      NaN          NaN   \n",
       "     5           146               3051      NaN          NaN   \n",
       "\n",
       "          lesao_corp_culposa  roubo_comercio  ...  pessoas_desaparecidas  \\\n",
       "vano mes                                      ...                          \n",
       "1991 1                   NaN             350  ...                    NaN   \n",
       "     2                   NaN             357  ...                    NaN   \n",
       "     3                   NaN             443  ...                    NaN   \n",
       "     4                   NaN             425  ...                    NaN   \n",
       "     5                   NaN             553  ...                    NaN   \n",
       "\n",
       "          encontro_cadaver  encontro_ossada  pol_militares_mortos_serv  \\\n",
       "vano mes                                                                 \n",
       "1991 1                 217              NaN                        NaN   \n",
       "     2                 209              NaN                        NaN   \n",
       "     3                 188              NaN                        NaN   \n",
       "     4                 140              NaN                        NaN   \n",
       "     5                  78              NaN                        NaN   \n",
       "\n",
       "          pol_civis_mortos_serv  indicador_letalidade  indicador_roubo_rua  \\\n",
       "vano mes                                                                     \n",
       "1991 1                      NaN                   672                 1348   \n",
       "     2                      NaN                   749                 1395   \n",
       "     3                      NaN                   738                 1385   \n",
       "     4                      NaN                   654                 1540   \n",
       "     5                      NaN                   670                 1266   \n",
       "\n",
       "          indicador_roubo_veic  registro_ocorrencias  fase  \n",
       "vano mes                                                    \n",
       "1991 1                    1174                   NaN     3  \n",
       "     2                    1097                   NaN     3  \n",
       "     3                    1265                   NaN     3  \n",
       "     4                    1415                   NaN     3  \n",
       "     5                    1449                   NaN     3  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exibindo as 5 primeiras linhas\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 952
    },
    "colab_type": "code",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roubo_bicicleta               80.232558\n",
       "furto_bicicleta               80.232558\n",
       "apreensao_drogas_sem_autor    52.325581\n",
       "posse_drogas                  52.325581\n",
       "apf                           52.325581\n",
       "aaapai                        52.325581\n",
       "cmp                           52.325581\n",
       "trafico_drogas                52.325581\n",
       "cmba                          52.325581\n",
       "estelionato                   41.860465\n",
       "furto_celular                 41.860465\n",
       "sequestro_relampago           41.860465\n",
       "furto_coletivo                41.860465\n",
       "roubo_apos_saque              41.860465\n",
       "roubo_conducao_saque          41.860465\n",
       "roubo_cx_eletronico           41.860465\n",
       "extorsao                      41.860465\n",
       "estupro                       41.860465\n",
       "pol_militares_mortos_serv     41.860465\n",
       "pol_civis_mortos_serv         41.860465\n",
       "pessoas_desaparecidas         38.372093\n",
       "encontro_ossada               38.372093\n",
       "roubo_celular                 31.395349\n",
       "ameaca                        27.906977\n",
       "lesao_corp_morte              27.906977\n",
       "sequestro                     27.906977\n",
       "lesao_corp_culposa            27.906977\n",
       "registro_ocorrencias          27.906977\n",
       "hom_culposo                   24.418605\n",
       "hom_por_interv_policial       24.418605\n",
       "roubo_carga                    0.000000\n",
       "latrocinio                     0.000000\n",
       "lesao_corp_dolosa              0.000000\n",
       "roubo_veiculo                  0.000000\n",
       "roubo_residencia               0.000000\n",
       "roubo_comercio                 0.000000\n",
       "roubo_em_coletivo              0.000000\n",
       "tentat_hom                     0.000000\n",
       "roubo_transeunte               0.000000\n",
       "fase                           0.000000\n",
       "roubo_banco                    0.000000\n",
       "outros_roubos                  0.000000\n",
       "total_roubos                   0.000000\n",
       "furto_veiculos                 0.000000\n",
       "furto_transeunte               0.000000\n",
       "outros_furtos                  0.000000\n",
       "total_furtos                   0.000000\n",
       "apreensao_drogas               0.000000\n",
       "recuperacao_veiculos           0.000000\n",
       "encontro_cadaver               0.000000\n",
       "indicador_letalidade           0.000000\n",
       "indicador_roubo_rua            0.000000\n",
       "indicador_roubo_veic           0.000000\n",
       "hom_doloso                     0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# porcentagem de valores nulos\n",
    "(df.isnull().sum() / df.shape[0] * 100).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G6dnsNYm-41g"
   },
   "source": [
    "## Informações Estatísticas da Violência no Rio de Janeiro\n",
    "\n",
    "As informações aqui apresentadas cobrem dados do período de Janeiro de 1991 a Agosto de 2019.\n",
    "\n",
    "Descreva e execute as seguintes etapas:\n",
    "\n",
    "* Imprima o resumo estatístico do seu DataFrame (`describe`)\n",
    "* Encontre as médias das seguintes variáveis (colunas):\n",
    "  * `roubo_veiculo`\n",
    "  * `furto_veiculos`\n",
    "  * `recuperacao_veiculos`\n",
    "* Calcule qual a porcentagem de carros recuperados em relação aos carros roubados + carros furtados:\n",
    "  * $\\frac{ \\text{recuperacao_veiculos}}{\\text{roubo_veiculo} + \\text{furto_veiculos}}$\n",
    "* Encontre os valores máximos (`max`) e mínimos (`min`) da coluna `hom_doloso`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H0WOsZvVGcyF",
    "scrolled": true
   },
   "outputs": [
    {
     "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>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>...</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>count</th>\n",
       "      <td>344.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>260.00000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.00000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>344.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>504.555233</td>\n",
       "      <td>4.334677</td>\n",
       "      <td>15.668605</td>\n",
       "      <td>71.90000</td>\n",
       "      <td>318.267442</td>\n",
       "      <td>5492.799419</td>\n",
       "      <td>369.945000</td>\n",
       "      <td>212.119231</td>\n",
       "      <td>3410.762097</td>\n",
       "      <td>455.005814</td>\n",
       "      <td>...</td>\n",
       "      <td>435.132075</td>\n",
       "      <td>66.261628</td>\n",
       "      <td>3.301887</td>\n",
       "      <td>2.085000</td>\n",
       "      <td>0.34000</td>\n",
       "      <td>577.691860</td>\n",
       "      <td>4734.738372</td>\n",
       "      <td>2448.697674</td>\n",
       "      <td>53794.161290</td>\n",
       "      <td>2.994186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>113.774481</td>\n",
       "      <td>2.755357</td>\n",
       "      <td>5.861427</td>\n",
       "      <td>36.48882</td>\n",
       "      <td>127.606645</td>\n",
       "      <td>1514.220579</td>\n",
       "      <td>94.392434</td>\n",
       "      <td>54.234700</td>\n",
       "      <td>776.156561</td>\n",
       "      <td>119.765076</td>\n",
       "      <td>...</td>\n",
       "      <td>65.353516</td>\n",
       "      <td>34.464519</td>\n",
       "      <td>2.192922</td>\n",
       "      <td>1.761616</td>\n",
       "      <td>0.63752</td>\n",
       "      <td>104.973929</td>\n",
       "      <td>3204.054864</td>\n",
       "      <td>789.689559</td>\n",
       "      <td>11039.051992</td>\n",
       "      <td>0.076138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>272.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>2350.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>1097.000000</td>\n",
       "      <td>30621.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>413.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>41.75000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>4067.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>3040.000000</td>\n",
       "      <td>365.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1557.000000</td>\n",
       "      <td>1889.750000</td>\n",
       "      <td>45374.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>507.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>318.500000</td>\n",
       "      <td>5830.500000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>3372.000000</td>\n",
       "      <td>433.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>4190.000000</td>\n",
       "      <td>2322.000000</td>\n",
       "      <td>54926.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>577.250000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>96.25000</td>\n",
       "      <td>398.250000</td>\n",
       "      <td>6582.500000</td>\n",
       "      <td>441.250000</td>\n",
       "      <td>236.250000</td>\n",
       "      <td>3733.500000</td>\n",
       "      <td>536.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>481.500000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>6872.750000</td>\n",
       "      <td>2778.000000</td>\n",
       "      <td>63292.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>831.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>194.00000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>9050.000000</td>\n",
       "      <td>561.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>6653.000000</td>\n",
       "      <td>914.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>13833.000000</td>\n",
       "      <td>5358.000000</td>\n",
       "      <td>75403.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hom_doloso  lesao_corp_morte  latrocinio  hom_por_interv_policial  \\\n",
       "count  344.000000        248.000000  344.000000                260.00000   \n",
       "mean   504.555233          4.334677   15.668605                 71.90000   \n",
       "std    113.774481          2.755357    5.861427                 36.48882   \n",
       "min    272.000000          0.000000    2.000000                 10.00000   \n",
       "25%    413.750000          2.000000   11.000000                 41.75000   \n",
       "50%    507.000000          4.000000   15.000000                 70.00000   \n",
       "75%    577.250000          6.000000   19.000000                 96.25000   \n",
       "max    831.000000         21.000000   36.000000                194.00000   \n",
       "\n",
       "       tentat_hom  lesao_corp_dolosa     estupro  hom_culposo  \\\n",
       "count  344.000000         344.000000  200.000000   260.000000   \n",
       "mean   318.267442        5492.799419  369.945000   212.119231   \n",
       "std    127.606645        1514.220579   94.392434    54.234700   \n",
       "min    122.000000        2350.000000  188.000000   111.000000   \n",
       "25%    200.000000        4067.000000  282.000000   176.000000   \n",
       "50%    318.500000        5830.500000  384.000000   203.000000   \n",
       "75%    398.250000        6582.500000  441.250000   236.250000   \n",
       "max    645.000000        9050.000000  561.000000   449.000000   \n",
       "\n",
       "       lesao_corp_culposa  roubo_comercio  ...  pessoas_desaparecidas  \\\n",
       "count          248.000000      344.000000  ...             212.000000   \n",
       "mean          3410.762097      455.005814  ...             435.132075   \n",
       "std            776.156561      119.765076  ...              65.353516   \n",
       "min            848.000000      243.000000  ...             236.000000   \n",
       "25%           3040.000000      365.750000  ...             390.000000   \n",
       "50%           3372.000000      433.500000  ...             427.000000   \n",
       "75%           3733.500000      536.250000  ...             481.500000   \n",
       "max           6653.000000      914.000000  ...             628.000000   \n",
       "\n",
       "       encontro_cadaver  encontro_ossada  pol_militares_mortos_serv  \\\n",
       "count        344.000000       212.000000                 200.000000   \n",
       "mean          66.261628         3.301887                   2.085000   \n",
       "std           34.464519         2.192922                   1.761616   \n",
       "min            0.000000         0.000000                   0.000000   \n",
       "25%           42.000000         2.000000                   1.000000   \n",
       "50%           60.500000         3.000000                   2.000000   \n",
       "75%           87.000000         4.000000                   3.000000   \n",
       "max          217.000000        13.000000                  10.000000   \n",
       "\n",
       "       pol_civis_mortos_serv  indicador_letalidade  indicador_roubo_rua  \\\n",
       "count              200.00000            344.000000           344.000000   \n",
       "mean                 0.34000            577.691860          4734.738372   \n",
       "std                  0.63752            104.973929          3204.054864   \n",
       "min                  0.00000            334.000000          1086.000000   \n",
       "25%                  0.00000            500.000000          1557.000000   \n",
       "50%                  0.00000            590.000000          4190.000000   \n",
       "75%                  1.00000            649.000000          6872.750000   \n",
       "max                  3.00000            848.000000         13833.000000   \n",
       "\n",
       "       indicador_roubo_veic  registro_ocorrencias        fase  \n",
       "count            344.000000            248.000000  344.000000  \n",
       "mean            2448.697674          53794.161290    2.994186  \n",
       "std              789.689559          11039.051992    0.076138  \n",
       "min             1097.000000          30621.000000    2.000000  \n",
       "25%             1889.750000          45374.500000    3.000000  \n",
       "50%             2322.000000          54926.500000    3.000000  \n",
       "75%             2778.000000          63292.500000    3.000000  \n",
       "max             5358.000000          75403.000000    3.000000  \n",
       "\n",
       "[8 rows x 54 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resumo estatistico do dataframe\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ppRh0-rdcBta"
   },
   "source": [
    "## Veículos\n",
    "Roubos, Furtos e Recuperação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EUTJ2PZGGiVP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2448.6976744186045"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# media de roubo de veiculos\n",
    "df.roubo_veiculo.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8u9QljcrG4ns"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1674.671511627907"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# media de furto de veiculos\n",
    "df.furto_veiculos.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "61ou6-4SG6T7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1772.2383720930231"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# media da recuperaçao de veiculos\n",
    "df.recuperacao_veiculos.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "i0CNBs5EHC_6",
    "outputId": "7ecf42d4-fb8a-4076-a0c7-db9f0f4efd0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42980346705075084"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# porcentagem de veiculos recuperados em relacao aos roubos e furtos\n",
    "df.recuperacao_veiculos.sum() / (df.roubo_veiculo.sum() + df.furto_veiculos.sum()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.326644796194538"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.roubo_veiculo.sum() + df.furto_veiculos.sum()) / df.recuperacao_veiculos.sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "afVSRgGWbd2g"
   },
   "source": [
    "## Homicidio Doloso\n",
    "O homicídio doloso é quando uma pessoa mata outra intencionalmente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ed40z3OsISkq",
    "outputId": "b93d0bc4-c64d-4bc2-ff2f-2399a28aa5e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# valor minimo de homicidio doloso\n",
    "df.hom_doloso.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "n9q_rC6mTtNQ",
    "outputId": "35caf16c-6cbf-442c-fe5e-a7c451476cc0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "504.55523255813955"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hom_doloso.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kgHweP6mIeDu"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "831"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# valor maximo de homicidio doloso\n",
    "df.hom_doloso.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7WK0NBQZASKC"
   },
   "source": [
    "## Visualização de Dados\n",
    "\n",
    "Todos os dados aqui apresentados estão de acordo com a Lei de Acesso à Informação, objetivando maior transparência no que diz respeito às estatísticas de criminalidade e atividade policial do estado.(Instituto de Segurança Pública do Rio de Janeiro)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exibindo as colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['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')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importando o pacote 'Seaborn' para visualizaçao de dados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>hom_doloso</th>\n",
       "      <th>roubo_residencia</th>\n",
       "      <th>latrocinio</th>\n",
       "      <th>roubo_veiculo</th>\n",
       "      <th>furto_veiculos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hom_doloso</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.558434</td>\n",
       "      <td>0.350517</td>\n",
       "      <td>-0.189194</td>\n",
       "      <td>0.705769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roubo_residencia</th>\n",
       "      <td>0.558434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.442688</td>\n",
       "      <td>-0.092299</td>\n",
       "      <td>0.698854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latrocinio</th>\n",
       "      <td>0.350517</td>\n",
       "      <td>0.442688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197046</td>\n",
       "      <td>0.322337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roubo_veiculo</th>\n",
       "      <td>-0.189194</td>\n",
       "      <td>-0.092299</td>\n",
       "      <td>0.197046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.312560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>furto_veiculos</th>\n",
       "      <td>0.705769</td>\n",
       "      <td>0.698854</td>\n",
       "      <td>0.322337</td>\n",
       "      <td>-0.312560</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  hom_doloso  roubo_residencia  latrocinio  roubo_veiculo  \\\n",
       "hom_doloso          1.000000          0.558434    0.350517      -0.189194   \n",
       "roubo_residencia    0.558434          1.000000    0.442688      -0.092299   \n",
       "latrocinio          0.350517          0.442688    1.000000       0.197046   \n",
       "roubo_veiculo      -0.189194         -0.092299    0.197046       1.000000   \n",
       "furto_veiculos      0.705769          0.698854    0.322337      -0.312560   \n",
       "\n",
       "                  furto_veiculos  \n",
       "hom_doloso              0.705769  \n",
       "roubo_residencia        0.698854  \n",
       "latrocinio              0.322337  \n",
       "roubo_veiculo          -0.312560  \n",
       "furto_veiculos          1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn é um pacote para visualização de dados Python baseada em matplotlib\n",
    "import seaborn as sbs \n",
    "\n",
    "# criar uma matriz de correlação para identificar alguma relação entre os dados\n",
    "corr = df[['hom_doloso', 'roubo_residencia', 'latrocinio', 'roubo_veiculo', 'furto_veiculos']].corr()\n",
    "\n",
    "# exibir de correlação\n",
    "display(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando um heatmap (mapa de calor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotar um heatmap (mapa de calor) para verificar de forma visual a relação entre os dados \n",
    "sbs.heatmap(corr, cmap='RdBu', fmt='.2f', square=True, linecolor='white', annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "dX82uj_4IxrF",
    "outputId": "a9687ad0-a9c4-4c38-baff-6a5b96a44c05"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograma de Homicidio Doloso\n",
    "df['hom_doloso'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráfico "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "Gtjd2fIDT0om",
    "outputId": "3ee112ec-6bcf-44af-f2f0-a7ab341e4332"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grafico de linhas para Homicidio Doloso por quantida por ano/mes\n",
    "df.hom_doloso.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3O6ydOqub3SV"
   },
   "source": [
    "## Roubo em Coletivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "a7zPdlsoJgff",
    "outputId": "78df59a8-d80c-47cd-aaa0-a2bc75195443"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de linhas \n",
    "df.roubo_em_coletivo.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LCsiXvM3BcsO"
   },
   "source": [
    "## Conclusão\n",
    "\n",
    "Análise realizada para o segundo exercício do curso Data Science na Prática do mestre Carlos Melo. \n",
    "\n",
    "Após uma análise superficial, obtive alguns insights que descrevo abaixo.\n",
    "\n",
    "\n",
    "* O percentual de recuperação de veiculos roubados ou furtados foi de 42% no período de Jan/1991 a Ago/2019.\n",
    "\n",
    "* O número de homicídios dolosos, apesar de ainda ser alto, vem diminuindo. Em contrapartida, os roubos a coletivos vêm aumentando.\n",
    "\n",
    "* Os dados referentes a roubo e furto de bicicleta, temos apenas informação para 20% dos casos.\n",
    "* Podemos observar um correlação entre Homicídios Dolosos, Roubo de Residência e Furto de Veículos \n",
    "\n"
   ]
  }
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