Analisando a Violência no Rio de Janeiro - Veículos.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Analisando a Violência no Rio de Janeiro - Veículos.ipynb","provenance":[{"file_id":"1pBHe7TaTjBgtI4x-ReQIkG_i2kxEMkMh","timestamp":1583849446317},{"file_id":"https://github.com/carlosfab/curso_data_science_na_pratica/blob/master/modulo_02/PROJETO_Analisando_a_Viol%C3%AAncia_no_Rio_de_Janeirob.ipynb","timestamp":1575881866356}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"kLI0gJtzDGuQ","colab_type":"text"},"source":["# Analisando a Violência no Rio de Janeiro\n","\n","# Roubo e furto de veículos\n","\n"]},{"cell_type":"markdown","metadata":{"id":"nVavnpYDoREa","colab_type":"text"},"source":["## Entendendo o problema\n","\n","O Estado do Rio de Janeiro é um destino turístico conhecido internacionalme, famoso pelas praias, música, cultura e infelizmente, pela sua reputação no que diz respeito à segurança. \n","\n","Para se sentirem mais seguros, a opção de usar um automóvel parece ser mais atraente em relação à se locomover por tranporte público em algumas regiões.\n","\n","Entretanto, possuir um automóvel cria a possibilidade que ele seja subtraido de forma indesejada, em outras palavras, para ter um carro roubado, você precisa ter um carro! \n","\n","Caso um morador do Estado do Rio de Janeiro esteja pensando em ter um (ou se já possui um) automóvel, pode ter algumas dúvidas em relação aos dados de roubo de veículos."]},{"cell_type":"markdown","metadata":{"id":"qMO1nh2Ft2GC","colab_type":"text"},"source":["## Questões a serem repondidas\n","\n","Levantaremos algumas perguntas que podem ser respondidas com a análise esploratória de dados da segurança pública do Estado do Rio de Janeiro.\n","\n","Antes de começar, vamos entender alguns termos:\n","\n","* __Furto:__ \"_É a subtração de algo móvel pertencente a outra pessoa para si ou para outrem. Caracteriza-se pela ação de tirar de outra pessoa algo móvel que lhe pertença, sem a sua permissão, com o objetivo de domínio definitivo do bem.\"_ [ref](https://alvesaraujoadv.jusbrasil.com.br/artigos/447365236/qual-a-diferenca-entre-furto-e-roubo)\n","\n","* __Roubo:__ _\" É considerado um crime mais grave do que o furto, isto porque a ação principal é a mesma, ou seja, tirar de alguém um bem móvel. Contudo, no roubo para a subtração do bem há o emprego de grave ameaça ou violência contra a pessoa, ou por qualquer outro meio que reduza a capacidade de resistência da vítima.\"_ [ref](https://alvesaraujoadv.jusbrasil.com.br/artigos/447365236/qual-a-diferenca-entre-furto-e-roubo)\n","\n","Depois de entender melhor os termos técnicos jurídicos, vamos as dúvidas:\n","\n","1. Os índices de roubo e furto de veículos, vem aumentando ao longo dos anos?\n","2. Qual época do ano são registrados mais roubos e furtos de veículos?\n","3. Em qual região do Estado do Rio de Janeiro são registrados mais roubos e furtos de veículos?\n"]},{"cell_type":"markdown","metadata":{"id":"0zxQHhIUDF_2","colab_type":"text"},"source":["## Obtenção dos Dados\n","\n","Os dados foram obtidos do site do Intituto de Segurança Pública do Governo do Estado do Rio de Janeiro [ISP - Dados Abertos](http://www.ispdados.rj.gov.br).\n","\n","Esse portal contém dados de segurança pública do Estado do Rio de Janeiro assim como um [_dashboard_](https://www.ispvisualizacao.rj.gov.br:4434/index.html) interativo muito interessante desenvolvido no _Tableau_."]},{"cell_type":"markdown","metadata":{"id":"7vh4at0l90nr","colab_type":"text"},"source":["### Importando os dados"]},{"cell_type":"code","metadata":{"id":"CYLNme_OCX_R","colab_type":"code","outputId":"b7d8c45f-e813-45cd-8f7a-76a166a7f612","executionInfo":{"status":"ok","timestamp":1584440270851,"user_tz":180,"elapsed":3769,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":71}},"source":["# Carregar pacote\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","%matplotlib inline\n","\n","# Ler arquivo csv\n","df = pd.read_csv(\n","    'http://www.ispdados.rj.gov.br/Arquivos/BaseDPEvolucaoMensalCisp.csv',\n","    encoding='latin-1', # artifício para a leitura de caracteres com acentuação\n","    sep=';', # O separadaor desse dataset é ';' -> comum no Brasil, pois usamos ',' como separador de decimal\n","    )"],"execution_count":77,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (26,30,37,45,46,47,49,50,51,52) have mixed types. Specify dtype option on import or set low_memory=False.\n","  interactivity=interactivity, compiler=compiler, result=result)\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"qb5By2nIMS6m","colab_type":"text"},"source":["## Análise Inicial e preparação dos Dados"]},{"cell_type":"markdown","metadata":{"id":"S0gYB-hcv559","colab_type":"text"},"source":["### Tamanho do dataset\n"]},{"cell_type":"code","metadata":{"id":"bzs2oHtovtZv","colab_type":"code","outputId":"a02a9751-750b-4b0b-d088-119145b09cfc","executionInfo":{"status":"ok","timestamp":1584440270853,"user_tz":180,"elapsed":3754,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["print(f'Número de limhas: {df.shape[0]}')\n","print(f'Número de colunas: {df.shape[1]}')"],"execution_count":78,"outputs":[{"output_type":"stream","text":["Número de limhas: 27450\n","Número de colunas: 65\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"h5B4piuSyI5_","colab_type":"text"},"source":["### Nome das colunas"]},{"cell_type":"code","metadata":{"id":"v4kGTLERyfAV","colab_type":"code","outputId":"29ca29a4-a61d-4e2e-e1b1-afb2f5e796c3","executionInfo":{"status":"ok","timestamp":1584440270854,"user_tz":180,"elapsed":3729,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":323}},"source":["df.columns"],"execution_count":79,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['CISP', 'mes', 'vano', 'mes_ano', 'AISP', 'RISP', 'munic', 'mcirc',\n","       'Regiao', 'hom_doloso', 'lesao_corp_morte', 'latrocinio',\n","       'hom_por_interv_policial', 'letalidade_violenta', 'tentat_hom',\n","       'lesao_corp_dolosa', 'estupro', 'hom_culposo', 'lesao_corp_culposa',\n","       'roubo_comercio', 'roubo_residencia', 'roubo_veiculo', 'roubo_carga',\n","       'roubo_transeunte', 'roubo_em_coletivo', 'roubo_banco',\n","       'roubo_cx_eletronico', 'roubo_celular', 'roubo_conducao_saque',\n","       'roubo_apos_saque', 'roubo_bicicleta', 'outros_roubos', 'total_roubos',\n","       'furto_veiculos', 'furto_transeunte', 'furto_coletivo', 'furto_celular',\n","       'furto_bicicleta', 'outros_furtos', 'total_furtos', 'sequestro',\n","       'extorsao', 'sequestro_relampago', 'estelionato', 'apreensao_drogas',\n","       'posse_drogas', 'trafico_drogas', 'apreensao_drogas_sem_autor',\n","       'recuperacao_veiculos', 'apf', 'aaapai', 'cmp', 'cmba', 'ameaca',\n","       'pessoas_desaparecidas', 'encontro_cadaver', 'encontro_ossada',\n","       'pol_militares_mortos_serv', 'pol_civis_mortos_serv', 'indicador_cvli',\n","       'indicador_roubo_rua', 'indicador_roubo_veic', 'indicador_roubo_carga',\n","       'registro_ocorrencias', 'fase'],\n","      dtype='object')"]},"metadata":{"tags":[]},"execution_count":79}]},{"cell_type":"markdown","metadata":{"id":"DBlOjgPByopz","colab_type":"text"},"source":["### Dicionário de variáveis com as colunas importantes\n","\n","* `mes`: Mês da comunicação da ocorrência\n","* `vano`: Ano da comunicação da ocorrência\n","* `Regiao`: 1-Baixada; 2-Capital; 3-Grande Niterói; 4-Interior\n","* `roubo_veiculo`: Roubo de veículo\n","* `total_roubos`: Total de roubos (incluindo veículos)\n","* `furto_veiculos`: Furto de veículo\n","* `total_furtos`: Total de furtos (incluindo veículos)\n"]},{"cell_type":"code","metadata":{"id":"PCckB_LF4otq","colab_type":"code","colab":{}},"source":["# Limpeza do dataset\n","df = df.loc[:,['mes', 'vano', 'Regiao', 'roubo_veiculo', 'total_roubos', 'furto_veiculos', 'total_furtos']]\n","\n","# Reirar dados de 2020 (incompleto)\n","df = df[df['vano'] != 2020]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sJjRrodw8_CM","colab_type":"code","outputId":"31358ab3-80b4-44f5-d62e-cc7561185da6","executionInfo":{"status":"ok","timestamp":1584440270857,"user_tz":180,"elapsed":3654,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["print(f'Número de limhas: {df.shape[0]}')\n","print(f'Número de colunas: {df.shape[1]}')"],"execution_count":81,"outputs":[{"output_type":"stream","text":["Número de limhas: 27313\n","Número de colunas: 7\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9Sx8EChK-AM-","colab_type":"text"},"source":["### Valores faltantes"]},{"cell_type":"code","metadata":{"id":"8mSVfxAt9Gnk","colab_type":"code","outputId":"8c13486c-dd07-4bd1-85ff-e88c1ea1bc1f","executionInfo":{"status":"ok","timestamp":1584440270858,"user_tz":180,"elapsed":3631,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":153}},"source":["df.isnull().sum()"],"execution_count":82,"outputs":[{"output_type":"execute_result","data":{"text/plain":["mes               0\n","vano              0\n","Regiao            0\n","roubo_veiculo     0\n","total_roubos      0\n","furto_veiculos    0\n","total_furtos      0\n","dtype: int64"]},"metadata":{"tags":[]},"execution_count":82}]},{"cell_type":"markdown","metadata":{"id":"ks4HBOgrCQJH","colab_type":"text"},"source":["### Visualização das primeiras linhas"]},{"cell_type":"code","metadata":{"id":"90NnnEc_CV1Q","colab_type":"code","outputId":"c90e71b3-5cc4-4665-f19c-5ebca37a3e0e","executionInfo":{"status":"ok","timestamp":1584440270859,"user_tz":180,"elapsed":3582,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["df.head()"],"execution_count":83,"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>mes</th>\n","      <th>vano</th>\n","      <th>Regiao</th>\n","      <th>roubo_veiculo</th>\n","      <th>total_roubos</th>\n","      <th>furto_veiculos</th>\n","      <th>total_furtos</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2003</td>\n","      <td>Capital</td>\n","      <td>5</td>\n","      <td>100</td>\n","      <td>12</td>\n","      <td>169</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2003</td>\n","      <td>Capital</td>\n","      <td>9</td>\n","      <td>82</td>\n","      <td>9</td>\n","      <td>115</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2003</td>\n","      <td>Capital</td>\n","      <td>11</td>\n","      <td>123</td>\n","      <td>28</td>\n","      <td>216</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2003</td>\n","      <td>Capital</td>\n","      <td>27</td>\n","      <td>143</td>\n","      <td>17</td>\n","      <td>90</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2003</td>\n","      <td>Capital</td>\n","      <td>23</td>\n","      <td>55</td>\n","      <td>12</td>\n","      <td>35</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   mes  vano   Regiao  ...  total_roubos  furto_veiculos  total_furtos\n","0    1  2003  Capital  ...           100              12           169\n","1    1  2003  Capital  ...            82               9           115\n","2    1  2003  Capital  ...           123              28           216\n","3    1  2003  Capital  ...           143              17            90\n","4    1  2003  Capital  ...            55              12            35\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":83}]},{"cell_type":"markdown","metadata":{"id":"6DdkMcYvE-cU","colab_type":"text"},"source":["## Respondendo às perguntas"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"JRK5z72WovpK"},"source":["### 1. Os índices de roubo e furto de veículos, vem aumentando ao longo dos anos?"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"U3pfA5dhovpO"},"source":["#### 1.1 Roubos em relação ao tempo"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"eatX7qI9FkYB"},"source":["__Total de roubos por ano__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"228894e4-5f41-40ef-f79d-bc95f577bead","executionInfo":{"status":"ok","timestamp":1584440270859,"user_tz":180,"elapsed":3556,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"6IHCESJYFkFj","colab":{"base_uri":"https://localhost:8080/","height":425}},"source":["roubos = pd.DataFrame(df.groupby(['vano']).total_roubos.sum()/1000)\n","media = roubos.total_roubos.mean()\n","maximo = roubos.total_roubos.max()\n","ano = int(roubos.index[roubos.total_roubos == maximo].values)\n","print(roubos)\n","print()\n","print('Total de roubos')\n","print('-'*60)\n","print(f'Média de roubos por ano: {media:.2f} mil')\n","print(f'O pior ano foi de {ano} com {maximo:.2f} mil roubos')"],"execution_count":84,"outputs":[{"output_type":"stream","text":["      total_roubos\n","vano              \n","2003       118.834\n","2004       110.903\n","2005       113.437\n","2006       124.087\n","2007       137.422\n","2008       141.175\n","2009       138.280\n","2010       120.300\n","2011       106.688\n","2012       103.775\n","2013       126.190\n","2014       157.995\n","2015       147.933\n","2016       208.781\n","2017       230.437\n","2018       231.606\n","2019       199.347\n","\n","Total de roubos\n","------------------------------------------------------------\n","Média de roubos por ano: 148.07 mil\n","O pior ano foi de 2018 com 231.61 mil roubos\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"w_STfW3hovpV"},"source":["__Total de roubos de veículos por ano__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"a00bb810-eff7-4d65-a226-3ea1f5d0716f","executionInfo":{"status":"ok","timestamp":1584440270860,"user_tz":180,"elapsed":3545,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"G_WfoWooovpW","colab":{"base_uri":"https://localhost:8080/","height":442}},"source":["roubos = pd.DataFrame(df.groupby(['vano']).roubo_veiculo.sum()/1000)\n","media = roubos.roubo_veiculo.mean()\n","maximo = roubos.roubo_veiculo.max()\n","ano = int(roubos.index[roubos.roubo_veiculo == maximo].values)\n","print(roubos)\n","print()\n","print('Roubo de veículos')\n","print('-'*60)\n","print(f'Média de roubos por ano: {media:.2f} mil')\n","print(f'O pior ano foi de {ano} com {maximo:.2f} mil roubos')\n","print(f'Com {maximo-media:.2f} mil roubos acima da média ({(maximo-media)/media*100:.2f}%)')"],"execution_count":85,"outputs":[{"output_type":"stream","text":["      roubo_veiculo\n","vano               \n","2003         33.531\n","2004         32.408\n","2005         32.690\n","2006         34.324\n","2007         31.490\n","2008         27.847\n","2009         25.036\n","2010         20.052\n","2011         18.773\n","2012         22.065\n","2013         28.002\n","2014         32.651\n","2015         31.035\n","2016         41.696\n","2017         54.366\n","2018         52.097\n","2019         39.749\n","\n","Roubo de veículos\n","------------------------------------------------------------\n","Média de roubos por ano: 32.81 mil\n","O pior ano foi de 2017 com 54.37 mil roubos\n","Com 21.55 mil roubos acima da média (65.69%)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"Vucz-0vjovpb"},"source":["__Proporção de roubo de veículos em relação ao total de roubos registrados__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"c5ca4745-03cb-47c9-d6e1-556a446ad9bd","executionInfo":{"status":"ok","timestamp":1584440270860,"user_tz":180,"elapsed":3534,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"GZd-785zovpc","colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["(df.groupby(['vano']).roubo_veiculo.sum() / df.groupby(['vano']).total_roubos.sum())"],"execution_count":86,"outputs":[{"output_type":"execute_result","data":{"text/plain":["vano\n","2003    0.282167\n","2004    0.292219\n","2005    0.288178\n","2006    0.276612\n","2007    0.229148\n","2008    0.197252\n","2009    0.181053\n","2010    0.166683\n","2011    0.175962\n","2012    0.212623\n","2013    0.221903\n","2014    0.206658\n","2015    0.209791\n","2016    0.199712\n","2017    0.235926\n","2018    0.224938\n","2019    0.199396\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":86}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"OB7pY05Movpe"},"source":["> A média da proporção de roubos de veículos em relação ao total de roubos foi de __22%__."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"JPWvV8ARovpf"},"source":["__Visualização dos roubos em relação ao tempo__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"69faf0c5-d7bc-4fd8-c812-9e060bab6ef0","executionInfo":{"status":"ok","timestamp":1584440271835,"user_tz":180,"elapsed":4495,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"KnqcBZplovpg","colab":{"base_uri":"https://localhost:8080/","height":297}},"source":["plt.plot(df.groupby(['vano']).roubo_veiculo.sum()/1000)\n","plt.plot(df.groupby(['vano']).total_roubos.sum()/1000)\n","plt.title('Contagem de roubos em relação ao tempo')\n","plt.ylabel('Roubos por 1000')\n","plt.legend(['Roubo de veículos', 'Total de roubos'])\n","plt.tight_layout()"],"execution_count":87,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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LWgUPl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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"K1i-0NI5ovpi"},"source":["> Analizando o gráfico de forma subjetiva, o número de ocorrências de roubo de veículos não parece estar aumentando ao longo dos anos."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"4OtThZq2ovpj"},"source":["#### 1.2 Furtos em relação ao tempo"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"ZMImUcapovpj"},"source":["__Total de furtos por ano__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"b030e918-2a6a-4f45-844d-b14ab7cbe7e9","executionInfo":{"status":"ok","timestamp":1584440271836,"user_tz":180,"elapsed":4471,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"RVHLMS0jovpk","colab":{"base_uri":"https://localhost:8080/","height":425}},"source":["furtos = pd.DataFrame(df.groupby(['vano']).total_furtos.sum()/1000)\n","media = furtos.total_furtos.mean()\n","maximo = furtos.total_furtos.max()\n","ano = int(furtos.index[furtos.total_furtos == maximo].values)\n","print(furtos)\n","print()\n","print('Total de furtos')\n","print('-'*60)\n","print(f'Média de furtos por ano: {media:.2f} mil')\n","print(f'O pior ano foi de {ano} com {maximo:.2f} mil furtos')"],"execution_count":88,"outputs":[{"output_type":"stream","text":["      total_furtos\n","vano              \n","2003       119.533\n","2004       118.770\n","2005       126.333\n","2006       140.874\n","2007       157.150\n","2008       168.945\n","2009       170.245\n","2010       174.776\n","2011       176.000\n","2012       178.739\n","2013       182.815\n","2014       182.340\n","2015       179.845\n","2016       169.238\n","2017       145.290\n","2018       143.870\n","2019       154.521\n","\n","Total de furtos\n","------------------------------------------------------------\n","Média de furtos por ano: 158.19 mil\n","O pior ano foi de 2013 com 182.81 mil furtos\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"bqHzjLefovpn"},"source":["> O ano com maior número de ocorrências de furtos foi em 2013 com __182 mil__ furtos."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"9q8cpzElovpn"},"source":["__Total de furtos de veículos por ano__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"4212f8b3-4551-419b-dbbc-a3352cd02e62","executionInfo":{"status":"ok","timestamp":1584440271837,"user_tz":180,"elapsed":4463,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"7qIpyAlSovpo","colab":{"base_uri":"https://localhost:8080/","height":442}},"source":["furtos = pd.DataFrame(df.groupby(['vano']).furto_veiculos.sum()/1000)\n","media = furtos.furto_veiculos.mean()\n","maximo = furtos.furto_veiculos.max()\n","ano = int(furtos.index[furtos.furto_veiculos == maximo].values)\n","print(furtos)\n","print()\n","print('Furtos de veículos')\n","print('-'*60)\n","print(f'Média de furtos por ano: {media:.2f} mil')\n","print(f'O pior ano foi de {ano} com {maximo:.2f} mil furtos')\n","print(f'Com {maximo-media:.2f} mil furtos acima da média ({(maximo-media)/media*100:.2f}%)')"],"execution_count":89,"outputs":[{"output_type":"stream","text":["      furto_veiculos\n","vano                \n","2003          19.942\n","2004          19.449\n","2005          21.322\n","2006          22.637\n","2007          21.668\n","2008          21.628\n","2009          20.342\n","2010          18.752\n","2011          15.593\n","2012          16.263\n","2013          17.668\n","2014          17.683\n","2015          16.944\n","2016          16.759\n","2017          15.708\n","2018          15.794\n","2019          15.595\n","\n","Furtos de veículos\n","------------------------------------------------------------\n","Média de furtos por ano: 18.46 mil\n","O pior ano foi de 2006 com 22.64 mil furtos\n","Com 4.18 mil furtos acima da média (22.66%)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"Q40p-4RZovpt"},"source":["__Proporção de furto de veículos em relação ao total de furtos registrados__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"e708d608-def9-4493-817c-144ac80a1c67","executionInfo":{"status":"ok","timestamp":1584440271837,"user_tz":180,"elapsed":4446,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"q9TCB3m9ovpt","colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["(df.groupby(['vano']).furto_veiculos.sum() / df.groupby(['vano']).total_furtos.sum())"],"execution_count":90,"outputs":[{"output_type":"execute_result","data":{"text/plain":["vano\n","2003    0.166833\n","2004    0.163753\n","2005    0.168776\n","2006    0.160690\n","2007    0.137881\n","2008    0.128018\n","2009    0.119487\n","2010    0.107292\n","2011    0.088597\n","2012    0.090987\n","2013    0.096644\n","2014    0.096978\n","2015    0.094214\n","2016    0.099026\n","2017    0.108115\n","2018    0.109780\n","2019    0.100925\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":90}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"rCVfqAWDovpv"},"source":["> A média da proporção de furtos de veículos em relação ao total de furtos foi de __11%__."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"Px_28QQXovpw"},"source":["__Visualização dos furtos em relação ao tempo__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"533aa9af-d9eb-4867-fc0d-0bd3cbbfe555","executionInfo":{"status":"ok","timestamp":1584440271838,"user_tz":180,"elapsed":4433,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"iJKO5Cdnovpw","colab":{"base_uri":"https://localhost:8080/","height":297}},"source":["plt.plot(df.groupby(['vano']).furto_veiculos.sum()/1000)\n","plt.plot(df.groupby(['vano']).total_furtos.sum()/1000)\n","plt.title('Contagem de furtos em relação ao tempo')\n","plt.ylabel('Furtos por 1000')\n","plt.legend(['Furtos de veículos', 'Total de furtos'])\n","plt.tight_layout()"],"execution_count":91,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwsBX4ZqE1i5w0085LgHFJkJqZ5dYkjtCuyHusSr5JHes89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0wHnjE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\nRCRLUMYYYyLS/wNQSP2VVQX7YQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"2PqdL0kYovpy"},"source":["> Analizando o gráfico de forma subjetiva, o número de ocorrências de furto de veículos não parece estar aumentando ao longo dos anos."]},{"cell_type":"markdown","metadata":{"id":"-P8FUh-0EogS","colab_type":"text"},"source":["### 2. Qual época do ano são registrados mais roubos e furtos de veículos?"]},{"cell_type":"markdown","metadata":{"id":"Fn73k_tLhWKZ","colab_type":"text"},"source":["#### 2.1 Roubos em relação à época do ano"]},{"cell_type":"markdown","metadata":{"id":"qVEbLQGFhyqt","colab_type":"text"},"source":["__Total de roubos por mês__"]},{"cell_type":"code","metadata":{"id":"hUfRGF15g_xy","colab_type":"code","outputId":"6e211f6c-3461-4d60-d671-18b573ec1165","executionInfo":{"status":"ok","timestamp":1584440271838,"user_tz":180,"elapsed":4405,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["roubos = pd.DataFrame(df.groupby(['mes']).total_roubos.sum()/1000)\n","media = roubos.total_roubos.mean()\n","maximo = roubos.total_roubos.max()\n","mes = int(roubos.index[roubos.total_roubos == maximo].values)\n","print(roubos)\n","print()\n","print('Total de roubos')\n","print('-'*60)\n","print(f'Média de roubos por mês: {media:.2f} mil')\n","print(f'O pior mês foi o mês {mes} com {maximo:.2f} mil roubos')"],"execution_count":92,"outputs":[{"output_type":"stream","text":["     total_roubos\n","mes              \n","1         208.079\n","2         198.853\n","3         222.259\n","4         217.687\n","5         223.258\n","6         206.961\n","7         208.126\n","8         208.224\n","9         200.687\n","10        211.915\n","11        204.972\n","12        206.169\n","\n","Total de roubos\n","------------------------------------------------------------\n","Média de roubos por mês: 209.77 mil\n","O pior mês foi o mês 5 com 223.26 mil roubos\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zVAmrPgXiW7B","colab_type":"text"},"source":["__Total de roubos de veículos por mês__"]},{"cell_type":"code","metadata":{"id":"cevkxIhYhhXo","colab_type":"code","outputId":"ea24608a-76df-4738-881d-b3b59c98453f","executionInfo":{"status":"ok","timestamp":1584440272175,"user_tz":180,"elapsed":4732,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["roubos = pd.DataFrame(df.groupby(['mes']).roubo_veiculo.sum()/1000)\n","media = roubos.roubo_veiculo.mean()\n","maximo = roubos.roubo_veiculo.max()\n","mes = int(roubos.index[roubos.roubo_veiculo == maximo].values)\n","print(roubos)\n","print()\n","print('Roubo de veículos')\n","print('-'*60)\n","print(f'Média de roubos por mês: {media:.2f} mil')\n","print(f'O pior mês foi o mês {mes} com {maximo:.2f} mil roubos')\n","print(f'Com {maximo-media:.2f} mil roubos acima da média ({(maximo-media)/media*100:.2f}%)')"],"execution_count":93,"outputs":[{"output_type":"stream","text":["     roubo_veiculo\n","mes               \n","1           49.031\n","2           46.582\n","3           51.358\n","4           49.133\n","5           48.527\n","6           44.354\n","7           43.813\n","8           43.792\n","9           43.420\n","10          46.203\n","11          44.911\n","12          46.688\n","\n","Roubo de veículos\n","------------------------------------------------------------\n","Média de roubos por mês: 46.48 mil\n","O pior mês foi o mês 3 com 51.36 mil roubos\n","Com 4.87 mil roubos acima da média (10.48%)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"tXyrd3-qi5eA","colab_type":"text"},"source":["__Proporção de roubo de veículos em relação ao total de roubos registrados__"]},{"cell_type":"code","metadata":{"id":"D1D3c_v1jBuO","colab_type":"code","outputId":"ec6bcec6-59f1-472b-f37f-03b3bd6fa2b4","executionInfo":{"status":"ok","timestamp":1584440272176,"user_tz":180,"elapsed":4691,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":255}},"source":["(df.groupby(['mes']).roubo_veiculo.sum() / df.groupby(['mes']).total_roubos.sum())"],"execution_count":94,"outputs":[{"output_type":"execute_result","data":{"text/plain":["mes\n","1     0.235636\n","2     0.234253\n","3     0.231073\n","4     0.225705\n","5     0.217358\n","6     0.214311\n","7     0.210512\n","8     0.210312\n","9     0.216357\n","10    0.218026\n","11    0.219108\n","12    0.226455\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":94}]},{"cell_type":"markdown","metadata":{"id":"lSGxIkDSjeDk","colab_type":"text"},"source":["> A média da proporção de roubos de veículos em relação ao total de roubos foi de __22%__."]},{"cell_type":"markdown","metadata":{"id":"f5ohoU8tlFXV","colab_type":"text"},"source":["__Visualização dos roubos em relação ao tempo__"]},{"cell_type":"code","metadata":{"id":"zu1GucryEKgZ","colab_type":"code","outputId":"9aea8392-8785-4aa4-e8d0-0aee43a86aed","executionInfo":{"status":"ok","timestamp":1584440272176,"user_tz":180,"elapsed":4669,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"colab":{"base_uri":"https://localhost:8080/","height":297}},"source":["plt.plot(df.groupby(['mes']).roubo_veiculo.sum()/1000)\n","plt.plot(df.groupby(['mes']).total_roubos.sum()/1000)\n","plt.title('Contagem de roubos em relação ao mês')\n","plt.ylabel('Roubos por 1000')\n","plt.legend(['Roubo de veículos', 'Total de roubos'])\n","plt.tight_layout()"],"execution_count":95,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8U2EqxfClrhr1iwzdMSoSUT9i6DHkNR2WKjcBq/eDK/dBj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W5ubn079+f7OzshJY397Z7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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"S0kMK7-ZoD5L","colab_type":"text"},"source":["> Analizando o gráfico de forma subjetiva, o número de ocorrências de roubo de automóveis não parece apresentar algum tipo de sazonalidade."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"vTIfMpXNlW4d"},"source":["#### 2.2 Furtos em relação à época do ano"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"vMmzWzVQlW4i"},"source":["__Total de furtos por mês__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"97e9fc09-579e-404b-b4cb-4ebbbc2c6f5b","executionInfo":{"status":"ok","timestamp":1584440272177,"user_tz":180,"elapsed":4649,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"bTIIAr3ulW4i","colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["furtos = pd.DataFrame(df.groupby(['mes']).total_furtos.sum()/1000)\n","media = furtos.total_furtos.mean()\n","maximo = furtos.total_furtos.max()\n","mes = int(furtos.index[furtos.total_furtos == maximo].values)\n","print(furtos)\n","print()\n","print('Total de furtos')\n","print('-'*60)\n","print(f'Média de furtos por mês: {media:.2f} mil')\n","print(f'O pior mês foi o mês {mes} com {maximo:.2f} mil furtos')"],"execution_count":96,"outputs":[{"output_type":"stream","text":["     total_furtos\n","mes              \n","1         233.489\n","2         232.504\n","3         236.897\n","4         216.617\n","5         227.065\n","6         215.385\n","7         221.501\n","8         222.844\n","9         217.236\n","10        223.502\n","11        215.975\n","12        226.269\n","\n","Total de furtos\n","------------------------------------------------------------\n","Média de furtos por mês: 224.11 mil\n","O pior mês foi o mês 3 com 236.90 mil furtos\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"VKpWftQalW4n"},"source":["__Total de furtos de veículos por mês__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"e1bc18c4-b42d-458d-d957-3edea442daac","executionInfo":{"status":"ok","timestamp":1584440272178,"user_tz":180,"elapsed":4640,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"zaOJlKAblW4o","colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["furtos = pd.DataFrame(df.groupby(['mes']).furto_veiculos.sum()/1000)\n","media = furtos.furto_veiculos.mean()\n","maximo = furtos.furto_veiculos.max()\n","mes = int(furtos.index[furtos.furto_veiculos == maximo].values)\n","print(furtos)\n","print()\n","print('Roubo de veículos')\n","print('-'*60)\n","print(f'Média de furtos por mês: {media:.2f} mil')\n","print(f'O pior mês foi o mês {mes} com {maximo:.2f} mil furtos')\n","print(f'Com {maximo-media:.2f} mil furtos acima da média ({(maximo-media)/media*100:.2f}%)')"],"execution_count":97,"outputs":[{"output_type":"stream","text":["     furto_veiculos\n","mes                \n","1            27.524\n","2            25.616\n","3            27.379\n","4            26.156\n","5            27.221\n","6            25.685\n","7            25.840\n","8            26.128\n","9            25.193\n","10           26.330\n","11           25.507\n","12           25.168\n","\n","Roubo de veículos\n","------------------------------------------------------------\n","Média de furtos por mês: 26.15 mil\n","O pior mês foi o mês 1 com 27.52 mil furtos\n","Com 1.38 mil furtos acima da média (5.27%)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"g4IOSUROlW4s"},"source":["__Proporção de furto de veículos em relação ao total de furtos registrados__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"dd587464-3363-4906-f961-f4cd0eec456a","executionInfo":{"status":"ok","timestamp":1584440272179,"user_tz":180,"elapsed":4624,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"AuWcwbcFlW4s","colab":{"base_uri":"https://localhost:8080/","height":255}},"source":["(df.groupby(['mes']).furto_veiculos.sum() / df.groupby(['mes']).total_furtos.sum())"],"execution_count":98,"outputs":[{"output_type":"execute_result","data":{"text/plain":["mes\n","1     0.117881\n","2     0.110174\n","3     0.115573\n","4     0.120748\n","5     0.119882\n","6     0.119252\n","7     0.116659\n","8     0.117248\n","9     0.115971\n","10    0.117807\n","11    0.118102\n","12    0.111230\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":98}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"iUzta9sslW4v"},"source":["> A média da proporção de furtos de veículos em relação ao total de furtos foi de __11%__."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"YwpTu4jhlW4v"},"source":["__Visualização dos furtos em relação ao mês__"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"6d9041ed-22e1-4fd2-839b-1b3e851c7b77","executionInfo":{"status":"ok","timestamp":1584440273468,"user_tz":180,"elapsed":5898,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}},"id":"CX2mPxr4lW4w","colab":{"base_uri":"https://localhost:8080/","height":297}},"source":["plt.plot(df.groupby(['mes']).furto_veiculos.sum()/1000)\n","plt.plot(df.groupby(['mes']).total_furtos.sum()/1000)\n","plt.title('Contagem de furtos em relação ao mês')\n","plt.ylabel('Furtos por 1000')\n","plt.legend(['Furtos de veículos', 'Total de furtos'])\n","plt.tight_layout()"],"execution_count":99,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+X34LPQeF05P73Zoeue54d/hD2bhE7Dho3Bn9v4TQ0vpsN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kVnyvKWueQH5Xn\nZOv3m0gmteiAOrx/Z/Jb5bCtqIRtRSVsKNzNxxt2sG1XCduKSikr9zrHb5ObvUdwpRpw+Xk5FJeW\nVwuW5ICp/FxYHLq3VQuf7SnUESC/dQ4FeYlXLq1zsthRXMr67cUUlZSxq6SMXbvDe0lZ/dOrrlVO\nFm1ys2mbFFptWoXP1UOw3J3imkKmIoCqhlBxaTm+91UCIMsgL6pPbraxa3cZO3aXpbTOAFrnZEXh\nlU27VkkBFpW1rVJWNejatsqu6Nc6J4tyB8fBqeh2h3L3iuWr+EwI5lDNxHANG6fcvWL4snKv+Fxe\nXtkvMVxZedXh3KGsYvg9hyt3p2yPaUN5tH5b52TRKieL1jlZtM7NDu852ZVlVcoTw2bTOjf6nJ2F\nWfp+/ACUlJVX/A0Ul5SzK/oO7todvpO7dpdRXBp9LiljV/KPomi8omi83aXl5GQZrXKyyM0Oy9Mq\nWo7WSd2J8sQwrauVp9qd7nUTBy06oM4/ol+t/dydnbvLQnjtKo3eSyo/J3c3MODqk51llcHSOpeC\nvBx6dcyjIK+gojw/Ki/ICwFYEIVfQaK7VQ5Ze9HCSf6DTYRWTe9FJWXsTHxO9NtdVvEHvnN3GYVJ\nIZgYNsuMvNysEBzRxigvJ5v2eTnkFbSOyqP+ieGijViiOy8ap+owYeOWl1s5zdxs2+OPONEiKywu\nZUdxCP9EXXdUlJUldVftv2XnblZt3smOxDC7S9nH/+ZmJ/F1a4z1UhlmUZDlRhv8pGCr6Bf1Ly+n\nMmiqB0+1lndpAyvZOicr7IXICT+8EvMvLXdKysrZXRq9ykLrP9Hd0B9bNa6b7ETQGbnZWWRnGVlm\n0TtkZRnZFZ/3LE/ubxa2N6mUZ0XTSUz36IHdOGbQAY23YEladEDVxcwqfhEf2GHvx68v4LYXlZCX\nm13Rqqls5VTuQszLbfpfSbnZ4ZddQV5uk863qZhZRdB1zW+9z9Nzd4pKqgZeIrgKi8vYWVzK7rJy\nLMycLAMj/OEnuqN/ZFkoD/2sor7VxyHqrj6OmWHVxklskLKsane2WcV8EhsuS96IJW+IzKr022M4\nq9xgJX9fS6ONc2IDXVxaFu2qreyurbzyVRaVVx8ntLi3F5WyoXQ3xaWhBVNUUk52FhUt99a52bTJ\nzaJLfquK1n2iX15uVmVZq/DDKDl0kn8gtcmt3DvQOidrr370JX9XSsu9IrxKEuFVLdB2Jy1jRdjV\nFnql5ewuK6O0LLRsk1u0ZVHLN7l1m+gui8pLy8spLq3aOk4eJtGKLiuPpuVOWXlYlkR5l3atFFD7\nm30NONk/mFnF8btuBfseeM1JTnYWOdlZtNNqAcJ3JdHa0TpJjY4Ci4hILCmgREQklhRQIiISSwoo\nERGJJQWUiIjEkgJKRERiSQElIiKxpIASEZFYUkCJiEgsmTfmzaGamJmtB1Zkuh77oCuwIdOVaGJa\n5pahJS4ztMzlboxl7uvu3aoX7tcBtb8zs9nuPjbT9WhKWuaWoSUuM7TM5U7nMmsXn4iIxJICSkRE\nYkkBlVn3ZroCGaBlbhla4jJDy1zutC2zjkGJiEgsqQUlIiKxpIASEZFYUkA1MTM7yMxmmdliM1tk\nZt/PdJ2aipllm9k8M3s203VpKmbW0cweN7MPzWyJmR2R6Tqlm5n9IPpuLzSzR8wsL9N1amxm9kcz\nW2dmC5PKOpvZy2b27+i9UybrmA61LPevou/3AjObYWYdG2t+CqimVwpc7e6DgcOBK8xscIbr1FS+\nDyzJdCWa2G+AF9x9EDCCZr78ZtYL+B4w1t2HAtnAf2S2VmlxP3BStbKpwEx3HwDMjD43N/ez53K/\nDAx19+HAv4DrG2tmCqgm5u5r3H1u1L2dsMHqldlapZ+Z9QZOBf4v03VpKmbWAZgI/AHA3Xe7+5bM\n1qpJ5ABtzCwHaAusznB9Gp27vw5sqlY8GXgg6n4AOKNJK9UEalpud3/J3Uujj+8CvRtrfgqoDDKz\nfsAo4L3M1qRJ3Ab8ECjPdEWaUH9gPXBftGvz/8ysXaYrlU7u/hlwM/ApsAbY6u4vZbZWTaa7u6+J\nuj8HumeyMhnyTeD5xpqYAipDzCwfeAK40t23Zbo+6WRmpwHr3H1OpuvSxHKA0cDd7j4K2EHz3O1T\nITruMpkQzj2BdmZ2XmZr1fQ8XL/Toq7hMbMfEQ5hPNRY01RAZYCZ5RLC6SF3fzLT9WkCE4DTzWw5\n8ChwrJk9mNkqNYlVwCp3T7SQHycEVnN2PPCJu6939xLgSeBLGa5TU1lrZgcCRO/rMlyfJmNmU4DT\ngHO9ES+uVUA1MTMzwjGJJe5+S6br0xTc/Xp37+3u/QgHzF9x92b/q9rdPwdWmtmhUdFxwOIMVqkp\nfAocbmZto+/6cTTzE0OSPANcGHVfCDydwbo0GTM7ibD7/nR339mY01ZANb0JwPmEVsT86HVKpisl\nafNd4CEzWwCMBP47w/VJq6i1+DgwF/iAsI1pdrf/MbNHgHeAQ81slZldDNwEnGBm/ya0JG/KZB3T\noZblvgMoAF6Otmf3NNr8dKsjERGJI7WgREQklhRQIiISSwooERGJJQWUiIjEkgJKRERiSQElIiKx\npIASEZFY+v8oC9To1DNeKQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"6A9t4YFKoZI2","colab_type":"text"},"source":["> Analizando o gráfico de forma subjetiva, o número de ocorrências de furto de automóveis não parece apresentar algum tipo de sazonalidade."]},{"cell_type":"markdown","metadata":{"id":"IBjOBTodEnPt","colab_type":"text"},"source":["### 3. Em qual região do Estado do Rio de Janeiro são registrados mais roubos e furtos de veículos?"]},{"cell_type":"markdown","metadata":{"id":"6qRbcE7XEy8G","colab_type":"text"},"source":["#### 3.1 Roubos por região\n"]},{"cell_type":"markdown","metadata":{"id":"RMaNyYHWFFN8","colab_type":"text"},"source":["__Regiões com os maiores índices de roubos__"]},{"cell_type":"code","metadata":{"id":"yEbmPBTfHRX5","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":170},"outputId":"8c37953e-82c1-4eda-81b3-0705e0eca7b5","executionInfo":{"status":"ok","timestamp":1584440273468,"user_tz":180,"elapsed":5873,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}}},"source":["df.groupby('Regiao', sort=False).roubo_veiculo.sum()\n"],"execution_count":100,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Regiao\n","Capital               304126\n","Baixada Fluminense    151872\n","Interior               25760\n","Grande Niterói         64204\n","Capital                 8525\n","Interior                 844\n","Grande Niterói          2481\n","Name: roubo_veiculo, dtype: int64"]},"metadata":{"tags":[]},"execution_count":100}]},{"cell_type":"markdown","metadata":{"id":"jvU0DyzKE_HP","colab_type":"text"},"source":["__Regiões com os menores índices de roubos__"]},{"cell_type":"markdown","metadata":{"id":"FYhCdpPyHERq","colab_type":"text"},"source":["#### 3.2 Furtos por região"]},{"cell_type":"code","metadata":{"id":"DGFfnat8FFaB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":170},"outputId":"6e8bb55c-e003-4bcf-d3a8-dec9347c0fc7","executionInfo":{"status":"ok","timestamp":1584440273469,"user_tz":180,"elapsed":5862,"user":{"displayName":"Caio Sain Vallio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GijPVxWLrw-Bs4-Am8AfklNPCPNkNfPC22y5BkF=s64","userId":"11223760173113471421"}}},"source":["df.groupby('Regiao', sort=False).furto_veiculos.sum()"],"execution_count":101,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Regiao\n","Capital               147029\n","Baixada Fluminense     60250\n","Interior               58287\n","Grande Niterói         42658\n","Capital                 3249\n","Interior                1377\n","Grande Niterói           897\n","Name: furto_veiculos, dtype: int64"]},"metadata":{"tags":[]},"execution_count":101}]},{"cell_type":"markdown","metadata":{"id":"6ZhliHL83tSi","colab_type":"text"},"source":["# Conclusões\n","\n","Analizando esse dataset, o número de casos cadastrados de roubos e furtos de veículos não parece estar aumentando ao longo dos últimos anos.\n","\n","__Roubos__\n","\n","* Na média, roubos de veículos correnpondem à 20% do total de roubos registrados no Estado do Rio de Janeiro.\n","\n","* O ano com o maior número de roubos cadastrados foi o de 2017 com 54.37 mil roubos, que corresponde 65,7% acima da média.\n","\n","* O mês de Maio é o mês com maior número de registros de roubos, com 10,5% acima da média.\n","\n","* A Capital do Rio de Janeiro tem o maior número de registro de roubos do Estado do Rio De janeiro.\n","\n","__Furtos__\n","\n","* Na média, furtos de veículos correnpondem à 11% do total de furtos registrados no Estado do Rio de Janeiro.\n","\n","* O ano com o maior número de furtos cadastrados foi o de 2006 com 22,64 mil furtos, que corresponde 22,7% acima da média.\n","\n","* O mês de Janeiro é o mês com maior número de registros de furtos, com 5,3% acima da média.\n","\n","* A Capital do Rio de Janeiro tem o maior número de registro de furto do Estado do Rio De janeiro."]}]}