EP2_AnaliseDadosPúblicos_OcorrenciasCriminais20152018.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ocorrências criminais de cada estado brasileiro entre os anos de 2015 a 2018\n",
    "Análise de dados públicos utilizando-se da biblioteca Pandas para extrair informações sobre o total de ocorrências criminais por estado brasileiro."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Importando bibliotecas, declarando váriaveis responsáveis pelo caminho do arquivo .xlsx e definindo os objetos dos gráficos de cada região brasileira."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from random import sample\n",
    "\n",
    "#files xlsx sudeste\n",
    "xlsx_sp_file = 'sudeste/sp---dadospublicos.xlsx'\n",
    "xlsx_rj_file = 'sudeste/rj---dadospublicos.xlsx'\n",
    "xlsx_mg_file = 'sudeste/mg---dadospublicos.xlsx'\n",
    "xlsx_es_file = 'sudeste/es---dadospublicos.xlsx'\n",
    "\n",
    "#files xlsx sul\n",
    "xlsx_pr_file = 'sul/pr---dadospublicos.xlsx'\n",
    "xlsx_rs_file = 'sul/rs---dadospublicos.xlsx'\n",
    "xlsx_sc_file = 'sul/sc---dadospublicos.xlsx'\n",
    "\n",
    "#files xlsx centro oeste\n",
    "xlsx_df_file = 'centro-oeste/df---dadospublicos.xlsx'\n",
    "xlsx_go_file = 'centro-oeste/go---dadospublicos.xlsx'\n",
    "xlsx_ms_file = 'centro-oeste/ms---dadospublicos.xlsx'\n",
    "xlsx_mt_file = 'centro-oeste/mt---dadospublicos.xlsx'\n",
    "\n",
    "#files xlsx nordeste\n",
    "xlsx_al_file = 'nordeste/al---dadospublicos.xlsx'\n",
    "xlsx_ba_file = 'nordeste/ba---dadospublicos.xlsx'\n",
    "xlsx_ce_file = 'nordeste/ce---dadospublicos.xlsx'\n",
    "xlsx_ma_file = 'nordeste/ma---dadospublicos.xlsx'\n",
    "xlsx_pb_file = 'nordeste/pb---dadospublicos.xlsx'\n",
    "xlsx_pe_file = 'nordeste/pe---dadospublicos.xlsx'\n",
    "xlsx_pi_file = 'nordeste/pi---dadospublicos.xlsx'\n",
    "xlsx_rn_file = 'nordeste/rn---dadospublicos.xlsx'\n",
    "xlsx_se_file = 'nordeste/se---dadospublicos.xlsx'\n",
    "\n",
    "#files xlsx norte\n",
    "xlsx_ac_file = 'norte/ac---dadospublicos.xlsx'\n",
    "xlsx_am_file = 'norte/am---dadospublicos.xlsx'\n",
    "xlsx_ap_file = 'norte/ap---dadospublicos.xlsx'\n",
    "xlsx_pa_file = 'norte/pa---dadospublicos.xlsx'\n",
    "xlsx_ro_file = 'norte/ro---dadospublicos.xlsx'\n",
    "xlsx_rr_file = 'norte/rr---dadospublicos.xlsx'\n",
    "xlsx_to_file = 'norte/to---dadospublicos.xlsx'\n",
    "\n",
    "#população por estado\n",
    "xlsx_pop = 'relacao/relacao_pop.xlsx'\n",
    "\n",
    "#columns relação por população\n",
    "pop_names = 'UF', 'Estado', 'Relativo'\n",
    "\n",
    "#columns ocorrências por estado\n",
    "names = 'UF', 'Tipo de crime', 'Mês/Ano', 'Total de ocorrências'\n",
    "\n",
    "#objects sudeste plot bar\n",
    "sudeste = 'SP', 'RJ', 'MG', 'ES'\n",
    "\n",
    "#objects sudeste plot pie\n",
    "sudeste_pie = 'MG', 'ES', 'RJ', 'SP'\n",
    "\n",
    "#objects sul plot bar\n",
    "sul = 'PA', 'RS', 'SC'\n",
    "\n",
    "#objects sul plot pie\n",
    "sul_pie = 'PR', 'SC', 'RS'\n",
    "\n",
    "#objects centro oeste plot bar\n",
    "centro = 'DF', 'GO', 'MS', 'MT'\n",
    "\n",
    "#objects centro oeste plot pie\n",
    "centro_pie = 'MS', 'MT', 'GO', 'DF'\n",
    "\n",
    "#objects nordeste plot bar\n",
    "nordeste = 'AL', 'BA', 'CE', 'MA', 'PB', 'PE', 'PI', 'RN', 'SE'\n",
    "\n",
    "#objects nordeste plot pie\n",
    "nordeste_pie = 'MA', 'PI', 'CE', 'RN', 'PB', 'PE', 'AL', 'SE', 'BA'\n",
    "\n",
    "#objects norte plot bar\n",
    "norte = 'AC', 'AM', 'AP', 'PA', 'RO', 'RR', 'TO'\n",
    "\n",
    "#objects norte plot pie\n",
    "norte_pie = 'RO', 'AC', 'AM', 'RR', 'PA', 'AP', 'TO'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Utilizando a sintaxe *read_excel* para leitura da tabela dos arquivos .xlsx, definindo alguns parâmetros e organizando informações."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read_excel sudeste\n",
    "sp = pd.read_excel(xlsx_sp_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "rj = pd.read_excel(xlsx_rj_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "mg = pd.read_excel(xlsx_mg_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "es = pd.read_excel(xlsx_es_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "\n",
    "#read_excel sul\n",
    "pr = pd.read_excel(xlsx_pr_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "rs = pd.read_excel(xlsx_rs_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "sc = pd.read_excel(xlsx_sc_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "#read_excel centro oeste\n",
    "df = pd.read_excel(xlsx_df_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "go = pd.read_excel(xlsx_go_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "ms = pd.read_excel(xlsx_ms_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "mt = pd.read_excel(xlsx_mt_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "#read_excel nordeste\n",
    "al = pd.read_excel(xlsx_al_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "ba = pd.read_excel(xlsx_ba_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "ce = pd.read_excel(xlsx_ce_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "ma = pd.read_excel(xlsx_ma_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "pb = pd.read_excel(xlsx_pb_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "pe = pd.read_excel(xlsx_pe_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "pi = pd.read_excel(xlsx_pi_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "rn = pd.read_excel(xlsx_rn_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "se = pd.read_excel(xlsx_se_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "#read_excel norte\n",
    "ac = pd.read_excel(xlsx_ac_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "am = pd.read_excel(xlsx_am_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "ap = pd.read_excel(xlsx_ap_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "pa = pd.read_excel(xlsx_pa_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "ro = pd.read_excel(xlsx_ro_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "rr = pd.read_excel(xlsx_rr_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)\n",
    "\n",
    "to = pd.read_excel(xlsx_to_file,\n",
    "                   sheet_name='Ocorrências',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Leitura das tabelas de relação por população de cada região brasileira."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_sudeste = pd.read_excel(xlsx_pop,\n",
    "                   sheet_name='Sudeste',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=pop_names)\n",
    "\n",
    "pop_sul = pd.read_excel(xlsx_pop,\n",
    "                   sheet_name='Sul',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=pop_names)\n",
    "\n",
    "pop_centro = pd.read_excel(xlsx_pop,\n",
    "                   sheet_name='Centro-oeste',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=pop_names)\n",
    "\n",
    "pop_nordeste = pd.read_excel(xlsx_pop,\n",
    "                   sheet_name='Nordeste',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=pop_names)\n",
    "\n",
    "pop_norte = pd.read_excel(xlsx_pop,\n",
    "                   sheet_name='Norte',\n",
    "                   sep=',',\n",
    "                   delimiter=';',\n",
    "                   encoding='iso-8859-1',\n",
    "                   names=pop_names)\n",
    "\n",
    "pop_br = pd.read_excel(xlsx_pop,\n",
    "                      sheet_name='Plan1',\n",
    "                      sep=',',\n",
    "                      delimiter=';',\n",
    "                      encoding='iso-8859-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Visualizando uma tabela com os valores aleatórios do estado de São Paulo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>UF</th>\n",
       "      <th>Tipo de crime</th>\n",
       "      <th>Mês/Ano</th>\n",
       "      <th>Total de ocorrências</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Roubo de veículo</td>\n",
       "      <td>11/2017</td>\n",
       "      <td>5634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Estupro</td>\n",
       "      <td>04/2017</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Roubo a instituição financeira</td>\n",
       "      <td>12/2018</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Roubo a instituição financeira</td>\n",
       "      <td>06/2017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Roubo de carga</td>\n",
       "      <td>02/2017</td>\n",
       "      <td>865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Roubo a instituição financeira</td>\n",
       "      <td>11/2016</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Lesão corporal seguida de morte</td>\n",
       "      <td>02/2017</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Roubo a instituição financeira</td>\n",
       "      <td>03/2018</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Furto de veículo</td>\n",
       "      <td>09/2018</td>\n",
       "      <td>7894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Roubo de carga</td>\n",
       "      <td>07/2017</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            UF                    Tipo de crime  Mês/Ano  Total de ocorrências\n",
       "322  São Paulo                 Roubo de veículo  11/2017                  5634\n",
       "27   São Paulo                          Estupro  04/2017                   875\n",
       "239  São Paulo   Roubo a instituição financeira  12/2018                     3\n",
       "221  São Paulo   Roubo a instituição financeira  06/2017                     5\n",
       "265  São Paulo                   Roubo de carga  02/2017                   865\n",
       "214  São Paulo   Roubo a instituição financeira  11/2016                    14\n",
       "169  São Paulo  Lesão corporal seguida de morte  02/2017                     2\n",
       "230  São Paulo   Roubo a instituição financeira  03/2018                     5\n",
       "92   São Paulo                 Furto de veículo  09/2018                  7894\n",
       "270  São Paulo                   Roubo de carga  07/2017                   853"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Visualizando uma tabela com os valores aleatórios do estado da Bahia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UF</th>\n",
       "      <th>Tipo de crime</th>\n",
       "      <th>Mês/Ano</th>\n",
       "      <th>Total de ocorrências</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Roubo a instituição financeira</td>\n",
       "      <td>08/2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Roubo de veículo</td>\n",
       "      <td>08/2016</td>\n",
       "      <td>1275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Furto de veículo</td>\n",
       "      <td>01/2017</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Roubo seguido de morte (latrocínio)</td>\n",
       "      <td>10/2018</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Estupro</td>\n",
       "      <td>07/2017</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Tentativa de homicídio</td>\n",
       "      <td>09/2015</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Homicídio doloso</td>\n",
       "      <td>01/2018</td>\n",
       "      <td>506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Lesão corporal seguida de morte</td>\n",
       "      <td>01/2015</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Roubo de veículo</td>\n",
       "      <td>01/2017</td>\n",
       "      <td>1211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>Roubo de carga</td>\n",
       "      <td>07/2016</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        UF                        Tipo de crime  Mês/Ano  Total de ocorrências\n",
       "223  Bahia       Roubo a instituição financeira  08/2017                     0\n",
       "307  Bahia                     Roubo de veículo  08/2016                  1275\n",
       "72   Bahia                     Furto de veículo  01/2017                   544\n",
       "381  Bahia  Roubo seguido de morte (latrocínio)  10/2018                    10\n",
       "30   Bahia                              Estupro  07/2017                   269\n",
       "392  Bahia               Tentativa de homicídio  09/2015                   217\n",
       "132  Bahia                     Homicídio doloso  01/2018                   506\n",
       "144  Bahia      Lesão corporal seguida de morte  01/2015                     9\n",
       "312  Bahia                     Roubo de veículo  01/2017                  1211\n",
       "258  Bahia                       Roubo de carga  07/2016                    27"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ba.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Visualizando uma tabela com a população e o valor relação (ocorrências criminais x população) por estado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>UF</th>\n",
       "      <th>ESTADO</th>\n",
       "      <th>População estimada  2018</th>\n",
       "      <th>OCORRÊNCIAS</th>\n",
       "      <th>RELATIVO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RR</td>\n",
       "      <td>Roraima</td>\n",
       "      <td>576568</td>\n",
       "      <td>7196</td>\n",
       "      <td>1248.074815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ES</td>\n",
       "      <td>Espírito Santo</td>\n",
       "      <td>3972388</td>\n",
       "      <td>49031</td>\n",
       "      <td>1234.295341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PA</td>\n",
       "      <td>Pará</td>\n",
       "      <td>8513497</td>\n",
       "      <td>76913</td>\n",
       "      <td>903.424292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RS</td>\n",
       "      <td>Rio Grande do Sul</td>\n",
       "      <td>11329605</td>\n",
       "      <td>172501</td>\n",
       "      <td>1522.568527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>DF</td>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>2974703</td>\n",
       "      <td>50502</td>\n",
       "      <td>1697.715705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MA</td>\n",
       "      <td>Maranhão</td>\n",
       "      <td>7035055</td>\n",
       "      <td>46557</td>\n",
       "      <td>661.785871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BA</td>\n",
       "      <td>Bahia</td>\n",
       "      <td>14812617</td>\n",
       "      <td>125526</td>\n",
       "      <td>847.426218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SE</td>\n",
       "      <td>Sergipe</td>\n",
       "      <td>2278308</td>\n",
       "      <td>22311</td>\n",
       "      <td>979.279360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PI</td>\n",
       "      <td>Piauí</td>\n",
       "      <td>3264531</td>\n",
       "      <td>30254</td>\n",
       "      <td>926.748743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MG</td>\n",
       "      <td>Minas Gerais</td>\n",
       "      <td>21040662</td>\n",
       "      <td>193328</td>\n",
       "      <td>918.830406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UF             ESTADO  População estimada  2018  OCORRÊNCIAS     RELATIVO\n",
       "3   RR            Roraima                    576568         7196  1248.074815\n",
       "17  ES     Espírito Santo                   3972388        49031  1234.295341\n",
       "4   PA               Pará                   8513497        76913   903.424292\n",
       "22  RS  Rio Grande do Sul                  11329605       172501  1522.568527\n",
       "26  DF   Distrito Federal                   2974703        50502  1697.715705\n",
       "7   MA           Maranhão                   7035055        46557   661.785871\n",
       "15  BA              Bahia                  14812617       125526   847.426218\n",
       "14  SE            Sergipe                   2278308        22311   979.279360\n",
       "8   PI              Piauí                   3264531        30254   926.748743\n",
       "16  MG       Minas Gerais                  21040662       193328   918.830406"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_br.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Somando o total de ocorrências criminais de cada estado brasileiro durante o ano de 2015 a 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sum estado sudeste\n",
    "total_estado_sudeste = []\n",
    "\n",
    "sp_sum = sum(sp['Total de ocorrências'])\n",
    "rj_sum = sum(rj['Total de ocorrências'])\n",
    "mg_sum = sum(mg['Total de ocorrências'])\n",
    "es_sum = sum(es['Total de ocorrências'])\n",
    "\n",
    "total_estado_sudeste = sp_sum, rj_sum, mg_sum, es_sum\n",
    "\n",
    "#sum estado sul\n",
    "total_estado_sul = []\n",
    "\n",
    "pr_sum = sum(pr['Total de ocorrências'])\n",
    "rs_sum = sum(rs['Total de ocorrências'])\n",
    "sc_sum = sum(sc['Total de ocorrências'])\n",
    "\n",
    "total_estado_sul = pr_sum, rs_sum, sc_sum\n",
    "\n",
    "#sum estado centro oeste\n",
    "total_estado_centro = []\n",
    "\n",
    "df_sum = sum(df['Total de ocorrências'])\n",
    "go_sum = sum(go['Total de ocorrências'])\n",
    "ms_sum = sum(ms['Total de ocorrências'])\n",
    "mt_sum = sum(mt['Total de ocorrências'])\n",
    "\n",
    "total_estado_centro = df_sum, go_sum, ms_sum, mt_sum\n",
    "\n",
    "#sum estado nordeste\n",
    "total_estado_nordeste = []\n",
    "\n",
    "al_sum = sum(al['Total de ocorrências'])\n",
    "ba_sum = sum(ba['Total de ocorrências'])\n",
    "ce_sum = sum(ce['Total de ocorrências'])\n",
    "ma_sum = sum(ma['Total de ocorrências'])\n",
    "pb_sum = sum(pb['Total de ocorrências'])\n",
    "pe_sum = sum(pe['Total de ocorrências'])\n",
    "pi_sum = sum(pi['Total de ocorrências'])\n",
    "rn_sum = sum(rn['Total de ocorrências'])\n",
    "se_sum = sum(se['Total de ocorrências'])\n",
    "\n",
    "total_estado_nordeste = al_sum, ba_sum, ce_sum, ma_sum, pb_sum, pe_sum, pi_sum, rn_sum, se_sum\n",
    "\n",
    "#sum estado norte\n",
    "total_estado_norte = []\n",
    "\n",
    "ac_sum = sum(ac['Total de ocorrências'])\n",
    "am_sum = sum(am['Total de ocorrências'])\n",
    "ap_sum = sum(ap['Total de ocorrências'])\n",
    "pa_sum = sum(pa['Total de ocorrências'])\n",
    "ro_sum = sum(ro['Total de ocorrências'])\n",
    "rr_sum = sum(rr['Total de ocorrências'])\n",
    "to_sum = sum(to['Total de ocorrências'])\n",
    "\n",
    "total_estado_norte = ac_sum, am_sum, ap_sum, pa_sum, ro_sum, rr_sum, to_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Atribuindo valores da coluna Relativo para a variável de relação para cada região"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "relac_sudeste = pop_sudeste['Relativo']\n",
    "relac_sul = pop_sul['Relativo']\n",
    "relac_centro = pop_centro['Relativo']\n",
    "relac_nordeste = pop_nordeste['Relativo']\n",
    "relac_norte = pop_norte['Relativo']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Atribuindo o total de cada estado para devida variável região *total_estado_\"nomeregião\"*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Região Sudeste =>  (818435, 312418, 193328, 49031)\n",
      "Região Sul =>  (146221, 172501, 76440)\n",
      "Região Centro-Oeste =>  (50502, 114772, 30580, 39981)\n",
      "Região Nordeste =>  (29220, 125526, 88238, 46557, 21355, 122400, 30254, 39456, 22311)\n",
      "Região Norte =>  (4916, 34047, 8201, 76913, 27785, 7196, 15375)\n"
     ]
    }
   ],
   "source": [
    "print('Região Sudeste => ', total_estado_sudeste)\n",
    "print('Região Sul => ', total_estado_sul)\n",
    "print('Região Centro-Oeste => ', total_estado_centro)\n",
    "print('Região Nordeste => ', total_estado_nordeste)\n",
    "print('Região Norte => ', total_estado_norte)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Com as informações organizadas, foi utilizado o matplot para gerar os gráficos para cada região brasileira, sendo possível visualizar as regiões e os estados que mais tiveram ocorrências criminais entre o ano de 2015 a 2018."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1. Ocorrências criminais da região Sudeste - entre os anos de 2015 a 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot bar sudeste\n",
    "plt.bar(sudeste, total_estado_sudeste, align='center', alpha=1, color='Red')\n",
    "plt.ylabel('Total de ocorrências criminais')\n",
    "plt.title('Ocorrências criminais da região Sudeste - período de 2015 a 2018')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2. Ocorrências criminais da região Sul - entre os anos de 2015 a 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot bar sul\n",
    "plt.bar(sul, total_estado_sul, align='center', alpha=1, color='Orange')\n",
    "plt.ylabel('Total de ocorrências criminais')\n",
    "plt.title('Ocorrências criminais da região Sul - período de 2015 a 2018')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3. Ocorrências criminais da região Centro-Oeste - entre os anos de 2015 a 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot bar centro\n",
    "plt.bar(centro, total_estado_centro, align='center', alpha=1, color='Yellow')\n",
    "plt.ylabel('Total de ocorrências criminais')\n",
    "plt.title('Ocorrências criminais da região Centro-Oeste - período de 2015 a 2018')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.4. Ocorrências criminais da região Nordeste - entre os anos de 2015 a 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot bar nordeste\n",
    "plt.bar(nordeste, total_estado_nordeste, align='center', alpha=1, color='Blue')\n",
    "plt.ylabel('Total de ocorrências criminais')\n",
    "plt.title('Ocorrências criminais da região Nordeste - período de 2015 a 2018')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.5. Ocorrências criminais da região Norte - entre os anos de 2015 a 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x2wH3kBpiLSIdcRrdYJk+RjqEuTQv20/Iv+kadSfl9yrG/EJhvEjbqcX5cRYrtlycVSPu4jp7Lml9fT4v89h6cVcelZN6ZlZC/mfYBnwqIm7o7nhajaQgHfZt/A/ZbBW0zKE/s1YlaT9Jg/L5sMo5t1u7OSyzNw0nKrOO/TvpcNEzpF4GxkfEssaTmFln8aE/MzNraU3do1LqPfo+SX+TdGlu/bGlpNuUOmq8vNCK5S15eF4eP7Iwn1Ny+YOS9iuUj81l8ySdvHIEZmbW0zVtjyo3cbyJdCX5MknTSE3IDyC1hrtM0gXAXyPifEmfB3aIiKMlHUzq/+4TuXnopaQ+5DYjte3fOr/NQ8AHSCe3ZwOHRMT9jeLaaKONYuTIkZ2+vGZmvdUdd9zxTEQM7a73b3aPyn2A/pJeJXXzsQDYm/Y2/BeTmj6fD4zLrwGuAH4iSbn8soh4GXhU0jxS0oLUndEjAJIuy3UbJqqRI0cyZ86cTlk4M7M3A0nVvf50qaYd+ot0ZfsPSP25LaD92qbnIuK1XK2N9m5MhpG7Lcnjl5A693yjvGqaeuUrkTRR0hxJcxYuXLjmC2dmZl2maYkqXx0+DtiSdMiu0hVStcqxx1rdedTrEqRR+cqFEZMjYnREjB46tNv2Xs3MbDU0szHF+0n3lFkYqYuc35LuFzVI7R3PDqe9H7A2cv9aefxA0lXPb5RXTVOv3MzMepFmJqrHgTG5vywB+5DOH91A6s4DUh9aV+bX02nvU+tjwPWRWnpMBw7OrQK3JHUpfzup8cSo3IqwL3BwrmtmZr1I0xpTRMRtkq4g9eX0GqnPvMmkPrIuk/StXHZhnuRC4Be5scRiUuIhIu7LLQbvz/M5NvLtKXInjjNIfaxNiYjV7XXYzMxa1Jvugt/Ro0eHW/2ZmZUn6Y6IGN1d7+8ulMzMrKU5UZmZWUtzojIzs5bW7J4pzHo9ndborvddLya9uc47W+/nPSozM2tpTlRmZtbSnKjMzKylOVGZmVlLc6IyM7OW5kRlZmYtzYnKzMxamhOVmZm1NCcqMzNraU5UZmbW0pyozMyspTlRmZlZS3OiMjOzluZEZWZmLa1piUrS2yXdXXg8L+l4SUMkzZQ0Nz8PzvUl6RxJ8yTdI2nnwrwm5PpzJU0olL9b0r15mnMktdb9FszMbI01LVFFxIMRsVNE7AS8G3gR+B1wMnBdRIwCrsvDAPsDo/JjInA+gKQhwCRgV2AXYFIlueU6EwvTjW3W8piZWffoqkN/+wAPR8Q/gHHAxbn8YmB8fj0OmBrJrcAgSZsC+wEzI2JxRDwLzATG5nEDIuKWiAhgamFeZmbWS3RVojoYuDS/3iQiFgDk541z+TBgfmGatlzWqLytRvlKJE2UNEfSnIULF67hopiZWVdqeqKS1Bc4EPh1R1VrlMVqlK9cGDE5IkZHxOihQ4d2EIaZmbWSrtij2h+4MyKeysNP5cN25Oenc3kbsHlhuuHAEx2UD69RbmZmvUhXJKpDaD/sBzAdqLTcmwBcWSg/LLf+GwMsyYcGZwD7ShqcG1HsC8zI45ZKGpNb+x1WmJeZmfUSfZo5c0nrAh8AjioUfxeYJulI4HHgoFx+NXAAMI/UQvAIgIhYLOkMYHaud3pELM6vjwEuAvoD1+SHmZn1Ik1NVBHxIrBhVdkiUivA6roBHFtnPlOAKTXK5wDbd0qwZmbWktwzhZmZtbQOE5Wk4yQNyK9/Kul2SSvtEZmZmTVDmT2qiRHxvKR9SdcpHQOc1dywzMzMkjKJqnJt0v7AzyPijpLTmZmZrbEyCeevkq4GPgxcI2l96lxYa2Zm1tnKtPo7gtSp7LyIeFHSRsCRzQ3LzMwsqZuoJI2KiLnAdsBLwHBJlZ4glndFcGZmZo32qE4m7TmdW2NcAHs2JSIzM7OCuokqIo7Mz3t0XThmZmYrKtUzhaRtgG2BfpWyiPhVs4IyMzOr6DBRSTqV1BHsNqQOYvcDbgKcqMzMrOnKNE//BPA+YEFEHArsSJP7CDQzM6sok6iWRcRy4DVJGwBPAm9rblhmZmZJmT2juyQNIvVePgd4HrizqVGZmZllHSaqiKjcS+pcSTOAARHhRGVmZl2iVJ99kt4qaRdgY6CfpPc0NywzM7OkTKu/bwOfBv5Oe48UQbobr5mZWVOVOUf1UWDriHip2cGYmZlVK3Po79GS9VYiaZCkKyT9XdIDkv5d0hBJMyXNzc+Dc11JOkfSPEn3SNq5MJ8Juf5cSRMK5e+WdG+e5hxJWp04zcysdZVJQEtJLf/OlfTflUfJ+f8YuDYitiFdf/UAqQ/B6yJiFHBdHoZ0v6tR+TEROB9A0hBgErArsAswqZLccp2JhenGlozLzMx6iDKH/q7Nj1WSb1+/J3A4QES8ArwiaRywV652MTALOAkYB0yNiABuzXtjm+a6MyNicZ7vTGCspFmkFoi35PKpwHjgmlWN1czMWleZ5ukXrua83wYsBH4uaUfgDuCLwCYRsSDPe4GkjXP9YcD8wvRtuaxReVuN8pVImkja82LEiBGruThmZtYd6h76k3Rpfr5L0p3VjxLz7gPsDJwfEe8C/kX7Yb6ab1mjLFajfOXCiMkRMToiRg8dOrRx1GZm1lIa7VF9JT9/bDXn3Qa0RcRtefgKUqJ6StKmeW9qU+DpQv3NC9MPB57I5XtVlc/K5cNr1Dczs16k7h5VRLTl54cj4mFgAelQXuXRUEQ8CcyX9PZctA9wPzAdqLTcmwBcmV9PBw7Lrf/GAEvyIcIZwL6SBudGFPsCM/K4pZLG5NZ+hxXmZWZmvUSZC34/C5xButj3ddIhtwDKnOz5T+ASSX2BR4AjSMlxmqQjgceBg3Ldq0kXEc8DXsx1iYjFks4AZud6p1caVgDHABcB/UmNKNyQwsyslynT6u8kYMeIeLrDmlUi4m5gdI1R+9SoG8CxdeYzhdQpbnX5HGD7VY3LzMx6jjLXUT1C6jHdzMysy5XZozoZ+IukW4GXK4URcULTojIzM8vKJKoLgL8A95LOUZmZmXWZMonq9Yj4QtMjMTMzq6HMOarrJH1G0lBJAyqPpkdmZmZGuT2qyjVPpxXKyjZPNzMzWyNl+vrbvKM6ZmZmzVI3UUl6b0TcKOnAWuMjYnrzwjIzM0sa7VF9ALiR9p4jioLU5ZGZmVlT1U1UEXGqpLWB30fEb7owJjMzszc0bPUXEcuB47soFjMzs5WUaZ4+Q9LxkjZ183QzM+tqZZqnH5WfT6T9hoVunm5mZl3CzdPNzKyldXjoT9LRkgYVhgdLmtjcsMzMzJIy56iOjojnKgMR8SzphoVmZmZNVyZRrV0ckLQWsE5zwjEzM1tRmcYUMyVdSrrdR5D2pv6nqVGZmZllZfaovkK6H9WXSC3/bgK+XGbmkh6TdK+kuyXNyWVDJM2UNDc/D87lknSOpHmS7pG0c2E+E3L9uZImFMrfnec/L0+r8otuZmY9QYeJKiKWR8RPImJ8RIyLiHMj4rVVeI/3RcROETE6D58MXBcRo4Dr8jDA/sCo/JgInA8psQGTgF2BXYBJleSW60wsTDd2FeIyM7MeoMweVWcbB1ycX18MjC+UT43kVmCQpE2B/YCZEbE4N+SYCYzN4wZExC0REcDUwrzMzKyXaHaiCuBPku4oNGnfJCIWAOTnjXP5MGB+Ydq2XNaovK1G+UokTZQ0R9KchQsXruEimZlZVyrTmOIN+RzQuhHxr5KT7BYRT0jamNQo4++NZl+jLFajfOXCiMnAZIDRo0fXrGNmZq2pzAW/U3P/fusC9wGPSjqhzMwj4on8/DTwO9I5pqfyYTvy89O5ehtQ7AVjOPBEB+XDa5SbmVkvUubQ3zsj4nnS+Z8/kRLC4R1NJGk9SRtUXgP7An8j3ceq0nJvAnBlfj0dOCy3/hsDLMmHBmcA++YeMQbn+czI45ZKGpP39A4rzMvMzHqJMof++krqQ2rscH5EvCLp9RLTbQL8LrcY7wP8KiKulTQbmCbpSOBx2m/MeDVwADAPeBE4AiAiFks6A5id650eEYvz62OAi4D+wDX5YWZmvUiZRPUzUkL5G3CjpBHACx1NFBGPADvWKF8E7FOjPIBj68xrCjClRvkcYPuOYjEzs56rzHVUZ0fEZhGxb04m84G9mx+amZlZyVZ/kvYDtgP6FYq/3ZSIzMzMCjpMVJLOAwYBewI/Bz4K3NrkuMzMzIByrf52j4hPAosi4uukroyGdzCNmZlZpyiTqJbl55ckvRV4CRjZtIjMzMwKypyjuibf4fcHwN3AclK/emZmZk3XYaKKiG/ml7+WdBXQv3Adk5mZWVPVTVSS3hsRN0o6sMY4ImJ6c0MzMzNrvEf1AeBG2nuOKApSl0dmZmZNVTdRRcSp+fnQrgvHzMxsRWV6Tz8jN6aoDA+WdFpzwzIzM0vKNE//UEQ8VxnId9n9cPNCMjMza1cmUa0tqW9lQFI/oG+D+mZmZp2mzHVUl5HuzjuF1IjiSOCSpkZlZmaWlbmO6tuS7iXdmkPAWRHxx6ZHZmZmRsne0yPiD8AfmhyLmZnZSsq0+hsn6QFJSyQ9L2mppOe7IjgzM7Mye1Q/BD4SEfc2OxgzM7NqZVr9PbUmSUrS2pLuyv0EImlLSbdJmivp8kqLQklvycPz8viRhXmckssfzDdxrJSPzWXzJJ28ujGamVnrKpOoZku6RNJBkg6sPFbhPb4IPFAY/h5wdkSMAp4ltSIkPz8bEVsBZ+d6SNoWOJh0h+GxwHk5+a0NnAvsD2wLHJLrmplZL1ImUW0IvA4cSOr37yDgY2VmLmk48EHgZ3lYwN7AFbnKxcD4/HpcHiaP3yfXHwdcFhEvR8SjwDxgl/yYFxGPRMQrpGb048rEZWZmPUeZ5ulr0tffj4CvAhvk4Q2B5yLitTzcBgzLr4cB8/N7viZpSa4/DLi1MM/iNPOrynetFYSkicBEgBEjRqzB4piZWVcr0+pvK0kzJP01D+8g6ZQS030IeDoi7igW16gaHYxb1fKVCyMmR8ToiBg9dOjQBlGbmVmrqZmoJK0j6Xt58GfAaaTDfwD3Ap8uMe/dgAMlPUY6LLc3aQ9rkKTKntxw4In8ug3YPL9/H2AgsLhYXjVNvXIzM+tF6u1RnUC6FxXAehFxc2VERATwakczjohTImJ4RIwkNYa4PiI+BdxA+zmuCcCV+fX0PEwef31+r+nAwblV4JbAKOB2YDYwKrci7Jvfw/fIMjPrZeolqvNIjSAAFuUEEQCSxgNPrsF7ngScIGke6RzUhbn8QmDDXH4CcDJARNwHTAPuB64Fjo2I5fk813HADFKrwmm5rpmZ9SJKOy0NKkhbAZOBMcBCYAFwcEQ81vTommD06NExZ86c7g7DehGdVut0afeJSY1/02arStIdETG6u96/Yau/fK3SjhGxt6SBpMT2XKNpzMzMOlPDVn8RsRw4Pr9e4iRlZmZdrcwFvzMkHS9pU0kDKo+mR2ZmZka5TmmPys8n0n79UgC+ctbMzJquo3NUawEHRcStjeqZmZk1S0fnqF4nXaRrZmbWLcqco5opyZ29mplZtyhzjuo4YKCkl4Fl5HNUETGkqZGZmZlRLlFt1PQozMzM6ihzm4/lkg4A9sxFsyLi2uaGZWZmlnSYqCSdSeoJ/Ve56KuSdo+IU5samXWKVurex137mNnqKHPo78PAu3IvFUiaAtwJOFGZmVnTlWn1B1DsiWKDurXMzMw6WZk9qrOAOyVdR2rxtxfwjWYGZWZmVlGmMcUvJd0A7EpKVN+IiH82PTIzMzNKHPqTdCDwQkT8NiJ+A/xL0oeaH5qZmVm5c1SnR8SSykC+1ccZzQvJzMysXZlEVatOmXNbZmZma6xMorpT0lmStpA0QtL3gbs6mkhSP0m3S/qrpPsknZbLt5R0m6S5ki6X1DeXvyUPz8vjRxbmdUouf1DSfoXysblsnqSTV3Xhzcys9ZVJVMflelcCf8hlny8x3cvA3hGxI7ATMFbSGOB7wNkRMQp4Fjgy1z8SeDYitgLOzvWQtC1wMLAdMBY4T9LaktYGzgX2B7YFDsl1zcysFynT6u8F4MuS1k2DsazMjCMigBfy4Dr5EcDewCdz+cXAN4HzgXH5NcAVwE8kKZdfFhHhp7K9AAANoklEQVQvA49KmgfskuvNi4hHACRdluveXyY+MzPrGcq0+ttO0mzgIaByWK7Unkve87kbeBqYCTwMPBcRr+UqbcCw/HoYMB8gj18CbFgsr5qmXnmtOCZKmiNpzsKFC8uEbmZmLaLMob+fAl+LiOERMQz4L2BymZlHxPKI2AkYTtoLeketavm5Vqd0sRrlteKYHBGjI2L00KFDOw7czMxaRplEtUFEzKwMRMT/sIrdKOUm7bOAMcAgSZVDjsOBJ/LrNmBzgDx+ILC4WF41Tb1yMzPrRcokqsdyq7vh+XEy8I+OJpI0VNKg/Lo/8H7gAeAG4GO52gRSIw2A6XmYPP76fJ5rOnBwbhW4JTAKuB2YDYzKrQj7khpcTC+xPGZm1oOUuR7qM6QLfK/Ow/8LHFFiuk2Bi3PrvLWAaRFxlaT7gcskfYvUzP3CXP9C4Be5scRiUuIhIu6TNI3USOI14NhCT+7HATOAtYEpEXFfibjMzKwHKdPqbxHlmqNXT3cP8K4a5Y/Q3mqvWP4ScFCdeZ0JnFmj/GraE6iZmfVC7mHC7E2olW6oCb6ppjVW9n5UZmZm3cKJyszMWlqZC363kjRD0l/z8A6STml+aGZmZuX2qH4GnAa8nofvBT7dtIjMzMwKyiSq9SLi5spAvrbp1eaFZGZm1q5MolqUL7QNAEnjgSebGpWZmVlWpnn6caSLcbeR9A9gAXBIU6MyMzPLylzwOw/YW9JAQLnfPjMzsy5RN1FJ+kKdcgAi4pwmxWRmZvaGRntUlfthjCJ1eVS5u++HgBubGZSZmVlF3UQVEV8HkDQD2Ckins/DXwcu75rwzMzsza5Mq78tgJcKwy8DWzYnHDMzsxWVafX3K+A2Sb8hNVH/D+CXTY3KzMwsK9Pq73RJ1wB75qKjI2J2c8MyMzNLSt3mIycmJyczM+ty7j3dzMxamhOVmZm1tKYlKkmbS7pB0gOS7pP0xVw+RNJMSXPz8+BcLknnSJon6R5JOxfmNSHXnytpQqH83ZLuzdOco8rVyGZm1mvUTVSSnpW0uMbjWUmLS8z7NeDEiHgHMAY4VtK2wMnAdRExCrguDwPsT7q4eBQwETg/xzEEmATsSrrweFIlueU6EwvTjV2VhTczs9bXqDHFRmsy44hYQOrAlohYKukBYBgwDtgrV7sYmAWclMun5tuI3CppkKRNc92ZEbEYQNJMYKykWcCAiLgll08FxgPXrEncZmbWWhr1TLG8OJz3bPoVip4o+yaSRgLvAm4DNslJjIhYIGnjXG0YML8wWVsua1TeVqO81vtPJO15MWLEiLJhm5lZCyhzK/oPSnqIlAhuy8/Xl30DSesDvwGOr3TDVK9qjbJYjfKVCyMmR8ToiBg9dOjQWlXMzKxFlWlMcSawG/BgRGwO7Ec6XNchSeuQktQlEfHbXPxUPqRHfn46l7cBmxcmH07aa2tUPrxGuZmZ9SJlEtVrEbEQWEuSImImsHNHE+UWeBcCD0TEfxdGTQcqLfcmAFcWyg/Lrf/GAEvyIcIZwL6SBudGFPsCM/K4pZLG5Pc6rDAvMzPrJcr0TLFE0nrATcBUSU8Dr5eYbjfgUOBeSXfnsq8B3wWmSToSeBw4KI+7GjgAmAe8CBwBEBGLJZ1Be88Yp1caVgDHABcB/UmNKNyQwsyslymTqMaTek8/nrTXMpB0T6qGIuImap9HAtinRv0Ajq0zrynAlBrlc4DtO4rFzMx6rjKH/k6JiOUR8WpEXJgP453Q7MDMzMygXKKqdRHtBzs7EDMzs1rqHvqTdBRwNLC1pDsLozYA5jQ7MDMzM2h8jmoaqYuj79DezRHA0oh4uvYkZmZmnatRzxTPAs8CB0naHtg9j/oz7dc+mZmZNVWZnimOJe1djciPaZI+3+zAzMzMoFzz9KOAXSLiBQBJ3wZuBs5rZmBmZj2ZTmutuw7FpJo9zPUIZVr9CXi1MPwq9a+PMjMz61SNWv31iYjXgF+QbrvxmzzqI6Tbc5iZmTVdo0N/twM7R8RZkm4A9iDtSR0dEbMbTGdmZtZpGiWqNw7v5cTk5GRmZl2uUaIaKqluV0lVPaKbmZk1RaNEtTawPm44YWZm3ahRoloQEad3WSRmZmY1NGqe7j0pMzPrdo0S1Ur3jDIzM+tqdRNV4S66ZmZm3aZMzxRmZmbdpmmJStIUSU9L+luhbIikmZLm5ufBuVySzpE0T9I9knYuTDMh158raUKh/N2S7s3TnCPJ59TMzHqhZu5RXcTKdwc+GbguIkaR7nVVuc/V/sCo/JgInA8psQGTgF2BXYBJleSW60wsTFfrTsRmZtbDNS1RRcT/AtXnucbR3k/gxcD4QvnUSG4FBknaFNgPmBkRi/P9sWYCY/O4ARFxS0QEMLUwLzMz60W6+hzVJhGxACA/b5zLhwHzC/Xaclmj8rYa5TVJmihpjqQ5CxcuXOOFMDOzrtMqjSlqnV+K1SivKSImR8ToiBg9dOjQ1QzRzMy6Q1cnqqfyYTvyc+WW9m3A5oV6w4EnOigfXqPczMx6ma5OVNOBSsu9CcCVhfLDcuu/McCSfGhwBrCvpMG5EcW+wIw8bqmkMbm132GFeZmZWS9S5lb0q0XSpcBewEaS2kit974LTJN0JPA4cFCufjVwADAPeBE4AtJFx5LOoP0WI6cXLkQ+htSysD9wTX6YmVkv07REFRGH1Bm1UtdMueXesXXmMwWYUqN8DrD9msRorUentdblcDGp7qlPM+sirdKYwszMrKam7VGZmXWmVtrb9p521/IelZmZtTQnKjMza2lOVGZm1tKcqMzMrKU5UZmZWUtzojIzs5bm5umroJWax4KbyJrZm4P3qMzMrKU5UZmZWUtzojIzs5bmRGVmZi3NicrMzFqaE5WZmbU0JyozM2tpTlRmZtbSnKjMzKyl9fhEJWmspAclzZN0cnfHY2ZmnatHJypJawPnAvsD2wKHSNq2e6MyM7PO1KMTFbALMC8iHomIV4DLgHHdHJOZmXUiRfTcjk0lfQwYGxGfzcOHArtGxHFV9SYCE/Pg24EHuzTQlW0EPNPNMayKnhYvOOau0tNi7mnxQmvEvEVEDO2uN+/pvafX6s58pcwbEZOByc0PpxxJcyJidHfHUVZPixccc1fpaTH3tHihZ8bc2Xr6ob82YPPC8HDgiW6KxczMmqCnJ6rZwChJW0rqCxwMTO/mmMzMrBP16EN/EfGapOOAGcDawJSIuK+bwyqjZQ5DltTT4gXH3FV6Wsw9LV7omTF3qh7dmMLMzHq/nn7oz8zMejknKjMza2lOVE0m6SOSQtI2hbKtJV2du316QNI0SZt0Z5wV1fFKGpmHzyjU2UjSq5J+0n2RrqhO3Msk3S3pfkkXSGqZ9V3S8hzb3yT9WtK6hXErrTPdqSrWP0gaVBi3naTrJT0kaa6kr0uqddlIl6sXd411Y6qkdbo51g1zPHdLelLSPwvDIyRdmT/fhyX9ODcee9NomR9uL3YIcBOpRSKS+gF/BM6PiK0i4h3A+UC3XUxXZYV4s0eADxWGDwJardFKrbgfjoidgB1IXWyN747A6lgWETtFxPbAK8DRhXG1lqU7FWNdDBwLIKk/qZXtdyNia2BH4D3A57st0hXVjDurrBvvJF3W8vHuCLAiIhblWHcCLgDOzq/fBVwB/D4iRgFbA+sDZ3ZftF3PiaqJJK0P7AYcSftG55PALRHxh0q9iLghIv7WDSGuoE68AMuAByRVLjr8BDCti8Orq0HcQGodCtwMbNXFoZX1Z3JsHS1LC7gFGJZffxL4S0T8CSAiXgSOA1qxc+hi3G+IiOXA7bXGtYi9gZci4ufwRrxfAj5T3Avv7Zyomms8cG1EPAQslrQzsD1wR/eGVVeteCsuAw6WNBxYTmtdWN0obvIPeh/g3u4IrhFJfUidKldia7gs3Sl3Ar0P7dcqbkfVuhwRDwPrSxrQxeHVVSPu4rh+wK7AtV0dV0m1PuPngcdp3T9enc6JqrkOIW3gyc+HdGMsZTSK91rgA7ns8i6OqyP14v43SXcDfwH+GBHXdEdwdfTPsc0hbXQuzOWtuM5UYl0EDAFm5nJRo8uyrBWue6kXN7SvG4uAxyPinu4IsIR6n3Gjz77X6dEX/LYySRuSdtu3lxSkC5IDOA14b3fGVkuDeM8DiIhXJN0BnEj6l/fh7oq1qIO4K+chWtGy6tjqLYukr0b3XvC4LCJ2kjQQuIp0rucc0nnKPYsVJb0NeCEilnZ9mCupFzfkdUPSpsAsSQdGRCv2anMf8NFiQd5b3Rx4uFsi6gbeo2qejwFTI2KLiBgZEZsDjwIPAe+R9MFKRaWbP76zuwLN6sU7vFDnh8BJEbGoWyKsrUzcPUW9Zdm9m+MCICKWAF8AvpxbyV0C7C7p/fBG44pzgLO6L8qV1Yi7OG4B6ZzaKd0RWwnXAetKOgzeOIz5Q+CifE7wTcGJqnkOAX5XVfYb0gnoDwH/mZub3g8cDjzdteGtpF68X6sMRMR9EXFxl0bVsQ7j7kEarTMtISLuAv4KHBwRy0j3fztV0oOk82yzgZa5bKGiGHeN0b8nJYM9ujaqjuU96Y8AB0maS/qj+xI9c/1ebe5CyczMWpr3qMzMrKU5UZmZWUtzojIzs5bmRGVmZi3NicrMzFqaE5WZmbU0JyozM2tp/x/++r12sQHEEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot bar norte\n",
    "plt.bar(norte, total_estado_norte, align='center', alpha=1, color='Green')\n",
    "plt.ylabel('Total de ocorrências criminais')\n",
    "plt.title('Ocorrências criminais da região Norte - período de 2015 a 2018')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Relação de ocorrências criminais por população de cada estado - Região Sudeste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(relac_sudeste, explode=(0, 0, 0.1, 0), labels=sudeste_pie, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=100)\n",
    "ax1.axis('equal')\n",
    "plt.title('Relação de ocorrências criminais por população de cada estado - Região Sudeste', loc='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.1. Relação de ocorrências criminais por população de cada estado - Região Sul."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(relac_sul, explode=(0, 0, 0.1), labels=sul_pie, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=100)\n",
    "ax1.axis('equal')\n",
    "plt.title('Relação de ocorrências criminais por população de cada estado - Região Sul', loc='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.2. Relação de ocorrências criminais por população de cada estado - Região Centro-Oeste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(relac_centro, explode=(0, 0, 0, 0.1), labels=centro_pie, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=100)\n",
    "ax1.axis('equal')\n",
    "plt.title('Relação de ocorrências criminais por população de cada estado - Região Centro-Oeste', loc='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.3. Relação de ocorrências criminais por população de cada estado - Região Norte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(relac_norte, explode=(0.1, 0, 0, 0, 0, 0, 0), labels=norte_pie, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=100)\n",
    "ax1.axis('equal')\n",
    "plt.title('Relação de ocorrências criminais por população de cada estado - Região Norte', loc='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.4. Relação de ocorrências criminais por população de cada estado - Região Nordeste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YUT7XDGkYqxGBl2CeA6lXsRvSHNZVwcgZZp6HJHc1gF2QDMQCMRzSe9AK6R1cx7r3hbABksHQNkjGjk/J54N+T7vhAKR7vh9SA3M1Y2y3HNbTe/W9fC0NkJ7/6YyxjuHymyD1gJsB3IIAy5wYY1YAV0L6CDZDej82+oT/B5IR1/2QPlRfQbJD6SndJlmGNyH1loohLbfyJ8NOAJfJMtbI+fk6EntQzvsTIrJCerYzAuTlK29LL+Tt6TtyKYBb5fL/T87HL4yxHwGsgjSN1iznd74crAWwFtIzOwCpAXeDHPa6/LeRiLaGQOZ+f5d64GkABUR0oizr0ZCe8X752joMfwFplDdRPv8CpIazI0C+Ae81k5YVroRkQNoAaeTuRLkxEgw3wufb2Nv85POnQnqezZBsHd7yCQ/Js5f/PxdSA3CXnNcbkOxdAkKdpyOiAyL6J4B7GGN7lJaFwwkWImKQpkl+D0Pe8yEZ+eT1FNdP2vMhWS/PDbVc3ZR5E6QVKJsiVSZnYEJEdwHIYoyd12NkTtBEnW9/eUi0GpKlJIfDiTHkOlwB4CilZeHEHiT5ABgvD4VPB/AHSN4hOSGkX/66w0QppGH/7oyqOBxO9PI5pKVKpyktCCcmMUAa6s+BNFV4L7hL95ATlcP+HA4nNiAiDyTDKII0z3+576oTIvozJEdJmYyxFmWk5HA4XeHKn8Ph9Bkiau1YDkvSypgbGGNH+oT/B5Kx1lOMsWeVkZLD4XQl6ub8ORxOzGKEZGkMACCiYkiOlW6E5HGMw+FECdE458/hcGIHPRFtg7RhSTY6e3JbAWnu9hsAJUSUwRirU0BGDofTBd7z53A4/aGdMTaRMTYS0uYxz8sOkQBpHfd62YHWW5A2leJwOFEAn/PncDh9xnfOXz7ucMSVBeAHSI54AMkBSVkkfQ1wOJzA8J4/h8MJCbIrVhUkj48rAJgYY0XyLweSn3N/rqQ5HE6E4XP+HA6nP3TM+QPScr/zGGMeIjoTh7ucfhvSVMBd4HA4isKH/TmcLshuel9kjJ0jH6shDV9/zxg7wSfeBgAZjLFZykjK4XA4fYMP+3M4h9MGYCwR6eXjxeiyDShJ2xRPBpBEREMiLB+Hw+H0C678ORz/fAjgePn/jiVrvpwG4F0A6xFgBzoOh8OJVrjy53D8sx7SHvc6AOMhbYnrS0eD4BVwBzYcDifG4Mqfw/EDY+wnAEWQFPsHvmFElAlp45pv5W2n3UQ0NuJCcjgcTh/h1v4cTmA2ArgHwHwAqT7nlwNIBrBX9mdjhDT0f2OE5evEps+LdZDW1/v7pUCq72pIy/FUXf53QXLN2yT/mrv8rQJQvnBBqTNyV8ThcMIFV/4cTmCeBtDCGPuZiOb7nF8BYAlj7N8AIBv8fYoIKf+iNe/nHDfkk1GnDX9vEoBRAEYDGAFJwYcT76bPi6sgbbu9G8Au+bdj4YJS7raXw4khuPLncALAGKsC8KDvOSIqAlAAYItPvL1EZCGiGYyxrrYB/aJozftaANMBHAFgLqQVBhk11rSfIXnSiyQCpGsvAHCUb8Cmz4vLAXzr89u1cEEpX0fM4UQpfJ0/hxNFFK15Xw1gHqQNco5gjE2XjQ47YaB6+wOLbzvsfBTRBGAzpIbApoULSn9UWB4Oh+MDV/4cjsIUrXk/AdKmOCcxxo4nouRg0j165JVtOi3iwytdyKiE5OHvLQDfLFxQ6lVYHg5nUMOVP4ejAEVr3o8HcDpjbBmAhUSk7W0et0y6viYvvT079NKFnToAGyA1BDYtXFDqUlgeDmfQwZU/hxNBita8P4cxdiHAlhEJCT2nCMwfi+/eN6O4MtY3ymkG8AKAdQsXlP6qtDAczmCBK38OJ8wUrXk/DcCFzOtZRYJqWKjyXZz+WsWZk74tCFV+UcDnAB4D8M7CBaVupYXhcAYyXPlzOGGiaM37o5nHfQ0E1Vl9GdbviWLdtoYbjng6LdT5RgH7ATwJ4MmFC0qre4rM4XB6D1f+HE6IKVrz/hzmdt4MlbiIZC9A4UCEzf3Y4utV4SxDYdwAXgZw+8IFpb8pLQyHM5Dgyp/DCRGF1707G173A6TWTItUmffP+ZPZGM+SIlWeQngAvNLWlnjr0hO38kYAhxMCuJMfDqefFF7zzljmdT0saOLmQ9BEtOy6FkOrMd4y0JW/ijGs2P3LvFlb/2v6CsDNJpOpSmmhOJxYhvf8OZw+UnjthgLmdjxAmriTlRp6X1nwSPlRI/cUKVF2JGlqSvpx544Tp8qHdgDrANxhMpkaFRSLw4lZ+K5+HE4vKVrzvjb/T+vvBwmlgjb+FCXn3MusQwf86J3Hw9iunbNG+ZzSAfgLgD0mk+kChcTicGIarvw5nF6Qd8kzp3id7ftUesNVJAiKK96ytpH98hUQC5SXJ9UylubPk2EKgKdNJtMmk8kUsiWUHM5ggA/7czhBkHf5CzlEwguq+KQFSsviCzE3e3zRn91qFYlKyxIO3G7Gvt9yYqvXm2zoIaodwC0A7jGZTNxHAIfTA7znz+H0QO7qp65R6Q1l0ab4AYCRmsxW0ay0HOFi797UA0EofkCaCrgTwI8mkyliqy04nFiF9/w5nABkLr81T0wrfENtSJuhtCzdcdXI2yrGFdQPJE9/AACXi3m/33JSO2OJvd28yAvgYQA3mEwmWxhE43BiHt7z53D8kH3ufSu1uaN/iXbFDwD7WvIGZAt+b1n6gT4ofkD6rv0JwPcmk2lEiMXicAYEXPlzOD6kL71On7vqibc12SOeFzT6mDCmK7MOi6xzgQjgdDJvXd28/vovGAvgB5PJdGooZOJwBhJ82J/DkUk/+a8TdHlj3lUlJOcrLUtvMKoabPcvvDVOaTlCye7dGdX1dcfkhjDLewCsMZlMnhDmyeHELLznzxn0xA2fQTNOveAi/dApm2NN8QOAxZMWZ3dgwMxtOxzMU183NyXE2V4DYJPJZMoMcb4cTkzClT9nUBM3fIbm8Zk16z4f/c6j49medqXl6SsNFl2L0jKEirLSnBogXh+GrI8E8D+TyTQ3lJkS0d+IaCcR/URE24hoBhF9SUS/ysfbiOiNUJbJ4fQXrvw5g5YLJmqS35772/fnFresjld7Nc/r79WkeBpisgddbUl3KC1DKLDbmbuhYU56GIvIBvCFyWRaFYrMiGgWgBMATGaMjQewCEClHHw2Y2yi/Ds9FOVxOKGCK3/OoOSa2drhN8zT/OeYQs/EjnMpGrfhJbrJqmJOr5Ky9YVyS8GAqMtlpXkHAL02zMWoAfzTZDJdG4K8sgE0MMYcAMAYa2CM7Q9BvhxOWBkQHwwOpzdcMV0z8/Lpmi+Hp6oOcwk7Kq4l8z7nHZX+0kUzZa0lMW/w194OV2Pj7IwIFvkPk8l0Rz/z+ARAPhHtIaJ1RHSkT9hLPsP+d/ezHA4npHBrf86g4orpmiXXztE+W5AodGv4ZWo+bt+z+pWFkZKrv4iwuR9bfL1KyU2G+suOnwsqm5uPVMLgch2Ay00mU58+hkSkAjAPwFEALgawBsD5AK5hjP0YKiE5nFDCe/6cQcHSEpGumqlZev1c7Qs9KX4AuDHxg7xpju9rIyFbKHAhTm21CTFr9GezwdXcPCtLoeIvBfC8yWTq00ZNjDEPY+xLxtjNAC4HcFpIpeNwwgBX/pwBz9ISkUakCsuun6N9Js8opAWTRi1A9VTcIwmZ7v2t4ZYvVNRZDDEja1dKfx9yANAouTnRSgBvmEymXtkbEFEJEQ33OTURwL6QSsbhhAGu/DkDmqUlIo1KE1ZeN0fzeLZB6NXacaPoiX9FdXO76HXEhGOYypasmNzNrrUVzubmGTlKywHgJADvm0wmXS/SJAB4joh2EdFPAEYDMMlhvnP+n4VYVg6nX/A5f86AZWmJSMNShPNumKd5IC1OSOxrPp9aCitWae6M+o1z5iR/UHXhtI/ylJajt2zfPqzK0jIrmuTeCOBU7g2QM5DhPX/OgGRpiSgkanH2n2dq/tEfxQ8Ai437Ci5rfzLqh3LL2kpiYi8CX6xWOFrMU7OVlqMLSwH8U2khOJxwwpU/Z8CxtEQkgXDK9XO1t+cnCiFxGPOXpC/y5zm+rglFXuHigKMw0e1hLqXl6A2lpSPqiUSV0nL44UKTyXSn0kJwOOGCK3/OQGTeVTM1t4zNUIVsqZ6KIDwe/8+kPNc+S6jyDDWM1GS2imal5QiWlhayW1qmRsNcfyDWmEym1UoLweGEA678OQOKpSXimJXjxbXzi9RjQp13vNqrf0W81aXztkZt77rGkhwz+xOUlY5sIFJF+zfoEZPJtERpITicUBPtFY/DCZqlJWL+0cWqe04frZ4ZrjLyde2pT3lujtr1/xWW3MNcE999dx1OP60cF/3hkONCi8WD666twXnnVuC6a2tgtR5u21Zb68Ilq6tw8R+r8IcLK/Huu9Kgh9PJsGZNDS76QyU2bDjkWuC+++rx22/BbTFgNpPNap0cyi17w4UKwGsmk2mc0oJwOKGEK3/OgGBpiZgyJVu4449TNAuEMHu5m2Ooybve/lB5OMvoK6XWYYetUz/mGAPuvLOzTd36V8yYNFmP554vwKTJeqx/5fDZgpQUNR58KBdP/DMPjzyai/WvmNHQ4MaPP9owYrgW/3wyDx+8b5XKLXWAeYHhw4NbJl9aOraJSIgVb4QGAO+ZTKZQbzPM4SgGV/6cmGdpiRiXrMO1f5qpXapRkSYSZa5O3FJ0jP3j6kiU1RvKbaMOW9kwfrweBmPnqr55sw1HHy0tDjj66AR8993hmxmKIkGjkfSz08nglZcFq1UEh4PB4zNY8OwzzTjv/OSgZGxqEtraWsdH09K+YCgA8LTSQnA4oYIrf05EISKP7PRkBxG9TkRx8vk+eadbWiKqAKz6yyztGUk6MoZU2G4gAh40PJ9W7NzTHKkyg6HFkx7ncKDHbYmbmz1ITZW82aamqmE2+1/SXlfnxqqLqnDWigqcuTwJaWlqTJmqR1OzG5dfXo1ly5OweXMbho/QIi0tOO+4ZWXjzUQx+ek5yWQyXaa0EBxOKIjJGsiJadrl/c3HAnAC6K819cLTRqlPn5ClKg6BbL1Cp2Lal7V3UIKnJbiJbgCWH97B/n9div1PXYr6jf8Aczs7hTO3C/Ub7kL1E6tQ8/xf4G6RzAvsVbuw/+nLUfPcn+FqlnaM9dpbUfvqTejqqKvBqgvZioSMDDWe/Fcenns+H5980ormJjdUKsLf/paJJ57Iw5FHxuOtN1twxhmJeGxdI24x1WLz5raA+TU0CNZ227hYmOsPxD0mk2m80kJwOP2FK3+OknwD4LBtdYNlaYlYVJxMq1aME6eHUKZekal1Jj3PbmoE8/ToKtNtbYDlv+8i67z7kfOHdYDXi7Zfvu4Up/WnTyDo4pF78ZMwTj0JzV8+CwCw/PA20k/+K5KOOBfW/30AADBvXo/EWcvQ1cShqiXd3pMsyckqNDZK3oAbG91ISup+qX1amhpFRSJ+/rlz1hs3WHD00Qbs2mmHWgRuvCkDL70YeLXh3r0TrT3JFuXoAKw3mUwxv4UyZ3DDlT9HEYhIDeBYAD/3Jf3SElEvCrj0ujnauZGa5w/E5ISGnNsc9wbnAdDrAXM7wbweMLcDqoTONmS237YgYexCAEDcyLmw79sOxhhIUEvp3A6QoIaruQYeayN0BYcboZdbCnqs17Nmx+GTT6SZlk8+acXs2Yfrsvp6NxwOafGA1erBjh0O5OUfutVWqwdbttiw+OgE2B0MAhGIJPsAf9TXqyz29jHRvK4/WEYBeFBpITic/sCVPyfS6IloG4AfAVQAeKq3GSwtEQnAmZdP1xyTbRCU2ga2E+ckbSs6xf5OZXdx1IY0GKefgurHLkDVI+eAtHHQD5ncKY6ntREqg+SUkAQVBG0cvO0WJM48A40fPQLLjxtgmHwCzF8/j6R5K/2WU9Zaovc9/vvttbjyiv2orHThzOX78OEHFpx5ZhK2/teG886twNb/2nDmiiQAwK+/OnDvPfUAgIp9Tlx+2X78cVUV/vLnGpyxLBFDhx5S/i+80IyzVyaBiDBtmh579jiw6qIqHHe8wa9c5XunBJ4PiD0uMplMy5QWgsPpK3xjH05EIaJWxthhPuio9aHwAAAgAElEQVQDnffH0hJx6rwC1d+vnq1ZHO5lfb3B6SXXya1rLLs041L9hXvsrah/+w6kn3Q9BG086jesRVzJHCSMOepgnP3/uhQZy26F2ijtPFz9xEXIOvc+qPSHbBntlTtg2/NvGCYdB/M3L4IEFZIX/AGqeMnaXoTN/dji61UURfemtlbdsufXFf3aYyEKaQEw3mQyVSgtCIfTW3jPnxNTLC0R0/VqrP7jFM30aFL8AKARmPiC7h4x2d3g19reXr4N6sRMqOISQSo14kbMgqP6l05xVIZUeKxSz5t5PfA6bBB0h3rSjDG0bH4ViXNWwPzdy0iaexbixxwFy3/fPRjHhTh1q01oQRRRXj4tZjwP9oJEAA8oLQSH0xe48ufEDPKyvotWT9VMTNRRktLy+CNV4zK+SP9nEZjrME97amM6nPt/hddlB2MM9n3bIabmd4oTN3wGWndsAgDYdn8LXcH4TgZ9bTs2QV88FSpdApjLAZAAEEn/+1BnSejT0slwUFOjaXY6hkXF9EwYOMVkMh2jtBAcTm/hyp8TUQIN7Qc55D+nJFWYfkShamKIxQopY+LNWXc77zxs/l+bU4K4kjmoefYq1Dx9GcAYDBOWwPzNi7D99j0AIGH80fC2W1H9xCpYfnwHSfPPP5je67KjdccmGCYdDwAwTjsZ9W/fAfNXz8Ew6bhOZVVasqNmL/qKfdOdPceKaR4ymUyKGp1yOL2Fz/lzYoKlJWISgDsfPU53bH6iELLd+sLJjeYTK17UrShQouw5yR9UXTjtI8W96FVXa5vKSpcNBre4a0wm011KC8HhBAvv+XNihdNPHKEujhXFDwAm47u5kx0/1itRdllbSVDGk+GmsmJm1IxAhJmbTCZTLDsv4gwyuPLnRD1LS8QRWhWOXD5WnKq0LL1BLUD1TNyDcenuAxFf4nbAUZjo9jBFtx6urNQ1ulwF6UrKEEHiAdyrtBAcTrBw5c+JamQjv5WrpmiGGrUU3M4xUUSi6Il/Rfi/NtHriGgPmJGazFYxsKu9cJfPGKoqZytVvFIsN5lM85UWgsMJBq78OdHOzBQ9DZ9fpIqpXr8vw+JaMx523xrxHQAPWJIUW15XWRlf73bn+vV3MMB5yGQyRdUSVA7HH1z5c6KWpSViPIAVF0wU85V24dtflhj3Fqxufyo4F8AhYp8lTxFrXq+XoapyTvebBQxcxgE4SWkhOJye4MqfE83MN2phnJmnmtxz1Ojn2qRN+XPs3x6IVHll1mJtpMrypaLCUOfxZA0GC/9A/FVpATicnuBL/ThRydISMQ7AvVfO0JQsGqo+qscEMUKrW2g/uv0O136xwNhz7P6RqKq33bfwtojuPuf1Mvz730uavZ4Mv/YZGzZswJ49exAfH49LL70UALBz50589dVXqK+vx6pVq5CT43/vny1btmDr1q0AgMmTJ2PmzJkAgE8//RS///47srKycMoppwAAtm/fjvb29oNxFGCRyWTapFThHE5P8J4/J1qZrVMjfk6+SrHtesNBgtqrX6++xaX12sJuid/iSY9zOODX1XC4KC9PrA2k+AFg4sSJWLmy84ZEGRkZWLZsGQoLA6/irKurw9atW7Fq1SqsXr0ae/bsQWNjI+x2O6qqqnDJJZeAMYba2lq4XC5s374d06ZNC92F9R7e++dENVz5c6KOpSWiFsBJ504Qs/UixSstT6gp0LenPum5OSLD/w1WXcR8/Hs8jNXsn6vvLk5hYSH0+s5R0tPTkZaW1m3e9fX1yMvLgyiKEAQBhYWF2L17N4gIHo8HjDG4XC6oVCps3rwZ06dPh0qlqNnBQpPJNKAarpyBBVf+nGhkhijAML9IPWA/nkcYqvOvtj9aHu5yqlvSIuZat7w8qdbrTQ3LdEZGRgb27dsHm80Gl8uF33//HS0tLdBqtRg1ahSeeOIJJCUlQavVYv/+/Rg5cmQ4xOgtvPfPiVrUSgvA4fiytEQUAZyyYpyYmaChsM+LK8llid8Vbm8p2f+ZbpH/Se4QUG4ppOmoClf2B3G7GTtQMy9sozTp6emYM2cOXnjhBWg0GmRmZkIQpL7LnDlzMGfOHADAxo0bMX/+fGzduhWlpaXIzMzEEUccES6xeuIkk8k02mQy7VJKAA4nELznz4k2pgBIPqooujfvCQUCgR42PJNa5CoNmzOestbh3Q7Dh4q9e1MPeL3Jhp5j9p3Jkyfj4osvxgUXXAC9Xo/U1M5uBGpqagAAqamp2L59O8444wzU1dWhsbExnGJ1BwG4TqnCOZzu4MqfEzXI3vxOnZItCKlxwkDdArYTehXTviLezuI9FkfPsXvPvvaRSSzMS3pcLuatPTA37KM0bW2Sl+SWlhb88ssvGDt2bKfwL774AkcddRS8Xi86LpmI4HIp6uV4uclkisrtpzmDGz7sz4kmhgFIP2GEOFxpQSJJts6R/GzrTdWns/tziULbHnciTmy1CWZDPAubAtpbln6AscSgpi7efPNNlJeXw2az4b777sP8+fOh1+vx4Ycfwmaz4eWXX0ZWVhZWrlwJq9WKjRs34uyzzwYAvPbaa7DZbFCpVDjuuOM6GQ7u3r0bOTk5MBikwYe8vDw89thjyMzMRFaWou1IHYAVAB5TUggOpyt8nT8nalhaIl4oCpj10mn6s3Rqiuj69GjgGfPk8lt01xSFOt8bxv2tqjjbGpbtfZ1O5v3P96faGUsYdM+rF/xoMpkUXXfI4XSFD/tzogLZqc+spSXqxEgp/gs3tCPjbivGrms9eO71nS6MWdcK4RYLftzvfy8eu5th+pOtmPB4K8asa8XNX9gPhp39lg3jH2vFDZsOnbvtKwc27O556Pn8xK1FS+3vhtw6r9KS5Q51nh2UlWXWcMXfAwxTHvm/e0crLQaH4wtX/pxoYRwA1RGF6vGRKvD8iSI+WtlZb43NEPDWMj2OKAy8RlyrAj4/Lx7bVydg28Xx+KjUjS1VbvxUKzUWfrokAd9UeNBiZ6ixevGf/R6cNFLsUR4i4G7D+syRzp1N/buyzpRZhoZles/hYJ76urmD2Y1vtzjbHdbc9qT/LnPOqj7ZOf1cpeXhcHzhyp8TLSzKSiB3QSJFbL7/iEI1UvSdN2Abla5CSVr3zmGICAkaKZ3LC7g8klm3KADtLsDLGJweBpUA/N8XDtw6P3gX+1oVE1/U/UOV6G4O2Y58e9tKwrIEr6w0pwaIj8hqgljB43bbdC20b07LsMZL6TjDsTRlipHF5QE4s2rNN3y3P07UwA3+OIqztETMAjDstFFilkoIscVbmPB4Gab8sw2/N3lx2TQNZuRJVakgUcDkJ9pwzngRvzd5wQBMyu6dp7k0jSvxJdeNNSeyh3SMVP1WGDWOoiSPh7lVKgpZfbfbmbuhYU56qPKLZbxer0fV5q0Z7clXTdUMzxK1oj8/xYUAZgP4LsLicTh+4cqfEw1MBeAdnynEzLyoSiBsW50As53hlFdt2FHnwdgMFR5YojsY58RXbHjiBB3+/rUD22s9WDxUjVVTgtuZeGx8c/Zd5jvLr9PdWNRfWRmpydwqmlMT3d370O0FZaV5BwB9WIwIYwHGGLzt7tpCZ6pjtnp0llGMz0PPMztngit/TpQQE70szsBlaYkoAFiUrIMlM4FiTpkk6QjzC9X46PfONnUbdrswNVuFNifDjnoPXjsjDi/85ILNFfzqmmVJu4pW2F+rDIWcNZakkG3w094OV2Pj7IxQ5RdLuB0uc5JZW35s6wTrxcKSzCW6aQVGdXxwLTrguLAKx+H0Aq78OUqTB8CwaKg6Wwj1IvcwUd/mhdkuKfF2F8Nne90YmXZIdJeH4cHvnbh2jgY2l2QPAABeBjj9LyAIyK3Gd7InOrfW91fmfS15IVvTW/p7wQFAF6zCi3k8bo9d24J9M81DG1azJUmn6+YW5YnpffFmOLRqzTfFIReQw+kDfNifozQjANCELNWwSBe84k0bviz3oMHGkHefFbfM1yJFT7jiQzvqbQzHv2zDxCwBH6+Mx36rFxdttOODs+NQ08pw3js2eLySQl82RsQJIw6N+T76gxPnTRARJxLGZwpgAMY91orjhqmRpOvdFL4oQP2M/gH9Isc9tkZ1Rp+X1JVZizXAtr4mP4jNBldz86wB733R6/V6hDZvzShPrjBVU5Kl9T+P3xeOBnf4w4kCuJMfjqIsLRFvAJDx0qn68wxa4m5QA7DHZqg9Dg+nuQVNn/apTRTqbPctur3f6/F//mlIpdk8N7+/+UQrbpurrsCR0j5bHJWVpDYEv0wjeN7OWzvv1DDky+H0ipgYZuUMTGTHPsXjMgQVV/zdMyLOmvmg67bqvqZv8WbEOZzo17x/ayuczc0zwrYDoVK4na6WRLNYfrRlnHW1sCTjOP30wjApfgBYULXmmz414DicUMKH/TlKUgyA5hWq+DxoEByfWFqwzfzsvid15/dpCLrBorPkptn73PsvLR1WRyTGnFGmPzxuj11vU9VOZEVx47XF6dAhMUJFJwKYAWBzhMrjcPzClT9HScYC8IxKi/x8f6yyxvhJ/k/WktrvtbMye5t2vyXVkZvWt8EDqxWOFvPUbIphNzXM6/WSzVszwp2DGZqSbK1GE6p5/N6yGFz5cxSGD/tzFGFpiUgApgqEphwDKfURjjlUAoR/xa8zZLuqrb1Nu9dS2GfVXVo6op5IjMnhane7qy7bbNh3mm2G8yL1MblH6MblagWNkt++hQqWzeEA4MqfoxwZAJInZArxoooGzbKxUGBQe+LWq292aL3tvdqwp8w6ok+ueFtayG5pmRpTc/1up8tiNKvLF1vGWFbTkozjddMLU9RGXc8pI8Ik7uqXozR82J+jFIUAMC5TFVNKJVoo1NvSnrDcXHm+5h9BW97vax+ZxBhjRL0bvC8rHdlApIr6uX6Px+PQtQm1E7xFuvHaoRmCTjAqLVMAEgAMBVCqtCCcwQtX/hylKAbgHposcOXfR+Ybq/KvMj9W/oDukqJg4jsRJ7bahBZDPAvauM1sJpvVOjk3Wuf6GWNetHlqRriy2XTtyGy9RlOgtExBMgFc+XMUhCt/jlKUALA+npaoGU3eiukeZ/xkuJJ1MbKxT7RwZeI3hdtbSvZ/oVsQVCOqzpJgNcRbg1b+paVjm4iEqOv1u9tdDbmOpLbZ6lGZqerE3Bj8kk0A8JbSQnAGL7FXZTgxz9ISUXQDRSqgYsuQpJH/FgX9UwDgZZ44m7sxx+ZsG+1weqe6nNpZzJmUJTC+bWwABAI9mvB0ypL2IS0V4pAelXqlJctdnB2crWBTk9DW1jo+L1p6/W6n25pk0zXOoBHJQ7TZadAhZBsVKcAEpQXgDG648udEnK1aTQ4BY9XxwuwEUTik2AVS2RLE1N8TxNTfEY+N8mm1w9Oa2uayDGt3Oic5HaoZXpdhLLkS1b2cux6oxKm9uvXibbaFnoec7aqEbo0n91qGqOfjt6DyLSsbbyYS4kMiZB/xeDxObRsdGO8t0k7UFmcKOqEvPvWjkYlKC8AZ3HD3vpyIY5xknATgCsPYeH3KorQz+5IHeZgroc3VnG9z2sc4nGy6x6mbxZzJiQIG7cqB762Z1cvU9+Z2tz9SruY3863zH+7Rm2JDg2D9ZdfZiihaxhhjNnfNcFe2d6Y4Mluv0sbkEsMgSM5bO8+stBCcwQnv+XOUIAcAEzO1fXbpy1QkWo2ajF1GDXYBeB0AGIPG7mnJaHNZS+wO92SXU5zpdRpGCN5otfoOKTMMtbk3mh8s/7vuz0WB4ux3DEn0eJhbpaJu63753klWABFV/m67qzHHntg6SzUqI11MysFAVfmHGAPgO6WF4AxOuPLnKMFQADZVgiq0LlWJ4NSrE6v06sQq6LFJPi24vPZEm8tcZHM6xjucNMPriJvKXMl6gQacerko8YeibS0fVL2vO86vkR4jNZlbRXNqojvgfHldndrS3j46Iqsw3C53a6JN2zCNhicN0+SmQofUSJQbJUSdISVn8MCVP0cJCgC0qXSqiMwne0VB15yozWpO1OJ/AJ4DAC/z6tvdTdk2V9tIu9Mz1eXQzGLOpDyB9XvnOyUhAu41vJSxp21o02+akSn+4tS0JNlSExsC5lFePrkNQNhGS7wej1NsowPjvAWaSdphWSqtKiFcZUU52UoLwBm8cOXPiSjGSUYBQAqAKkErKPfRF0hojxdTyuLFlDLE4QP5tMrpaUtpc7UUy8aF0z2u+AnkShZjyLhQp2Kal7RrVYtc99st6uTDvNpVWHK9Y+Ff+dceEFsc9pKQKyV5Hv9AsTPTPVMzKjteo4uV9fjhhCt/jmJw5c+JNHoABICRhhS1JPeHR6OKr9eo4uuTddgC4DEA8DB3gs1tzrM5bWMcTjbN7dDNhDMplRCubV/7TYbWmfiC+6aak9mDWYxUnRouZdZhWmC733T79k1rB0K3w53b7mrMthutM1WjMjPF5GzwRZu+cOXPUQyu/DmRJgEAAwBBo2DPvzeoSN1qENN2G0TsRjzelE+Ldo8lvc1pHW53uqY4neoZXqdhJLkThSgZJJgQ35R9h/mufX/V3dBp46S9tpF+h/RrajTNDkdxVn/LdbvcbUabpn4qhieO0OYNtnn83sCVP0cxuPLnRJp4AAwEIpFien7dpVMZ9+v0xv3Q4yv5HLm9jsQ2t7mg3Wkf53DQDI9TPw2u5ARSpq6tSNpR+D/zm5Wv6U47uAeA2ZMZ73DCptWg0/3ft2+Gq6/leL1el7qNHRjrKVBP1g7PUmtVRf0Qe7DAlT9HMbjy50SaBACkTlLH9XaDmViAqQWtOVGTaU7U4Cck4CUAYIzp2j3NWW3OthK70z3F5dTMgtNYRN6IjHzcbnwz+xfrsIaftRMOWvg3WHSW3DT7QeVfXa1tcjmLMnqTL2OMeW3u2qHODOcszaicBFGfDzGUkg94uPLnKAZX/pxIEw+AxCQx6ub7wwYR2ePUyeVx6uRyxOFj+bTg9NpS2lwtQ9qdjglOh2qmxxk/Aa6kUO9voBGgfl5/r3ah825HkzpTCwD7LamO3LTqg3EqKmZ6gs3PbXc1Z9oNllmqUelZYkoWn8fvMylVa77R5K2d5ww2ARGdAmlPgFGMsd1EVATgPcbY2DDJyBmgcOXPiTRGAFAZIrPML5rxaoS4Bo02riFZix9gwL+Ag/sb5NqcttF2p2ea26GbxVyJGf3c3yBZ4za86Lq5can3AY1b0FF5SyFNg6T8Kyv1jW5XQXp36d1ut83QJtZPwTDDSG1BCnRI7o88feHqD9ZiU+lmpMYlY9MfngMANLdbcNkGEyotNcg3ZmPdybcgSdfZN9HmfVtxy+ePHDwubazAI0tvxpIR83DFu7did30ZFhbPxpoj/wgAeOC75zAqYyiOGT4vEpelBxC08gewAsC3AM4EYAqHQJzBAVf+nEiTBsApqAeMj/bQIu9v8FuCmPob4rFBPq22e6xpNpdlWLvTNcnlUM/0OBPGkjupN8aFo+MtqXc0mdqvi1urL2sboQM2gzGGqspZfuN7vV63qo0dGOPJV03RDMsStWKh34gR4oxxS3D+5FNw1ft3HDy3bstLmFM0GZfNXIlHt7yIdVtexA3zL+mUbnbhZHx8wdMApMbCvH+uwJFDpuGXOmlH3U8vfBanvnQ5LI5WtLvs2F7zC66ac16kLivoFSNElABgDoCjAGwEV/6cfsCVPyfSJANwMfA9JXqDW6cyHNCpDAegw7cAHoa0v4GhzdVcYHO2j3U4Md3t0E+HKzlRCDzzviylQr/H/K+a5+nMNMYYKivi693u3IO9fsYYvO3u2iHOdMdM9ahsoxiXFy3z+DPzJ6KypabTuU9+/xavrXgQAHD62CVY9sqfDlP+vnzw65c4augM6EUd1IIKdrcDXuaFy+OCigTc+83TuHrehWG9ji70ZrnoyQA+YoztIaImIpoMoClMcnEGOFz5cyKNGoAXXPv3G6Yi0WLUZOwwarADwHoAYIxp2j3mTJurtcTu8ExxOdUzvM7E4cIh48I1xs8zd1hHWJrMgr6qao4KANwOV3NGe0LLTGFkWo4mLROHuQaKThrampGZINkxZiakobGtudv4G3/ZhFXTlgMAhqcVIdeQiWOfvQinjjka5c3VYGAYmzki7HL70BvlvwLAA/L/6+XjR0MuEWdQwJU/hzOQICJnnDqpMk6dVAk9PpNPC06vw9jsYll2r2MCa2enaZ/T7NoxqUZjzqYZGMpGa4tSlZjHjyS1rQ3YXV+GI4dMP3jOtOjKg/9f8MYa3HnMNXho8/P4pa4U84qm4qyJJ4ZbrKCMO4koFcACAGOJiAFQQfKXsS6MsnEGMCG1KuZwgoABIN7zjyxejaA1Z2p1uwv1ia8XJCXt3GX0tm9KsDbtrHbamls1To/Lq7SMfSEtPhm1rZKr4trWBqTGB26/vLf7CywZcQRE1eF9no9/+wbjs0rQ7mrHrw178djJt+DNnR+j3WUPm+y95HQAzzPGChljRYyxfAB7wTcH4vQRrvw5kYYrfQUp/tnhuusBJ1u+R5WgT5udt2Xv//778HcvPHTNB2vXbvjlszf2NVf/4vZ63ErLGSyLh83BGzs+AgC8seMjHD1sbsC4G3ZtwkmjFh523uVx4+kf38DqGSvQ7naAIBlRMsbg9PTZ71GoWQHg7S7n3gRwA4ASIqry+Z0RefE4sQYf9udEGq78FUDX5nWcu97RtqBWnSIQwcuA5NQxhhRdJmz2ZoPV2WZ+Z9dnO9/Z9dlOozZBs7B41ogJWSNH5yZmDVcLfrrKCnDZxluwpeJ/aGpvwbRHT8PVcy/AZTPPxiUbbsb6n95HrjETj510KwBge81uvLhtA+4+9noAQGVLDfZb6zCzYOJh+T639S2cPnYJ9KIOo9KLwcCw6KnzsKB4JhJ1YV+UElR9YIzN93PuIQAPhVogzuCAGOPfYk7kME4yXgFguGGCISnlqJRzlJZnMDDzG0fdhd8iJQmHlLhVk9zww+zb0w407L5ly/aX7ABGQNpwyQLADFkpRWtDYACRlbd2Xq3SQnAGH7wicxSB8VZn2Empc1svecXFJtjEw9z21sfntgNAVtrIwk9/++6CxcPnJAEYA2AefBoCFker+e1dn+54e9enOw42BLJHjckzZg5T8YZAKOBL9TiKwCsvJ9IwAOS1eaPGkmqgQR4vO3Gjs/70X4Q0HYl+7Xrq9Fkq+d/FAPDpb9+ZAXwH4Ds/DQEAsPKGQMhpzVs7L2qMCjiDCz7sz4koxknGSwGMElNFR845OVeHu7yqp6pg3WaF2qjG8L8PBwC0/KcFde/UwVHjQPH/FUM/xL/n3IaPG9D8VTNAgC5Ph9w/5ELQCKh8vBL2KjsMEw3IOl3aAbduQx10+ToYJ/vdLTdiDPnN3XTZ2259gUfdrTvgT4efc0CVO7Nj+94xlz2+YJe/eD4NgSMADJdPW9FlamBR8ewR47NH8oZA76jIWztPUa+JnMELr6ScSNMCQHQ1u5oYYyzcO/slz01G6sJUVD1ZdfCcNk+LgisKUP1sdcB0rmYXGj9txPA7hkPQCKh4tAIt37dAXyTp1OG3D0fZHWXw2DzwOr1oL2tHxkm92hQvpGjbvc5z1jssC2vUaSrquVo749JVPq2DxQA6Kf97l5+wHsDuJZOTX7v61fc6RgSScWhE4GBDwOJoNb+165Mdb+36ZAdvCPQKPuTPUQxeMTmRpgGABl4w5mI20lBYN/iJL4mHs77zvim6nODc1zEvg9fpBakIzMmgTlYDKoC5GJiXgbkZIAB1b9Uh41TlFP+0zc66i75iyckQ0xBkU0rQp2p8DhcDeHBMbpYGwHgAv104d+p+ADcDuPne5SfsAvDaksnJr1/96nvfAvg2mIZAos6gWTh01ogJ2SPH5PKGgD+48ucoBq+MnEjT0vGP1+m1ChohKnf3E5NFpC1Jw56r94A0hIQxCTCMlZZ9iSkiSm8uRdLsJDhrpYaFvjDy+9omN3haV7/s9ExqO9ygrzs8jJioMST4nDry0dWfi5AcxlwNoP2HvZWYNiS/I3w0pE1kTPcuP2EngNflEYGuDYEjAAyDbCzYYrd2bggUzyqZkDVyNG8IHIQrf45i8ArIiTStALwA4HV4W5HQQ2yF8LR5YP2fFSPuHgFVnAoVj1bAvNmMpNlJyD47+2C8fffvQ875OajbWAd7pR0JYxKQMj8lvMJ5vez495z1y3YIafoABn3d0axLtQqCytc4IQHALACbIS31a/i1tiFpSmGeVxCoa/5j5F/vGwI7P/n5rZ2f/MwbAgdpVFoAzuBlsFY6jnK0dvzjtXtbu4uoJK07WyGmiVAbpSpinGqE7XcbkmYnHYxj2WqBfogeXocXjmoHCi4rQNkdZUialQRBGx7nmQWl7ubL33Jri9zqjGCH+LvSGJfTBqCrZeLRO6sPfD0mN2sHgBKn21NvdTgqEvW6om6y6toQCDQ1wBsC/ilXWgDO4GUwVTROdGCFpATgbY9e5S+mimgvbYfX4QVpCG272g4a+wEAczM0ftqIwqsK4ah14KAiZlJYr/ZqC0Yeu9e18lWH+ehqdXowBn3d0Ryf6/RzejGAGwH8F8AkAGiwtpX1oPx9GQPgFgC3BNkQAABrgIZAh42Ayn9RA4bdSgvAGbzwpX6ciGKcZNQCeBzAvpQFKdMN4w3HhrO8yscq0ba7De5WN9RGNTJOzoA6QY39L+6Hx+qBECdAX6BH0TVFcDW7UP1MNYr+UgQAqH27Fi3ft4BUBF2BDrkX5kKQR9kbPm6AKl6F5LnJYIyh6vEq2KvtMIw3IGtZVjcS9Z7J3zvqVn2O5FSoxFDkt6nkgn2UPbXrEjMvgLR1756lBXAHgIohack5R40sXtXP4nYAeB3Aa1e/+t5uAPBpCBwJoFiO12n5YKLOoFlUPLtkfFbJQG4IjMpbO483ADiKwJU/J6IYJxkJkvKvM0wwFHIXv5RADLoAACAASURBVIExNnraVr/idE21ikk9xw6ejyZdv1+TWJDjJ+j0de+e9RaA+wHYCXCcM2vStWqVKlTWjIEaAmMhjQgMpoaAG0Acd/LDUQqu/DlBQ0RZAB4AMA2AA9Kc5VUAtgP41SfqfYyx5wPlY5xkvBOASkwVhUg4+ok1mNeLYz901q7YLqTrSQi58cBHs9aaNVqDvwbFE5c9vmD1mNyscwDMBVCzdMKoM9IM8aNDLQOkhsBrAF7voSFggbRChAFAks6oXVg8a8QAaAjsyVs7r0RpITiDFz7nzwkK2RnP2wCeY4ydKZ+bCCATQClj7PDt0gJTCWC0q9HV4HV5bYIoxIVe4tgkb6+7+fI33JqhbnVmXw36usMNwStq4gO5ITxa/vszgKMAoNbSWhom5T9W/t167/ITfoa0auD1q1997xsA33RpCAyDpPwtZrul5c2dH//85s6Pfz7UEBg5JteYEWsNAT7cz1EUrvw5wXIUABdj7PGOE4yxbURU1Ie8fgcwBQA8rZ46IVnoSx4DCtHuda943dG8pFKdpiZ12LweNunSLURCoGmEIY+u/rwYQJl8TGUNTWVjcjPDJU4H4+Sfb0PgNZ+GQAo6Gwv6bQgsKp5VMi5r5OgYaQhw5c9RFK78OcEyFpIluD+KiWibz/EVjLFvusmrpuMft9l9QEwWi0IgX8wy4Udn/cWfscQ0JqaHo7fvS2N8jg1AdzYEi3dWH3h8TG5WFYCEemub2e5yNelEMczOCw7S54bAGzs//umNnR//FCMNAa78OYrClT8nFPR22L+u4x9HnaM60MY6Ax1Ds8f2x5edjhkWMT1SZZrjc3oyMFsMySDzBwAnA7A2tbWX5iRFTPn70rUh8FqXqYFYbgj8R2kBOIMbrvw5wbITwOkhyqsBgAeAyl5h348ZIco1RmBeL47+2Fl71v+EtHgSI2rv0BaXjR4sCBc8uvpzFXwMOPebLWU5ScZpYRatJzoaArfdu/yEn3C4jUDXhgAAtPhrCIzPHjkmx5BRrFRDgDHWQER+d1HkcCIFt/bnBIVs8LcFwL8YY0/K56YBiAPwKGNsbG/yM04y3gAgHUBL/mX51wuiENxuOzFOzj63+bLX3eJwl1qRPQ0+nPzXGq0xL7uHaLPWvXvWVgCPAjgQpxHVy6eNv47oMFe/0cBPOLR8cA8A+DQEjgQwFJILphZ0WTWwaJi0fFCBhsA7eWvnnRLB8jicw+A9f05QyNvvngLgASJaA8COQ0v9us75P80Ye6iHLHcBOBFAi9virtKkaob1ED+mUTm9njNfdzQdvy+8Bn09QbrkYBodi3dWH9gyJjfrFwBFNqerqdXhrDbotPk9pow84+Wf74hAVxuBjlUDBxsCZrul5Y0dH/30xo6PfkrWJ2oXFs+SGwKZxSpBCHdD4Osw58/h9AhX/pygYYztB7DMT1BfJu3LO/5x1DhKB7LyH/s/Z/3qj5kxIwIGfd3hgsojinpDEFEXA7gNkoHnWABNDa1tZVGq/H3p2hDosBH4GsDXXRoCHX4EWprbWyLdEODKn6M4fNifowjGScY0AP8AUKHL16VmnpZ5udIyhZr4Fo9t1ctOx2yzmKy0LABwIC6nedf0vwUjiwtAyrp3z0qE1AioGJaRmn/EiCEXhlfCsLEdh6YGfgMOTg2MAzAPPg0B+EwNhKMhwBizEFFK3tp5nv7mxeH0B678OYogu/m9Sz605V2cd6VKr4oKJRkKFnziqF35I6UlUNiHkINmR9rU6rqxF+QGGf3Ede+e9QGABwG0qohcK2dNuk4lCCHesijidNcQ6JgaAPw0BBYVzx45LmvE6H42BD7KWzsvrPtZcDjBwJU/RzGMk4ynATgWQFXGKRnH6gv105WWqb9kVbrNl73uVpc41AlKy9KV7wqX7nMMOabrhj6BePiyxxdcOSY360IA0wEcOHnS6DNT4uMGkktafw2BVHS2EQBC2xD4a97aeWtDdgUcTh/hc/4cJdkB4DgAaC9v/y2Wlb/K5fWc8aaj6cQydZqooEFfd9jistELLbVY/rsdkp9/1FpaSweY8p8g/26/d/kJ23HIRuArAF/5aQgwAJbm9paW13d8uP31HR9u72gIjM8qGZNtyBgaREPg3XBeEIcTLLznz1EM4ySjBsAjAGpJQ5R/cf711N/N6hVg1E+u+ks+8BqzmCqqh8Q/nPK3A1pDTm/2G85f9+5Z/9/encfHXdeJH39958gxySRNmrZpai964NIiLYesQIHqtsoKuv7UZXd1F0V3t6ure6Auq79FXRbQtVVBC+uFuyuHpYICpRSRplBA6EF6pWeSJmmmmcyRycx37uP72T++kzYtPZLm+E4y7+fjMY8kc3y/70mbeX8/1/sTA74LdEyvdk++6dKLJ9zcjDPYhdkjsP4cPQKK0zYdOt+FgFLqyMxvX79wDN+HEGc17j5oxcQRaYqkq5ZW7QUuVmkVyIQyR0vqShZYHddgleu55GcfS8evDTqm2LSCGdo/K1vZpKHWFljR7PH+fNGM+m6gsjusB1PZbLjU4agejfgKyJL87Z41t968i5PLBwfdI1BbPqnMrCx48aLp7inz7Da7TdO0pyx7R0KcRpK/sNo2YClA6njqyHhJ/te/lOy5bZttshtnrZXL9wYrpTmzDseglvkNtBL4OeaSv5uAaCiWaKuvdi8d8QAL1+kXAmcaGuhfNdB/IRDuTfRFntj3/O4n9j2/u7rMPf9vrrq15ZKp89dZ9i6EOI10+wtLVS2tqgXWAB3OOqd7+iem/1O+mmBBmurJRj63LqNdknIONZFaylMxs/fQVXcOtT6/H5j24LN/8U7gS8CxpbMaFi2d1TBSZZ7Hs13AE5hDAy1wYmhg4IUAmMMCVcA/vnjktYgVgQpxJtLyF5aKNEV6q5ZWHQcqM4GMnglmjpTUlRTcuKgtYxgf/XU6+OEWe12J5izYi5Oz6XU1xIGhJv8pmK3eA/mfbS2+YNuSmdNVIV+gjZH+HoF719x6cxMn9xrYAmwZcCFwPXBAEr8oNJL8RSF4Ffg4EI0djL1Vcl1hJf+FzZnA5zbkKhsMh6UV+oYjXNGQvcCXrmj2eJsWzag/AkzXk6lQLJ3xVpaWnG9/gGKyNH8beCHwxIALgXH6v0ZMZIW4UYcoPtvzX7XIW5HDRsrQLY0mrzyaS37uJ4ngvz9NXYPhGNcbD8Ur6i/0b71/yd92wA0QjMZaRySoiWkpcC/QsubWm99ac+vNd37g8pqLzvciIcaaJH9huUhTJIC55r8OA5U8ltx1vteMtmu3pHp+8IOs48aAc7JtAvRw58rrLnQZ4nVrV20uB1rIL2nzhCJtIxbYxLYUuI8BFwJrbr153vleJMRYkG5/UShewhwjJfJWpKl8XvkyK4aVJ3uz+ud/mWFxwjltzE8+imxlky604mAZ5gS2l4A04Gz1Bzuvvmhmxm6zOUcswImvf2jgvjW33vwWJ+sISC+KsIS0/EWh2A/EgLLU8VQoG8oeHcuTa1nD+MiTSd/3H1aVixPjayb/+SRspWmno2yoa/wHWtHs8eYwq/3VZHJGLpJMdYxQeMXock72COxcc+vNn7E6IFF8JPmLghBpimSA32HOMCd2OLZzrM4972AmuOa76fSfH3ZMLZ2As9gDrvrhzjTvH/dvwuwJwB+JSdf/yLgcmGt1EKL4SPIXheQNzP+TWmRn5KCRNmKjebKymJH6258lgvc8xeR35Mb3hL5z6a2YkRjmId61dtXmqcCJLuqOYEi6q0fOI1YHIIqPJH9RMCJNES9wGKhRGZWLH4m/NlrnunprqueBBzK29/kmxoS+cwm7Goa7d7yG2foP5m+uY6GwL53NRYcdXJEzlNp1x7oNB62OQxQfSf6i0LyAWRGN0Cuh7UbaGNEEU+vL6l+9PxG541X7tEnYi2LCWrJi2kj8na9o9ngVsAOoAeiLJ6Trf5hsmva/VscgipMkf1Fo9gA9QJWRMrKxw7ERaf1rOUN96DdJ3/d/alQsiTurRuKY40WuvG4khjT6x/2bwdwZ2BvWpet/GJRSOeCXVschipMkf1FQIk2RLOYyqFqA0CuhHcNt/c89ku39zpp08pMHHFPLNFvR/Z+3l1aPxOqFhrWrNl8C9K/C0Fr9vdLyHwYFm+5Yt6Hb6jhEcSq6D0IxLjSRb/2rtMrGDsa2XshBShNG+jM/TwTuXa9qZ+Uc5SMb4klf6+7mupYjfOjoyVz4QMDPnxw9ykfaj/LZY534spkzvna1z8ctR9u4+Wgb9/T0oJQibRj8zbFjfOhoG4+HQiee+3VvN/uTySHFFreXJx2O0pF67yuaPd4Y0AZUheKJaDyd9o3QsYuOTdP+w+oYRPGSIj+i4ESaIrmqpVVPAF8AIqGtoZ0VF1dcZyu1DboFe9Xrad9nX1Y1NTjrRrse/0eqq/lETQ13dh8/cd/tNbV8sW4KAL8I9fJgIMg36utPeV1TIk5TIsFv5pgrvT7Z2cH2RJyoYXBJWRn/VfcOPtrRzp/X1HAwmcQALikbWg++z9Wgk1+eNwJWAvdjlvr9UyAcjCbaXLUlUy/kYOu27WZ/t4/K0hK+/IEbAHhh32HePNpJZalZkPCmSy/mD6afenhfJMojbzSd+DkYjfP+xQu5fuFcNuw+wCGvn4ZJVfz51UsA2NneRTydYdnCwllRl8nl3rzzV8+/YXUconhJ8heFahfQDVSrjArHDsW2ut/l/uPzvWhSIBdb9Xg6e3nUeUEJ6UJc6XLhyaRPua/Sbj/xfcJQZ7z+0NBIKYOMUiggq2Cy3UHSyJBSBgN34vlBIMDX64dedDBU0TC0roJzu2Htqs0lwJH+O473RVpn1lb/4YUc7Mq57+DaBXN4/M1Tqzlfv2AuN77z7FVwp1ZV8s8rlwFgGIq7N7zE4hnTSKQzdARD3PH+63n0jSa6+yLUVVawvb2Lv77+3RcS4qix22z/ZnUMorhJt78oSJGmSA5z7L8GILQ19FYumQuf9QWGoT74TNJ3/49z5ZdHndVjFOY5fd/v572tLWyIhPlCXd3bHl9SXs67XRXc0NrCDa0tXFtRwbzSUq6pqCCQzfFnHe18praWzVGdS8rKmOoY+uKESEWDMRLvJa8CeA/QCWQBR4sv0GGYE9eGbN6UybhKhrfg4ogvwOQKF7UVLjRNI2solFJkcjnsNhuNh9q4bsEc7AU01SOTy+378hPPvWh1HKK4Fc5fhBBvtxs4jtn6z0V2Rjad6UmzWrOh/1yTTtzW7JhaXkAT+v5xyhQ2z5vPzVXVPNoXetvjHek0bekUm+fNp3HefN6Mx9gRj+PQNL7T0MBTc+byfncVvwiF+FRtLd/29fCPHg+bo4Pf9DDpGpFlfgOtaPZ4s5gbMU1KZXMZPZk6NpIneK2lgzUvvMK6bbuJp888V6Lfrs7jLJnVAECZ08G73lHP9158ldoKF2VOB8d6+1g8o/6cxxhrdpvt61bHIETBfFAKcbp86/8J8jP/I9sjB9OB9IkuZ2fSyHz6fxL+b69TNXOyDpdVcZ7PB6uqeFF/e8L+XVTnsrJyKmw2Kmw2llVUsjtxajG+X/aF+HBVNbsTCZyaxpqGBn4UDA763Eb55JGuXNi/5G8n4ALw6yO3xe8182fzr3+8nH9auYyq8lKe3bX/rM/N5gyaj/dw2czpJ+5b/s55/PPKZXxoySVs2neYDyxeyJttnfzv62/xu/1HznqssZLNGa02Tfu11XEIIclfFLrdmK3MaQC9m3ufVzmVXbot5XvgexluOu6cYi/ACn3t6ZNzABqjOheVvH1H3QaHk+2JOFmlyCjF9kSci0pLTjwezuXYEo3y4aoqEoaBhoYGpAw16DjspdUjXdPgyrWrNtcwoNTvsd6+EVvy5y4rxWbTsGkaV180i87evrM+96DXxztqqnGXvf136wmZI0R17gp2tHv4q2suxxvW8eujWjH6vDSNb96xbsPg/wGFGCUy4U8UtEhTRFUtrXoU+A/Ake5K6sseDB35YrTqD6yOrd+XjnvYFo/Tl8uxvLWFv59cxyuxKEfTaWxoNDgdfH2a2fW8L5lgXV8fd9dPZ6XbzRvxOH/Sbi6dX1ZRwfLKkwsaHgoGWDW5Dk3TuK6igsf7Qny4/Si3Tpo0qLh0R2XcYXeOdI+IDXgf8CQQBsraA6HubC6XcNjtw15SGEkkqSo3Oyv2dXmZfo4SBQO7/E+3ad9hPnblpRj5OQAAmqaRyQ230vGFyxrGcYfN9phlAQgxgCR/UfAiTZHjVUurNpQn1aen9TF7TzoSDNW6/DV2xxSrYwNY3TDjbfd99CwJenFZOYvrzRxp1zS+WX/28eg7p56c3V9qs/HTmbOGFFfANT1Kvmt+hK1o9nh/tWhG/U5gmYLuUDzZNsVdsWgoB3nk9020+oPEUmnufvYlVi5aQKu/l+N9ETSgpqKcj11xKQDhRJL12/fw2fys/XQ2x+GeAB/NPz7QPo+XmbXVVOcvImZPnsTqF15herWbhknWFXfU4J471m2w7upDiAG0/qtiIQpZ1dKq8gUe9YvyFMpp0Ly0rHz6Z2pr/9o2AbfgHSk7p9/Y2T3nplmPvbya7lA7oPGJG77ERfUnc/Th47v48Qt3MdltXoQsmXsdN13xV+iJPn7y26+TSEW5+apPc9nc6wD40aZ/4+PXfaHz3x75s9mLZtQvAb4IdF49d+bli2ZMu2XM3+Q4kTOMoN1mm3HHug0pq2MRAqTlL8aJSFMksdztvgv4F8DWlEx070smX39Xefm1VsdWqPSKBuNXr/+QS2ZexWdXfoNsLkM6+/bcM69+MX93072n3LezZTNXL1zJFfOW8+DGO7ls7nXsbX+dmXULqK2cOmvtqs3zMCv9AWht/t7WRTOGXoegWBiGuvMr6yXxi8IhE/7EuNGo6/uBLcAMgP8J9W6J5HIBS4MqYH2OSmdr917e806zNpLD7sRVWjmo19ptDjLZFNlcBk3TyBk5Gvc+xR9d9qf9T1nZ7PFGgGNApT8aCyczmd7ReB/jXSKdeevOJ5//qdVxCDGQJH8x3qwHYkBlQqnsI6HQ+qxS514MXqR82bSrsqyaR7b8J9/61d/y6MurSWUSb3ve0Z793Lf+r3lw451097YDcOX893KgawdrN97JH19xG1ubn+bdC1dQ4jyxcrB/yd8OYBJAMJqQXf5OkzOMbCyV/kur4xDidJL8xbjSqOtR4GFgKmDbl0r6fqvrT1scVkGyOV2VxwJHWHbJh7jzYz+i1FHGi7tO3UF2Zt0C7v7E4/zrx3/CDYs/wo9fuAuA8tJK/u6me/mXjz7EzCkL2Nf5Bksvup7HXl7DT3/7DVq7965Yu2qzHTjYf6zucESS/2kiidQD921sPHuxAiEsIslfjEe7gd8CswA26JHmfcnE760NqbCEnVWxOvd056SKKcyZZq6KXHLR9RwLnFroprykglKnufpg0ayryRlZoolTqyg/v/MXvH/pJ9jRspmZUxbyiRu/zIYdP68ErgI6AAOwt/iC7UqpkSwnPK4l0pnOZDb7FavjEOJMJPmLcadR1xVm5b8jQD3Aj4LBF3uymXYr4yokfteMaJWrlprKKfT0mdV3D3maqJ80+5TnReK9J9bBt/sOolBUlJ1cDucLdxGOBVnQcBnpbDJfaEgjk02DueQvDRwAJsXTmVQ0lfaMzTssbIZSRjSV/uTqTS/L0j5RkGS2vxiXGnU9vdztfgj4JlCdg/BDweD6r0yZ+rcum826xdwFIlTRkAL4+LVf4L9fupeckaGuajqfvPErbN3/LADLLrmFprZX2Lr/GeyaHaejlE+/7/8zcPXks9se5pZ33w6Y8wB+/MJdbNn3FB+88lNgbvF7N+a4/2IgGIjG2txlpTPH8r0Wokgi+fi3NjZutToOIc5G1vmLcW25270A+BrmBkDpK8rLGz5VU3u7XdPs53nphNa44JPtasZ75ozyabJA7YPP/kUV5kVA5/ypk2dev3Du7aN83oKWymSDsXR61j0bNsetjkWIs5FufzGuNer6EeB/gXcA2s5E4viWWPQ5i8OyXMo1bSx69RzAjUA35gqM0qP+Xk/OMIp6PbueSq2SxC8KnSR/MRE0Aq8AMwGeDIebDqeSO60NyVqqvHbYdfYHaUWzx2sATUBNTimjL55sH6NzF5xIIvnifc81/srqOIQ4H0n+YtzLTwB8BLPgzDSAtcHgxq5M+pClgVnEUOAscVeP0elW5r/uBkoAfHq0KJf8JdKZQCqbvdXqOIQYDEn+YkJo1PUksBZQgDujlLHa71/vyWSs38R9jPWV1uo2m32sJvNevHbV5pmYW/xqAO2B0Iht8TteZHK5VFug92Pf2rglZHUsQgyGJH8xYTTqug+4H7PiXGVaqdxqv29ddybTYnFoYyrgaoiO8SlXNHu8fZhj/5XdYT2YymbD53vRRGEopQ50+//94a3bX7Y6FiEGS5K/mFAadf0QsAaoASpT+QsAbyZTNK3RUEVDeoxP+bZSv72x4in12+bv/eWO9q5vWR2HEEMhyV9MOPkNgL4L1AIVCaWyq/2+x4ulCFCsYvpYr99939pVmzXMYj8agDesF8XF1vG+yI5XDh+9PT/pUYhxQ5K/mJAadb0Z+B5QB7jiSmVX+/2P+bLZDotDG3Vp11TnGJ9yCrAUOJr/2dbiC7apCV5EJBRPdL3ZduyDzR5v0upYhBgqSf5iwmrU9b3A9zE3AXLFDCOzxu97LJDNHrM4tNFVVuuy4Kwr8knwMFCtJ1OJWDrjtSCOMZFIZ/Q9x7o/9OqRoz6rYxHiQkjyFxNao67vBh7AvAAo1w0jvdrve2SiDgHklKYcJZVWlDceOO7vBghGYxNy3D+bMzL7PD1/88yu/U1WxyLEhZLkLya8Rl1/C/gh5iZAZRHDSN/T0/OLA8nkdotDG3G9ZXW6zWazorTxdWtXbS7H3GxJAXSFJt4Wv0opddDr+85ej3ed1bEIMRyS/EVRaNT1HZh1AKYBVVkwfhAMbGyM6huMCbQNbbBi+lgv8+tXCiwDPEAacLb6gsdyhpGxKJ4Rp5TicE/gl9uOdt3V7PFO6PkMYuKT5C+KRqOubwO+hZmopgKsD4d3PtoX+p+UYUyIWux9rhlWJtuVzR5vDtgF1GQNIxdJpibEBEullNrn6XnutZaOz+bfoxDjmiR/UVTydQC+CQQx9wLQfh+Pd94fCPy4L5frsTa64YtV1Ft5+v5x/yagDMAfiY37JX9KKbXrWPdvt7d33d7s8U6Ii0QhJPmLopOvBHgfZpKaAzjaM+nwvb6en3Wm0/stDW6YMmO/zG+gS9eu2jwNOJHw24OhcT3ur5RSTZ3HNzV1Hr+t2eOVmf1iwpDkL4pSo67HgQeBp4BZgCtqGJlv+33rd8TjW8btEvWymgoLz64Bf4TZqxIEXF2hsC+dzVk1D2FYDKWMtzo8G3Yd6/5Us8c77nuFhBhIkr8oWo26bjTq+tOY+wHUALUKeDjU+/L6cPiRuGFErI1waLLYDGdJhdviMFbkJ8PtwPydEoonxl3Xf9YwMq+3dPxqd5f3M9LiFxORJH9R9Bp1fSdwN5ABGgC2xKKt9/h6HmpNpXZbGtwQBMunRDTNZvXfdP+4fzNgB+gJ6+Oq6z+dzSa2HGx77HBP4HPNHq/f6niEGA1Wf1AIURAadb0T+HdgP3ARUB7K5ZJrAv7f/CYc/mXSMGLWRnh+QVdDIcTYsHbV5kWcHPfXWvzBcdPyT6Qz+ov7W37U2dv3D80eb/B8z9c0Ladp2i5N0/ZpmrZe0zTXaff33+4c/eiFGDxJ/kLkNep6GLMa4I8xu6zrAX4b1Q/d4+tZ25JKNRXyXIBwRUPW6hjyVuRnxbcBVX3xZDSeThd813konujZtO/wmp5I9GvNHu9gtyROKKWWKKUWY9Y3WHXa/f032fVPFBRJ/kIMkJ8H8CrwNczkNRcoC+Zyie8G/M882hf670guF7A2yjOLuaZbHUK//q7/7UAVQDAaL9iuf6WUOtwT2P100/67QvHEfcNYzrcVmD+SsQkxWiT5C3EGjbruB1YDP8fsBZgBaK/H4x139Xj/a0c8viWnVKG0tAHIuqaUWB1D3g1rV20uwSz1C8DxvkhBdv2nstnolkNtL756pP0+Q6mfNXu86Qs5jqZpDuAmYG/+rvLTuv1vHbGghRgBDqsDEGI0aZqWw/xAdmBuOfuXSqk+TdPm5H/+olLqB/nn/hDYoZT6bzB7AYAty93uPcCfAVcDwbRSkYdDvS836I6d/6960nUXl5Zeadc0K+rpn8raZX4DVQDXAK8CWcDR4gt2XDV3Zs5WCL+nvEA01rH5QGtjNJW+H9h9gSV7yzVN25X/fivws/z3CaXUkhEJVIhRIC1/MdENHJPtBT4/4DEf8A+app2zxdyo673AQ5g9ARowGyg9ns1GfxgMbPq233f/wWRye04py8q+ZnDknE6X1cv8BlrR7PFmMS+8JqWyuYyeTBXEVsqGUrl9np7tz+w68JNoKv3lZo931zBq9Q8c2/+CUuqCeg6EGGuS/EUx+T1m930/P/AScNv5Xtio66pR1/cCXwXWAZPIXwR0ZTL6A8HAxu/4fQ8cSiV3WrFRUMA1NaJpmjbW5z2H/nH/twAXgF+3fovfRDrT9+L+I89vO3rsXuDbzR5vQc7fEGK0Sbe/KAqa2d38Pk52y/b7FvC8pmkPD+Y4jbqeADYtd7u3AjcCtwAlgLczk4ncHwhsmFtS8uqfVFVfP6+k5DKbpo3JBXbQ1RAnX1SnQFyxdtXmWuBEwu8M9rXOnzr5fVYF1N0XaWk81Pa7ZCb7QLPHe2CUTzdwOABgk1JKlvuJgiHJX0x0/R/Cc4CdwIsDH1RKHdU0bRvwF0M5aKOux4DnlrvdL3PyIsAB9BxNp/u+F/A/M6+kZOstVVXXzi0pvdR5nqGF4SqgZX79bMB7gSeBMFDWEQx5s7lcwd9QlwAAB1RJREFUwmG3l49lIOlsLranq7tpT5f3SeDnQ1jGd15Kqcqz3F8wcxuEOBPp9hcTXf/Eq9mYLfTPn+E59wL/wgX8PTTqerRR1zcAXwKeAeowdwt0tqbToe8HAhu+6u1e/YIe+XVPJnN0tOoExCumF1KXf7+V+bH0nZilk1UonhyzWf85w8i0+oLb1u/Y8+s9Xd67gQdGMvELMZ5Jy18UBaVUWNO0LwJPa5r20GmPHdQ0bT9wM7DtQo7fqOs68PRyt7sRc3jhJsAJRGKGEXo6EtnzdCSyZ46zpPq9lZVL/qCsbEmFzTZpeO/qpGz5lJICbGr2j/vvxewFoCcSbZ3irlg0midVStETie57vaVjf18i2Q78V7PHe3Q0zynEeCPJXxQNpVSTpmm7MZftbT3t4Xswt/gdlkZdjwC/Xu52vwBcipkA5wEGEGjPpMMPh3pf1uDla1wVs//Q5Vo6u6TkEoemDWsrXlvZpDN2P1tsztpVm+czoNRvmz/YtnjGtFE7YTiR7HijrXOXJxTxAuuBrRe6dl+IiUySv5jQTh+TVUrdMuDHxQPu380IDoPltwx+E3hzudtdD1yFud3tVCClwP9aPNbxWjzW4bbZNl5fUblgQWnJ3AaHc26l3V47lHOlNGfW4SgrxOQP5pK/hxbNqD8GVAai8XAikwmWO52TR/IkiUwmuLuze8f+bt9x4AVgY7PHq4/kOYSYSCT5CzHKGnXdCzy73O3eCCwElgHvxtz1LqIbRvg5PdKMTjPAbKez+vJy19x5JSVzpzudc8tttnOu3w+46iOapg3pgmG0ZXLpqNNesomTrf7twEcAvTeaaJtRMzLJP5vLJQ55A9t3tHd15JR6E3iy2eP1jsSxhZjIJPkLMUYadT0HHAAOLHe7HwMuw7wQWDDgaeGOTCbSkQnvAnYBLCwpnby0vHzu3JKSuVMdjlllNtsprfxec5mfZck/Z+SyiXQsEEuG/YFId1eH72BnZ+CwHfj83vbX+7vcD/U/vzscaZ1RU3XVcM6ZzGRDncHQ3p0dHk8ikz0MPAYcGUaxHiGKiiR/ISzQqOtR4DXgteVudxkwC7NX4HLMZYlgzhPoO5xOBQ+nU0FgB8Bku718QWnplPKSqmtnlJZqYUdl2EjHXKXO8mqbZhu1eX85I5uOp6KBWDLsD8d7/b16j7+nr9PfE+7qU8oowyx85MRcVdGF2bPRryP/fuwtvmD7FbNnGNoQayAopVRvLHH4cE9g94FuXxzoAx4HdjZ7vJZVVxRiPNIKeYtSIYrRcrfbhXkB8E7Mi4EGQGGWFk4CcSCuuxo+kC6p2qPszi4ATbNpk9317jp3/aQqV22101Fa4rA7nQ6b0+mwO0scdqfTbnOU2G0Op8PmcNptjhJNs9lyRjaTM3KZnJFJZ41sJptNp1LZRDyZjscT6Vg8ntIToagv7I8cj2Am9grMqn32AXGFMCdM7gfagdDe9tdP+XBZNKP+nzF3SQx+7MpLb68qK505mN9HOpvTPX3ht3Z1dreF4gkNiALPAVuaPd7kBf+ihShi0vIXosDkJwvuz9+eWu52V2HWDpgKXIRZs6BB2WxK2ewlmL0GKGUkA5HjyUDkeBBz3wIDyOVvg2HDbLn33xxAWf6xKsyWvQ50Ym6K1AUEMMskR09P9mewA3OSZTCgx1rPlfyVUoQTybYWX3DXPk9Pn6GUDXPo4AWgudnjzQzyPQkhzkCSvxAFLr98sDl/awRY7nbbbcqoQ7P1J+U6zB6CaUA5ZtJ2AaWYrfX+xKwGfA9mq13DTPwZzFZ1JH/rw0zsx/Nf/XvbX7/Qve4BWvq/6QqF2y6aUnvj6U9IZ7MRbyS6b/ex7iN+PaYwezpeAl5r9ni7h3FuIcQAkvyFGIfykwd78rdzunTONRqntuhLMLvsM0A6/zUD5AbReh8OL+bFRelRf6/nmvmzU3ZNK4mm0l2+SPRIW6C39VhvmHyM7cBGYK907Qsx8mTMXwgxZhbNqP80cDXgXTitbnZ3X6RPT6VdmAk/C7yMWYDpmMzcF2L0SMtfCDGW9mCWP555uCdgYPZCvIG59e+RZo93OMMKQohBkuQvhDgjTdNymHX5HZj1CW5TSsUH3K9hTib8e6XU64M87GHg95jFfw5gtvBlmZ4QY0y6/YUQZ6RpWrS/PLKmaY8CO5VS3z3t/vcDX1VK3WBlrEKIoZEtfYUQg7EVmH+G+6sw1/gLIcYR6fYXQpyTpmkOzC2KN+XvKtc0bRfmcsLp5LfrFUKMH9LtL4Q4owFj+2C2/O9QSqVP6/Z/D/BTYLGSDxMhxg1J/kKIMxqY5M91v6ZpPcClSinfmAYohLhgMuYvhLhgmqa9E7NgUNDqWIQQgydj/kKIoeof8wdzud9tSilZrifEOCLd/kIIIUSRkW5/IYQQoshI8hdCCCGKjCR/IYQQoshI8hdCCCGKjCR/IYQQoshI8hdCCCGKjCR/IYQQoshI8hdCCCGKjCR/IYQQoshI8hdCCCGKzP8BZhQhPccVGMwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(relac_nordeste, explode=(0, 0, 0, 0, 0, 0.1, 0, 0, 0), labels=nordeste_pie, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=100)\n",
    "ax1.axis('equal')\n",
    "plt.title('Relação de ocorrências criminais por população de cada estado - Região Nordeste', loc='center')\n",
    "plt.show()"
   ]
  },
  {
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   "source": [
    "#### authors: Fabrício da Cunha Vasconcellos, Jonatas dos Reis, Samuel Xavier     \n",
    "#### database: http://dados.gov.br/dataset/sistema-nacional-de-estatisticas-de-seguranca-publica\n",
    "#### format: *.xlsx*\n",
    "#### libs: pandas, matplotlib"
   ]
  }
 ],
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