Fator_Contribuinte.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carregando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>codigo_ocorrencia3</th>\n",
       "      <th>fator_nome</th>\n",
       "      <th>fator_aspecto</th>\n",
       "      <th>fator_condicionante</th>\n",
       "      <th>fator_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39115</td>\n",
       "      <td>APLICAÇÃO DE COMANDOS</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39115</td>\n",
       "      <td>JULGAMENTO DE PILOTAGEM</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39115</td>\n",
       "      <td>PERCEPÇÃO</td>\n",
       "      <td>ASPECTO PSICOLÓGICO</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>FATOR HUMANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>PLANEJAMENTO DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39115</td>\n",
       "      <td>POUCA EXPERIÊNCIA DO PILOTO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>79397</td>\n",
       "      <td>JULGAMENTO DE PILOTAGEM</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4157</th>\n",
       "      <td>79397</td>\n",
       "      <td>PERCEPÇÃO</td>\n",
       "      <td>ASPECTO PSICOLÓGICO</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>FATOR HUMANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4158</th>\n",
       "      <td>79397</td>\n",
       "      <td>PLANEJAMENTO DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4159</th>\n",
       "      <td>79665</td>\n",
       "      <td>MANUTENÇÃO DA AERONAVE</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>MANUTENÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4160</th>\n",
       "      <td>79665</td>\n",
       "      <td>SUPERVISÃO GERENCIAL</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4161 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codigo_ocorrencia3                   fator_nome  \\\n",
       "0                  39115        APLICAÇÃO DE COMANDOS   \n",
       "1                  39115      JULGAMENTO DE PILOTAGEM   \n",
       "2                  39115                    PERCEPÇÃO   \n",
       "3                  39115          PLANEJAMENTO DE VOO   \n",
       "4                  39115  POUCA EXPERIÊNCIA DO PILOTO   \n",
       "...                  ...                          ...   \n",
       "4156               79397      JULGAMENTO DE PILOTAGEM   \n",
       "4157               79397                    PERCEPÇÃO   \n",
       "4158               79397          PLANEJAMENTO DE VOO   \n",
       "4159               79665       MANUTENÇÃO DA AERONAVE   \n",
       "4160               79665         SUPERVISÃO GERENCIAL   \n",
       "\n",
       "                 fator_aspecto     fator_condicionante         fator_area  \n",
       "0     DESEMPENHO DO SER HUMANO    OPERAÇÃO DA AERONAVE  FATOR OPERACIONAL  \n",
       "1     DESEMPENHO DO SER HUMANO    OPERAÇÃO DA AERONAVE  FATOR OPERACIONAL  \n",
       "2          ASPECTO PSICOLÓGICO              INDIVIDUAL       FATOR HUMANO  \n",
       "3     DESEMPENHO DO SER HUMANO    OPERAÇÃO DA AERONAVE  FATOR OPERACIONAL  \n",
       "4     DESEMPENHO DO SER HUMANO    OPERAÇÃO DA AERONAVE  FATOR OPERACIONAL  \n",
       "...                        ...                     ...                ...  \n",
       "4156  DESEMPENHO DO SER HUMANO    OPERAÇÃO DA AERONAVE  FATOR OPERACIONAL  \n",
       "4157       ASPECTO PSICOLÓGICO              INDIVIDUAL       FATOR HUMANO  \n",
       "4158  DESEMPENHO DO SER HUMANO    OPERAÇÃO DA AERONAVE  FATOR OPERACIONAL  \n",
       "4159  DESEMPENHO DO SER HUMANO  MANUTENÇÃO DA AERONAVE  FATOR OPERACIONAL  \n",
       "4160  DESEMPENHO DO SER HUMANO    OPERAÇÃO DA AERONAVE  FATOR OPERACIONAL  \n",
       "\n",
       "[4161 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fator_contribuinte=pd.read_csv('Dados/fator_contribuinte_2010_2020.csv', sep = ';', low_memory=False)\n",
    "fator_contribuinte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verificando os tipos das colunas e se existem dados faltantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4161 entries, 0 to 4160\n",
      "Data columns (total 5 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   codigo_ocorrencia3   4161 non-null   int64 \n",
      " 1   fator_nome           4161 non-null   object\n",
      " 2   fator_aspecto        4161 non-null   object\n",
      " 3   fator_condicionante  4161 non-null   object\n",
      " 4   fator_area           4161 non-null   object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 162.7+ KB\n"
     ]
    }
   ],
   "source": [
    "fator_contribuinte.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verificando se existem dados duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated values:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Duplicated values: \", fator_contribuinte.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carregando os dados das ocorrências que foram filtradas no notebook Ocorrencias.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>codigo_ocorrencia1</th>\n",
       "      <th>ocorrencia_classificacao</th>\n",
       "      <th>ocorrencia_cidade</th>\n",
       "      <th>ocorrencia_uf</th>\n",
       "      <th>ocorrencia_aerodromo</th>\n",
       "      <th>ocorrencia_dia</th>\n",
       "      <th>Dia</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Ano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40211</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>RJ</td>\n",
       "      <td>****</td>\n",
       "      <td>03/01/2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40349</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>BELÉM</td>\n",
       "      <td>PA</td>\n",
       "      <td>SBBE</td>\n",
       "      <td>03/01/2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40351</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>RJ</td>\n",
       "      <td>SBRJ</td>\n",
       "      <td>03/01/2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40324</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>PELOTAS</td>\n",
       "      <td>RS</td>\n",
       "      <td>SBPK</td>\n",
       "      <td>05/01/2010</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5747</th>\n",
       "      <td>79804</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>CAMPINAS</td>\n",
       "      <td>SP</td>\n",
       "      <td>SBKP</td>\n",
       "      <td>29/12/2020</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5748</th>\n",
       "      <td>79757</td>\n",
       "      <td>INCIDENTE GRAVE</td>\n",
       "      <td>LAGOA DA CONFUSÃO</td>\n",
       "      <td>TO</td>\n",
       "      <td>****</td>\n",
       "      <td>30/12/2020</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5749</th>\n",
       "      <td>79802</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>RJ</td>\n",
       "      <td>SBGL</td>\n",
       "      <td>30/12/2020</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5750</th>\n",
       "      <td>79756</td>\n",
       "      <td>INCIDENTE GRAVE</td>\n",
       "      <td>VICENTINA</td>\n",
       "      <td>MS</td>\n",
       "      <td>****</td>\n",
       "      <td>31/12/2020</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5751</th>\n",
       "      <td>79844</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>RJ</td>\n",
       "      <td>SBJR</td>\n",
       "      <td>31/12/2020</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5752 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codigo_ocorrencia1 ocorrencia_classificacao   ocorrencia_cidade  \\\n",
       "0                  40211                INCIDENTE      RIO DE JANEIRO   \n",
       "1                  40349                INCIDENTE               BELÉM   \n",
       "2                  40351                INCIDENTE      RIO DE JANEIRO   \n",
       "3                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "4                  40324                INCIDENTE             PELOTAS   \n",
       "...                  ...                      ...                 ...   \n",
       "5747               79804                INCIDENTE            CAMPINAS   \n",
       "5748               79757          INCIDENTE GRAVE   LAGOA DA CONFUSÃO   \n",
       "5749               79802                INCIDENTE      RIO DE JANEIRO   \n",
       "5750               79756          INCIDENTE GRAVE           VICENTINA   \n",
       "5751               79844                INCIDENTE      RIO DE JANEIRO   \n",
       "\n",
       "     ocorrencia_uf ocorrencia_aerodromo ocorrencia_dia  Dia  Mes   Ano  \n",
       "0               RJ                 ****     03/01/2010    3    1  2010  \n",
       "1               PA                 SBBE     03/01/2010    3    1  2010  \n",
       "2               RJ                 SBRJ     03/01/2010    3    1  2010  \n",
       "3               MT                 ****     04/01/2010    4    1  2010  \n",
       "4               RS                 SBPK     05/01/2010    5    1  2010  \n",
       "...            ...                  ...            ...  ...  ...   ...  \n",
       "5747            SP                 SBKP     29/12/2020   29   12  2020  \n",
       "5748            TO                 ****     30/12/2020   30   12  2020  \n",
       "5749            RJ                 SBGL     30/12/2020   30   12  2020  \n",
       "5750            MS                 ****     31/12/2020   31   12  2020  \n",
       "5751            RJ                 SBJR     31/12/2020   31   12  2020  \n",
       "\n",
       "[5752 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocorrencias=pd.read_csv('Dados/Ocorrencias_Filtradas.csv', sep = ',', low_memory=False)\n",
    "ocorrencias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renomeando a primeira coluna para dar o merge com o outro arquivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>codigo_ocorrencia3</th>\n",
       "      <th>ocorrencia_classificacao</th>\n",
       "      <th>ocorrencia_cidade</th>\n",
       "      <th>ocorrencia_uf</th>\n",
       "      <th>ocorrencia_aerodromo</th>\n",
       "      <th>ocorrencia_dia</th>\n",
       "      <th>Dia</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Ano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40211</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>RJ</td>\n",
       "      <td>****</td>\n",
       "      <td>03/01/2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40349</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>BELÉM</td>\n",
       "      <td>PA</td>\n",
       "      <td>SBBE</td>\n",
       "      <td>03/01/2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40351</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>RJ</td>\n",
       "      <td>SBRJ</td>\n",
       "      <td>03/01/2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40324</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>PELOTAS</td>\n",
       "      <td>RS</td>\n",
       "      <td>SBPK</td>\n",
       "      <td>05/01/2010</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5747</th>\n",
       "      <td>79804</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>CAMPINAS</td>\n",
       "      <td>SP</td>\n",
       "      <td>SBKP</td>\n",
       "      <td>29/12/2020</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5748</th>\n",
       "      <td>79757</td>\n",
       "      <td>INCIDENTE GRAVE</td>\n",
       "      <td>LAGOA DA CONFUSÃO</td>\n",
       "      <td>TO</td>\n",
       "      <td>****</td>\n",
       "      <td>30/12/2020</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5749</th>\n",
       "      <td>79802</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>RJ</td>\n",
       "      <td>SBGL</td>\n",
       "      <td>30/12/2020</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5750</th>\n",
       "      <td>79756</td>\n",
       "      <td>INCIDENTE GRAVE</td>\n",
       "      <td>VICENTINA</td>\n",
       "      <td>MS</td>\n",
       "      <td>****</td>\n",
       "      <td>31/12/2020</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5751</th>\n",
       "      <td>79844</td>\n",
       "      <td>INCIDENTE</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>RJ</td>\n",
       "      <td>SBJR</td>\n",
       "      <td>31/12/2020</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5752 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codigo_ocorrencia3 ocorrencia_classificacao   ocorrencia_cidade  \\\n",
       "0                  40211                INCIDENTE      RIO DE JANEIRO   \n",
       "1                  40349                INCIDENTE               BELÉM   \n",
       "2                  40351                INCIDENTE      RIO DE JANEIRO   \n",
       "3                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "4                  40324                INCIDENTE             PELOTAS   \n",
       "...                  ...                      ...                 ...   \n",
       "5747               79804                INCIDENTE            CAMPINAS   \n",
       "5748               79757          INCIDENTE GRAVE   LAGOA DA CONFUSÃO   \n",
       "5749               79802                INCIDENTE      RIO DE JANEIRO   \n",
       "5750               79756          INCIDENTE GRAVE           VICENTINA   \n",
       "5751               79844                INCIDENTE      RIO DE JANEIRO   \n",
       "\n",
       "     ocorrencia_uf ocorrencia_aerodromo ocorrencia_dia  Dia  Mes   Ano  \n",
       "0               RJ                 ****     03/01/2010    3    1  2010  \n",
       "1               PA                 SBBE     03/01/2010    3    1  2010  \n",
       "2               RJ                 SBRJ     03/01/2010    3    1  2010  \n",
       "3               MT                 ****     04/01/2010    4    1  2010  \n",
       "4               RS                 SBPK     05/01/2010    5    1  2010  \n",
       "...            ...                  ...            ...  ...  ...   ...  \n",
       "5747            SP                 SBKP     29/12/2020   29   12  2020  \n",
       "5748            TO                 ****     30/12/2020   30   12  2020  \n",
       "5749            RJ                 SBGL     30/12/2020   30   12  2020  \n",
       "5750            MS                 ****     31/12/2020   31   12  2020  \n",
       "5751            RJ                 SBJR     31/12/2020   31   12  2020  \n",
       "\n",
       "[5752 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocorrencias_new = ocorrencias.rename(columns={'codigo_ocorrencia1': 'codigo_ocorrencia3'})\n",
    "ocorrencias_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Juntando os 2 dados, de ocorrências e dos fatores contribuintes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>codigo_ocorrencia3</th>\n",
       "      <th>ocorrencia_classificacao</th>\n",
       "      <th>ocorrencia_cidade</th>\n",
       "      <th>ocorrencia_uf</th>\n",
       "      <th>ocorrencia_aerodromo</th>\n",
       "      <th>ocorrencia_dia</th>\n",
       "      <th>Dia</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Ano</th>\n",
       "      <th>fator_nome</th>\n",
       "      <th>fator_aspecto</th>\n",
       "      <th>fator_condicionante</th>\n",
       "      <th>fator_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>APLICAÇÃO DE COMANDOS</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>JULGAMENTO DE PILOTAGEM</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>PLANEJAMENTO DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>PROCESSO DECISÓRIO</td>\n",
       "      <td>ASPECTO PSICOLÓGICO</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>FATOR HUMANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39707</td>\n",
       "      <td>INCIDENTE GRAVE</td>\n",
       "      <td>CANUTAMA</td>\n",
       "      <td>AM</td>\n",
       "      <td>****</td>\n",
       "      <td>09/01/2010</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>INDISCIPLINA DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>79195</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>PORTO VELHO</td>\n",
       "      <td>RO</td>\n",
       "      <td>****</td>\n",
       "      <td>20/02/2020</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>OUTRO FATOR</td>\n",
       "      <td>OUTRO</td>\n",
       "      <td>***</td>\n",
       "      <td>OUTRO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4157</th>\n",
       "      <td>79397</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>CORRENTINA</td>\n",
       "      <td>BA</td>\n",
       "      <td>****</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>ATENÇÃO</td>\n",
       "      <td>ASPECTO PSICOLÓGICO</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>FATOR HUMANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4158</th>\n",
       "      <td>79397</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>CORRENTINA</td>\n",
       "      <td>BA</td>\n",
       "      <td>****</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>JULGAMENTO DE PILOTAGEM</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4159</th>\n",
       "      <td>79397</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>CORRENTINA</td>\n",
       "      <td>BA</td>\n",
       "      <td>****</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>PERCEPÇÃO</td>\n",
       "      <td>ASPECTO PSICOLÓGICO</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>FATOR HUMANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4160</th>\n",
       "      <td>79397</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>CORRENTINA</td>\n",
       "      <td>BA</td>\n",
       "      <td>****</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>PLANEJAMENTO DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4161 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codigo_ocorrencia3 ocorrencia_classificacao   ocorrencia_cidade  \\\n",
       "0                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "1                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "2                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "3                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "4                  39707          INCIDENTE GRAVE            CANUTAMA   \n",
       "...                  ...                      ...                 ...   \n",
       "4156               79195                 ACIDENTE         PORTO VELHO   \n",
       "4157               79397                 ACIDENTE          CORRENTINA   \n",
       "4158               79397                 ACIDENTE          CORRENTINA   \n",
       "4159               79397                 ACIDENTE          CORRENTINA   \n",
       "4160               79397                 ACIDENTE          CORRENTINA   \n",
       "\n",
       "     ocorrencia_uf ocorrencia_aerodromo ocorrencia_dia  Dia  Mes   Ano  \\\n",
       "0               MT                 ****     04/01/2010    4    1  2010   \n",
       "1               MT                 ****     04/01/2010    4    1  2010   \n",
       "2               MT                 ****     04/01/2010    4    1  2010   \n",
       "3               MT                 ****     04/01/2010    4    1  2010   \n",
       "4               AM                 ****     09/01/2010    9    1  2010   \n",
       "...            ...                  ...            ...  ...  ...   ...   \n",
       "4156            RO                 ****     20/02/2020   20    2  2020   \n",
       "4157            BA                 ****     12/06/2020   12    6  2020   \n",
       "4158            BA                 ****     12/06/2020   12    6  2020   \n",
       "4159            BA                 ****     12/06/2020   12    6  2020   \n",
       "4160            BA                 ****     12/06/2020   12    6  2020   \n",
       "\n",
       "                   fator_nome             fator_aspecto   fator_condicionante  \\\n",
       "0       APLICAÇÃO DE COMANDOS  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "1     JULGAMENTO DE PILOTAGEM  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "2         PLANEJAMENTO DE VOO  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "3          PROCESSO DECISÓRIO       ASPECTO PSICOLÓGICO            INDIVIDUAL   \n",
       "4         INDISCIPLINA DE VOO  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "...                       ...                       ...                   ...   \n",
       "4156              OUTRO FATOR                     OUTRO                   ***   \n",
       "4157                  ATENÇÃO       ASPECTO PSICOLÓGICO            INDIVIDUAL   \n",
       "4158  JULGAMENTO DE PILOTAGEM  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "4159                PERCEPÇÃO       ASPECTO PSICOLÓGICO            INDIVIDUAL   \n",
       "4160      PLANEJAMENTO DE VOO  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "\n",
       "             fator_area  \n",
       "0     FATOR OPERACIONAL  \n",
       "1     FATOR OPERACIONAL  \n",
       "2     FATOR OPERACIONAL  \n",
       "3          FATOR HUMANO  \n",
       "4     FATOR OPERACIONAL  \n",
       "...                 ...  \n",
       "4156              OUTRO  \n",
       "4157       FATOR HUMANO  \n",
       "4158  FATOR OPERACIONAL  \n",
       "4159       FATOR HUMANO  \n",
       "4160  FATOR OPERACIONAL  \n",
       "\n",
       "[4161 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_fator_contribuinte = pd.merge(ocorrencias_new, fator_contribuinte, on=['codigo_ocorrencia3'])\n",
    "merge_fator_contribuinte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checando as áreas de investigação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FATOR OPERACIONAL    2720\n",
       "FATOR HUMANO         1325\n",
       "OUTRO                  62\n",
       "***                    28\n",
       "FATOR MATERIAL         26\n",
       "Name: fator_area, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_fator_contribuinte.fator_area.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>ocorrencia_classificacao</th>\n",
       "      <th>ACIDENTE</th>\n",
       "      <th>INCIDENTE</th>\n",
       "      <th>INCIDENTE GRAVE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fator_area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>***</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FATOR HUMANO</th>\n",
       "      <td>1139.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FATOR MATERIAL</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FATOR OPERACIONAL</th>\n",
       "      <td>2117.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUTRO</th>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ocorrencia_classificacao  ACIDENTE  INCIDENTE  INCIDENTE GRAVE\n",
       "fator_area                                                    \n",
       "***                           21.0        1.0              6.0\n",
       "FATOR HUMANO                1139.0       10.0            176.0\n",
       "FATOR MATERIAL                15.0        3.0              8.0\n",
       "FATOR OPERACIONAL           2117.0       31.0            572.0\n",
       "OUTRO                         47.0        NaN             15.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fator_contribuinte_plot = merge_fator_contribuinte.groupby(['fator_area', 'ocorrencia_classificacao']).size().reset_index().pivot(columns='ocorrencia_classificacao', index='fator_area', values=0)\n",
    "fator_contribuinte_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Substituindo NaN por 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>ocorrencia_classificacao</th>\n",
       "      <th>ACIDENTE</th>\n",
       "      <th>INCIDENTE</th>\n",
       "      <th>INCIDENTE GRAVE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fator_area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>***</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FATOR HUMANO</th>\n",
       "      <td>1139.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FATOR MATERIAL</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FATOR OPERACIONAL</th>\n",
       "      <td>2117.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUTRO</th>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ocorrencia_classificacao  ACIDENTE  INCIDENTE  INCIDENTE GRAVE\n",
       "fator_area                                                    \n",
       "***                           21.0        1.0              6.0\n",
       "FATOR HUMANO                1139.0       10.0            176.0\n",
       "FATOR MATERIAL                15.0        3.0              8.0\n",
       "FATOR OPERACIONAL           2117.0       31.0            572.0\n",
       "OUTRO                         47.0        0.0             15.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fator_contribuinte_plot = fator_contribuinte_plot.fillna(0)\n",
    "fator_contribuinte_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Panorama das ocorrências por Área de Investigação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "\n",
    "rc={'font.size': 12, 'axes.labelsize': 24, 'legend.fontsize': 40, \n",
    "    'axes.titlesize': 24, 'xtick.labelsize': 12, 'ytick.labelsize': 12}\n",
    "\n",
    "sns.set(rc=rc)\n",
    "\n",
    "ax = fator_contribuinte_plot.plot(kind='bar', stacked=True, figsize=(15, 8))\n",
    "\n",
    "plt.legend(fontsize='x-large')\n",
    "plt.xlabel(\"Área de Investigação\")\n",
    "plt.ylabel(\"Contagem Classificação\")\n",
    "plt.title(\"Classificação das ocorrências por Área de Investigação\")\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "bottom = np.zeros(len(fator_contribuinte_plot))\n",
    "\n",
    "for i, col in enumerate(fator_contribuinte_plot.columns):\n",
    "    ax.bar(fator_contribuinte_plot.index, fator_contribuinte_plot[col], bottom=bottom, label=col)\n",
    "    bottom += np.array(fator_contribuinte_plot[col])\n",
    "\n",
    "# Somando as linhas dos dados para obter o valor total de cada barra.\n",
    "totals = fator_contribuinte_plot.sum(axis=1)\n",
    "\n",
    "# Add labels to each bar.\n",
    "for i, total in enumerate(totals):\n",
    "    ax.text(totals.index[i], total + 1, round(total), ha='center',\n",
    "          weight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Como *, fator material e outro tem baixa quanidade de ocorrências em relação à fator humano e fator operacional, juntei as 3 áreas minoritária de investigação em outro, de modo a investigar as 2 áreas de investigação majoritárias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FATOR OPERACIONAL', 'FATOR HUMANO', 'OUTRO', '***', 'FATOR MATERIAL']"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = merge_fator_contribuinte.fator_area.value_counts().index.tolist()\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2720, 1325,   62,   28,   26])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = merge_fator_contribuinte.fator_area.value_counts().values\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FATOR OPERACIONAL', 'FATOR HUMANO', 'OUTRO']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_labels = [labels[0], labels[1], 'OUTRO']\n",
    "new_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2720, 1325, 116]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_values = [values[0], values[1], sum(values[-3:])]\n",
    "new_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Panorama das ocorrências por Área de Investigação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1L3YeynV2KEILtXzNEfafLBDdGi5IjMpogfQGMz/szmDF2qPN+r6DYkK4ZUwUYSGemM1WMvIreGHlHgbFBPPIzD719l/4xnbOZJdd9pwhfm68+dhINCo5Z3PKWfD6NgDiogO4/8ZeeHuo2HM0n7dWJ2O21P6p/ueBeM7mlDf79bdGcpmU1x4aRscQDzF924WIR7MtjMFk4VhaMSvXNW9SGh7Xnidm98NosrD7aB56o4WoDt6o/jTdN/lUIZkFlfbfy6ouX5tDKpXw6N/6oJDX/eImkcBjs/pSozdz4GQho/p14GRmKRt/S2NEXHvaB7rz74/2NO0FtlFmi5XnPkhi2eOj8PFQI5WK1bhdgUjMLYjZYqWopIaX/reP5v6eM2dSdwCe/3AXR1OLL7rP9uRsftqXddXnvGVsF8JDPEnYnsrNo6Ps2z3dlHi5q/j25xQStqfSr1sQHYM80KjkzJ0Sw8frjqETfaNNprLGxD/e/Y03Fo5Aq1Y4OxwBkZhblBq9mWeXJ2Fo5gL37fzdCPDRYjCamT4qiufmDaK0Uk/ijrNs/O2Pad/3TOvJA9N7U1Raw8akdNbtPHvJc0Z18ObWsV34MOEIBlPd66moNlJeZWDK0M706OyHRiUno6CS28ZHk3euim0Hsx12rW1V7rlq/r1yD/+6Z5Aotu8CxMO/FsJgtLBohXNKd3q6KwFQKeUE+2n59fcc/Lw0PHBTLwbFBGO1wunMUn79PYcDJwoI8XPj3ht6ct2gjhc9n0oh49FZfTl0uoiNSen1XrfZYOmqA5gtVnpF+vPLgSxOpZdwfXw4HyYe5a4pPVj+9BhefnAoXcJ8HHnpbcrRs8V88cNJMVLDBYiHfy2A3mDmyy2n+H7bGae8f4i/Gx88MxaAR/+7nZSsMu6/qReT4sPZujeDt74+VGf/O67vxowxXTh4qpDnP9hV73wxnf146cGhnEwvobLGiJ+Xhs7tvajWmTieVswLK+v3H7/4wBDScivIKqjkvht78tTbvzIpPpzeUQHM/fcWh1x3W/XCfYPp0dkPpXgY6DSixezijCYLpzJLnZaUAYpKa6jWmS76mt5gIcTP7aKv2c6vvC2TSggNdCc00B2JBDj/fKlrJ1/6dw+mc3svANw0Cvp3D653nuFx7ekQ6MGqH07SuZ0X1TozKVllnM4qw99bg6eb8tovUrB79dP91OhFq9mZRGeSi9MZzbzy6T6nxmC22Fi7M5Xbxndl4W19OJlRwvC4UCwWK9sOZvPQLbG4axWkZJXhrlEwsEdtcr3QF+znpea9p8YAMPPZDRxNLWbKY4n284/p34FHZvapM1zuArVSxl1TevDx+mPU6M1kF1bh7aHiqTv60aWDDxXVBiprjM1zI9qIKp2JxSv38OID8aiUotXsDKLF7MIM56vFVdZcvLXanL7aeppvfjqNm0bBsN7tycyr4N8f7eF0ZinbDmZhMlsZ0qsdsV0CSc+r4I0vDzbJQ7rbxkeTX1zDLwdqz7V5dzrbDmbRJzoQs9XKki8ONPsIlbbgVGYpX245KUa/OInoY3ZReqOZdTvP8qlYgURwEokEXn9kBOHtPJFJRRuuOYm77YJsNhulFQZW/XDS2aEIbZjNBq99th+zWawX2dxEYnZBRpOFVz7bZ5+CLAjOknuumq+2nhZD6JqZSMwuRm80s3l3BqnZ5c4ORRAA+H7bGYrKdFitoqHQXERidjE6g5nPNol+ZcF1WK02Xv1sPybRpdFsRGJ2IXqDmbe+PtTsU64v52/XdWXd0mn0jgpwdih1vDJ/KF/+eyJuGlHboTmk51WwaVeaS/1ttmZiHLOLsFqtnM0tZ/+JAmeHYueuUTBtRARpueX8nlK7wGtclwBuGhVFx2AP3DQKzpXp2Lo3k29/TgH+GJN8Mc+8+2vtGOZhnRnZJ5QQfzfkMinZhVV8tfUUe/9U8L9TiCf33BBDdEdfDEYLu47ksnLtH8WLEren8sycAdw0MlJ8w2gmq344xdj+YWJsczMQLWYXYTLbePfb350dRh2j+3VAo5KzIznHvq1rJ1/C23ly7Gwx+08UEOznxp2TujN5aDgAmfmVJO5Itf/8eqj2WIvVRkFJDQCDY0Jw0yjYeyyfjLwKojp488yd/e0zADUqOf++bwi9IgPYf7yAwpIarhvUiQdn9LbHsfd4ATV6E+MGhIlSlc1EZzCzYu1RMba5GYgWswswmS0kHcklI7/yyjs3owvTo4+e/WOR199+z+Xbn1Ps/Y2PzIxjTP8wYrsEsP7XNFKyykjJKrPvf9eUHgDsOZpHUakOgJVrj5KaU/twUyqB5U+PJcTfjZ4R/pzNKWfcgDC8PVTsPZbPy5/uQ62U8fkLExnauz2fbTxBQUkNZouVU5mlxHUJJCrUm1OZpc1xS9q8n/dnccuYLmgC3J0dSqsmWswuwGK18dG6Y84Oo55OIZ4AZBVU2bdlFlTWeQgkl9X+CRWX1a96p1bKGDewtsJc4o5U+/YLSdl+jvOF8ovLaxP3hZZzyvnVT/RGC9mFlcikEsLbedqPyz4f14X9Bcez2eDd7w6L4XMOJhKzk+mNZtZsO0NZpcHZodTjrq19sHapr65DeoUwLLY9FdUGex/zn40dEIa7RsGZrDKOp5Vc9BzzpsYQ4K3heFoxSUfyAPD2UAHU+Y//wkMnbw+1fVuN3lQnTqF5/J5SxNnccsSkYccRidnJbDZYsy31yjs6wYWKchpV/R6vcQPCeHJ2PyprjPxzeRJFZbp6+0wZ2hmAtTvrX59UAg/dEsvU4RGkZJbywso99nGyFz6k1H96X/X54u1llX+0zC+stnGpyneC43yy/rgYoeFAIjE7kcFoZu2OVJd9mJKeVwFAh6C6/Yk3j47i4VvjOFem48llO0nLrah3bP/uQbQLcKe4XM/OQzl1XlPIpTwzZwDjB3Yk+VQh/3jvtzrJNS23tqujSwdvoPaDITTQHavVZo8JIPR8XH/tGhEc70R6CVmFVVfeUWgUkZid7M99r65m3/Ha4Wsxnf3t28YP7Mid59f/O51VxvXx4dw9LYaZ47rUOXba8AgANial1Zta/vCtsQyKCcFgspB7rprZE7tx97QYRsS1B2DLnkwqqg307x7M03f056W/x6NUyPjtcC75xbUjO+QyKdFhPpRW6us8bBSaz/82HHfZRkVLJxKzkxjNFn7YneESJT0v5ef9WegMZoafT5hQW1v5gmGx7Zk2PIJpwyMYO+CPZaTCgj3oHRWAwWRh8670euf189QAtUtMTYoPt58jLjoQqO3T/r/3d3HkzDn6dQ8i0FfLlj0ZLFt9yH6OAd2D0KoVbN2TKaYKO8nvKUUUldY4O4xWSZT9dBKDycI9L26l1AUf+v3Z367ryszx0fxzeZJ9kokreGX+UMKCPLj7Pz+KPmYn6t89iCdm97vocwih8URidgKLxcr25Gze+DLZ2aEIwjX76P/GE+CtcXYYrYroynACk8VKwnbX7VsWhIZI2HZGjGtuYiIxO0Fhqe6iIxkEoSX6aX+WmBbfxERibmY6Q+2EEkFoLap1JvYcy8dqFWVBm4pIzM1MIqHeuF5BaOkSt6diMInE3FREYm5GFouVHck5YsaU0OqcyiylvMq1Rxi1JCIxNyOj2crGpDRnhyEIDrHhtzQMRvEQsCmIxNyM9EazWMtPaLV++z0XiUQ8BGwKIjE3E7PFyvaD2c4OQxAcpqhMZ58yL1wbkZibiclsrbMSiCC0Rj/uyxDdGU1AJOZmYjRZRLEdodX7VXRnNAmRmJvBhdEYgtDaFZXq7Gs7Co0nEnMz0Bst/HY419lhCEKz2Hkop87yY0LDicTcDBRyKacyLr60kiC0NsmnCzGZxVj9ayESczNIzSmvVyxeEFqrlMwy+yK9QuOIu+dgRpOFPUfznB2GIDQbi9XG6cxSZ4fRoonE7GBmi5VDLlRgXhCaw+6jeRhMojujsURidjCpVEKaWCxUaGMOnS4SS35dA5GYHSw1qxzx9ym0NZkFlchEjeZGE4nZgSxWG0fPnnN2GILQ7Gw2yC6scnYYLZZIzA6kN5g5k13m7DAEwSmOpxUjlhRtHJGYHUgmk5Aq+peFNupURik6sRZgo4jE7GBFpTpnhyAITiEaJY0nErMDZRWIPjah7coprEQhFymmMcRdc6DTYhq20IZZbbU1moWGE4nZQQxGC1niqbTQxhWIwvmNIhKzg5gtVgpF+UOhjcssqHR2CC2SSMwOIpFAQalIzELblltUJVY0aQSRmB1EqZCJFrPQ5hWU1IjKio0gErODmMxW9EZRxEVo2wpKahArTTWcSMwOUlqpd3YIguB0RWU6VAqZs8NocURidpDyKqOzQxAEpzOIb42NIhKzg5RXGZwdgiC4BFGXueHkzg6gtRKJWWioh26JpVsnX/y9NZjMVk5nlvLx+mNk5l96yFl4O0/mTO5BZKg3KoWUghIdG347y8akdABG9Q3l9uu7o1bK+GlfJivXHgNALpPw9hOj2fBbGut2nnXodekMZrRqhUPfo7URLWYHEV0ZQkONH9iRap2JHcnZ6PQm+nULYtE9gy87rfnZuQPpEx1IfnE1SYfzCA1054HpvekZ4Y+nm5L5M2KpqjFyKqOUG0ZE0q9bEAA3jozEaLKw4VfHJmWAGr0YLtdQosXsAGaLlRq9ydlhCC3MI29sIzW7tvBPoI+Glf8cj7+3hrAgj4sWBJJJJfh7awB48+tkMvMrCQ3yIKqDN4G+WvRGM0qFjK+2nOLo2WK+eGEiYcEeZORVMGNMF/714a5mWcShSif+W2gokZgdwGKxiXKHQoNdSMqAfZVpi8VKScXFR/hYrDbW7UzlhhGRLLg1jpzCKiLae3E2p5zdR3KRyaQYTRb+NqErJRW1XWuZ+ZXcPS2G3UfyOJ7WPLVcqmrEt8eGEl0ZDmDDJgbVC42mVspYMDMOgIQdqZRWXvp5xe6j+RQUV9MlzIdR/TpgsdrYfTQPncFMRbWRt7/5Ha1aQWSoFwnbz2CxWOkdFcCXW0/x0C2xvP/MGBbdO5h2/m4Oux6zxeqwc7dWosXsIGJQvdAYnm5K/nX3IKLCfNi8K51P1h+/5L4eWgX/unsQapWcp97eSUZ+JS/cO5hZ13WlvMrAxqR0fjmQxS8HsoDaB37LHh/Nqh9OMrZ/GP27B/HP95K454aeLJgZx1Nv/+qQaxKLsjacxCbWfmlyeqOZlYlH2bw7w9mhtGmv/n0QEWHeGCwWbNiw2Wy48l+7RCLBXaVBJpWiNxnRm+p3AUjPf+JbbTZkUikeai02m41yXTUAWqUKpVyBwWRE95fjVXIFSrmcSr0ON5UaCRKqDDrUCiUqucJ+jqYml0lxV2kdcu7WSrSYHUACosnsZDePiiQqREPVoR9RBHfG5h2ATOWGRqnBarVSUH2O9NIsMspzyCzLodzg/CpoTw19AJlUSqmunONFKfbth/OPk12RT1xIDDf3uJ68ygLe3vM/FFIFTw69H61SQ5mxjJKaMnoFdwNgzcnNHCk4aT+Hl8qDBYPn8dHBr0kvy2Zi1CiGhPUlrTydaL8I8ipLeWfv/xxyXbf1nEZMULRDzt1aicTsICItO0+Qr5bZ4yMo/OYVdOmH67wmdffFLbIv7mHd6BXUkZ7+EahUbpisZnIr8jlTksHZ0kwyynLIrsjDZGm+EQWeag8AfDRexIf1s28/lHeMlOI02nnUDnUzmI2kFKcBsHj7W8zsOZVwnw6EuAeSX1nI1tSdbEr5pc65Fw65m73ZyWxN3QlAfmUhWoWG7oFR5FcV8d7ez0grzXTIddWYRLH8hhJdGQ5gMJpZufYYm3alOzuUNumTf4xEnrGH4k3vX/UxypAItBF9ULXvgs0vBInWA7VCQ7m+kszyHE4Xp5FRlk1meS6FVeewIf6zuVpPDfs7fdv1dHYYLYpoMTuARCoRa505yb03xOApN5G99eMGHWfMS8WYl1p3o1yFtnMvwjv1oktIFNaOg5Gr3ZBJ5RRWFXG2NIszJelknu8OqTQ6po+2pZNJRBGjhhKJ2QEUMikeWjEFtblFtPdi0qBQ8j77P2zmJhg7azZQc3ofNaf31dks9/RHE9mX2LBuxIUOhK7eqFRajBYTORX5nClJ52xpJpllOeRU5GOytu0x7RqFqsHHfP/993z88cdkZmbi7u7O2LFjeeyxx/D09OTpp58mKCiIhQsX2vfPzs5mzJgxHDt2jP79+9u363Q6lEolMlnth8OiRYvIyMhg+fLl9u2RkZE89dRTxMXF2Y+rqKhg6dKl/Pjjj1RVVREWFsacOXOYPn36NdyJqycSswNIJBK8PdTODqPNWXx3X8p3r8Xw15ZvEzNXnKPy4A9UHvyhznZVuy4ERMTRvn0Uw6O7I9V4oFKoKdNXkFGWTUpxGhllOWSU53CuuqTNdIe4Kxs2Rvqjjz5ixYoVvPzyywwePJiCggIWLVrE3Llz+fLLL694fHJysv3/jx49msWLFzNkyBD7tmXLljFx4kSWLFmC2Wxm2bJlLFiwgB07dgBgNBqZM2cOfn5+fPXVVwQHB7Nr1y6efvppKioqmDt3boOupzFEYnYQL3els0NoU566vS8KfSmFO1c7LQZD7mkMuafrblSqcescS0SnnkQHd8UaHo9C5YZUKqOwqojUkszz3SG5ZJbnUG1sfaveuCmvfqhcVVUVy5Yt48UXX2T48OEAhIaG8t///pcxY8awdu3aJo1NLpczZcoUli9fTklJCb6+viQmJpKXl8fnn3+OVlsb+/Dhw/nnP//Js88+y4wZM3B3d2/SOOrF5dCzt2Fe7g3/+iY0Tp/oQIZ09ydn5eNgc7FZZkY91Sd3U31yd53Ncu8gtBF96BvWjX4d41F4BaBUqNEZdWSW55JSkkZaaRaZ5TnkVBRgdkB3SM+grvzfyAV8c3QD3xxb3+Tnv0Ajv/pvjwcPHsRgMDB+/Pg6293c3BgxYgRJSUkolU3X6DEajSQkJODt7Y2npycASUlJDB8+3J6ULxg/fjyPP/44ycnJDBs2rMliuBiRmB3EXSP6mJuDXC7lH7N7UfLLF5hK8pwdzlUzlxVQcWATFQc2IVW7E/bQcgwFGZRs/pDAiN506NAdZd9hyORKJOfHxB/IOczpkjQySmtHh4yLGMbA0DjaedYOo3tnz//Ynv7HB8CMHpOYETO53nvPW/M4lcZqjhScJKMsm8nRY9h4+meqTU3fWpdJpChkV59mSktL8fHxQS6vf0xAQADHjh0jKCjomuPavHkz27Zto7q6Gk9PT9566y37e5aWlhITE1PvGLlcjo+PD6Wlpdf8/lciErODuGtFV0ZzeOGeAVjPZVKxf6OzQ2k0954jkCo1VB/fiSH7BIbsEygDOxI0/Ql0ZQVoO8cCEFWQT9fg7ljDh6FUu6GQKjBYjBgtJpQyBSEeQWgVmnrjhndnHaRYV2b/3finsdlJmQe4rdc0hnbszw9ntjf5tXmrvTBaTKjlV/cN8kLiM5vN9ZJzUVERPj4+yGQyzOa63yDMZjNSqRSp9OpGQ02YMIElS5ZQUlLCww8/zLFjxxg4cKA9hqKionrHmM1m+weHo4nE7CBebkokElx6CnBLN6pvKN07eJD9/j+dHco10UbVTibRZfxRF8NYmEHWe/NRBnWyJ+ZzfxmXLfcJQhvZD5/ht4JMwfWRI5jadSw6k57s8jykktokdTDvKDsz9mKx1l9J5HhRbZ9433Y9HZKYfTReF33fS4mLi0OpVLJlyxauv/56+/bq6mp27NjBo48+SkFBAWfOnKlzXHZ2NiEhIVedmC/w9fXlhRdeYPr06UyePJnAwECGDBnC66+/Tk1NTZ3ujC1btqBUKomNjW3QezSGGGzrICaLFV9PMTLDUdzVch6+qTvnNr6PpbJ5ylc6iiqwIwCm4uwGHWcuLaBi3wbM5YUAVPywksyXbqNy9asEpxwhXF77gOqB/rez6uZlfHHzMl4a+xTTuo4nLqQHfhofsivyAejk06EJr+gPPhovJA2YB+vh4cGDDz7I4sWL2bFjByaTiezsbB555BGCg4OZNm0a1113Hdu3b+fXX3/FYrFQUFDAe++9VyeRN0Tnzp0ZNmwYK1asAGDatGkEBwezYMECsrOzMZlM7Ny5k8WLFzN//nw8PDwa9T4NIVrMDmKx2Aj2c6O4XKyW7QgvPzAIfcYRqo87piJac5Kqa4eTWQ1NMXXZij7zGPrMY9hMejThvTEV56DwDUHTqScRfp1ojxJzpAaFSsuF4gGeKnfGRQwjoyyHrPJcdOam+bv10/oglzVsgsk999yDt7c3r776ap1xzEuWLEGpVBIVFcXSpUt5/fXXycjIwNPTkwkTJjB//vxGxzlv3jzuvPNO7rvvPvz8/Pj44495/fXXueWWW6iqqqJDhw4sXLiQGTNmNPo9GkJMyXYQncHM+2sO89O+LGeH0urcNCqSO0aHkfXefKz6KmeHc806PvIRMjcv0pfegVVfd/agMqgToXcvBeDsixef3ND+7iWogsIpXPc2VYd/ueg+AIE3LMS9x1AqDm6xd4so20UROvdlrGYTNeUFSLReqFQaaow6ssrzSClJI700m4zybPIqC7E2cNTL/f1nM7pzfIOOEUSL2WFUShkhfo4rPt5WBfhouGN8BIXfvtoqkjLU9idrwnuh8AvFkHOqyc4r9wnGXJpf/4U/tcUk5/tk9RnHKPzq37UbpXLUYd1p17k3ndpFYmsfh1TtjlKuorimhPSybE6fS6udil6eQ6mu/rJXF4Q7qIuktROJ2UGkEgmd2nk5O4xW59X7B1B9bCe6tN+dHUqTqTlzAE14LzQdu9sTs1Tjgd+YO5Fq/pjIEDC59qt60fq3AfAafCNKv/bIPf0B8IwdgyasBxWHfsSQfZKQWc9hqSrDWJSJ3NMfbUQcNquFqj91/2jCethjsLOa0acfRv/XynxaT7QRcXTvGENMcCy2qFEoVW5YsZFXWUBqSQapJRnnJ8vkYjAbCHIPaPob1gaIxOxAndt5OjuEVuXuqT3wUprJ3vKRs0NpUpVHtuEzYiZu3YdRlrQGAKlSjUfvUXX2u/D7hcSsjYhF0/GP8bbqDt1Qd+iGLvMYhuyTVCb/iFv3eNy7D8Vms6LPOklp0nfoM/8Y/eHWfShWo46qo1cekWGtqaDqyHaqjtTdVxHQAc+IvgwOjWZQxGgkWg9USi3VxmqUUjGevzFEH7MDmS1WZv5zIwbj1Q8XEi6uczsv3nhoMHlfPIch98yVD2hhfIbPxGfYDPK+WFSvhrSjaDr1IuRvz1O6czWlO75u2pNL5XgNmIT30JuRidVLGkwkZgeq1plYtGI3J9Jb9nAuV/Dl86MxHdlC6bZVzg5FuEqe/SfhO3o2UrmYbNVQYhyzA8llUiJCRT/ztXpydh+UhrKmb9UJDqXu0E0k5UYSidmBVEoZPcL9nB1Gi9Y7yp/4HgEUfPcaNGAGmeB86g5dnR1CiyUSs4N16ej4efWtlVwu5Z93xFKybRWm4hxnhyM0gMzdxz5xRmg4kZgdzMdDhaeb+DrXGIvm9cdWnEXFvpZboKitUnfoBpa2vXLLtRCJ2cGMZiu9Iv2dHUaLM6JPe2I6elG4Zim0kZU+WhNN51gkSo2zw2ixRGJ2MK1KzoAewc4Oo0VxV8tZML0HRZtafoGitkoT3tNeR1poOJGYHUwikRDXJdDZYbQoLz0wCGPmUaqP7XR2KEIjSDUeyN3Fs5VrIRJzM9CoZAT5ikH2V+OGERF08FNStO5tZ4ciNJI2sg820b98TURibgY2G8R1ETUDriTAW82cCZEUJvy31RQoaovcY2pXZBEaTyTmZqBWyRnVT1TZupJX7h9I9fHf0J095OxQhEaSyJWow7o5O4wWTyTmZhLVwRsPrSjocilzp3THR2WheMtKZ4ciXANNp55imFwTEIm5mZgtNgbFhDg7DJfUKcSTG4aEUfDda9hMBmeHI1wDtx5DkSjFkmrXSiTmZqJRyRk3sKOzw3BJ/7m3H+X7N2DITXF2KMK1kMpwi+qHRCLSyrUSd7AZRYZ6iVmAf/HYrDhUxnJKt3/l7FCEa6SNiHN2CK2GSMzNyGyxMbin6M64oFekP8N7BlLw7auiQFEr4NlvIlJRe7lJiMTcjDQqOdOGRzg7DJcgl0v5vztiKdn+pShQ1ApItZ6ow7o7O4xWQyTmZhbgrRE1moHn7+oHpdlU7N3g7FCEJuARMwKsoqZJUxGJuZkp5NI232oeFtueXp28KfheFChqLTz7T0SqVDk7jFZDJOZmJpNJGdKrHVp121wHV6uWs3BGD85t/gBLZbGzwxGagKp9NDKt+BbYlERidgKb1caYNjoT8OX7B2LMOk7V0R3ODkVoIj7DZiARS0g1KZGYnUCtknPjqCjaWlXEqcM6E+avpmjdW84ORWgicp9g1GE9kEhFKmlK4m46ibtaQf/ubadOs5+XmrsmRlGY+F+sOlGgqLXwHnKjSMoOIO6ok2jUcm6f2HaKvbz6wEBqTu5Cl5rs7FCEJiLVuOPeYxgSWdt8XuJI4o46UZCvll6R/hw+c87ZoTjUnEnd8VVbyfphhVPjCJgyH014L2QaT6xGHYa8VEp++QJjQRqaTr3wGX4LyuDOSBUqdBlHyfv8+cueT6p2x2/sHDSdeyPTeGCpLqP61F6Kf/4ULGaUwZ0JmPx3FD7B6DKOUbR2mb2cacCU+UhV2trJNS2UV79Jzg6h1RItZifSqOTMmdzD2WE4VMcQD24ceqFAkd6psci9AtBnHKfy95+x6CrRRsQRNOMpABR+IUgUKoxFWVd9Pr+xc/DoPQpsVioP/4JEqcZrwCR84qcDEHD9/cjdfak+sQttRBzeQ24CQBUajVvXwRRv+bjpL7KZSNVueA2aglQhhsg5gmgxO1looDsxEX4cTW2dQ8f+c09/KvZvxJBz2tmh1GkBK4PDCZ23BLmHL0hlVBz4gYoDP+DZfxLqdpFXdT65TxAAZbsSqNi/CatRh/egaci9ahdFUPiHUnnoJ4q3rETVLgplQAeQSPGfcA9lSd9jrihq+otsJt5DbwaJzNlhtFoiMTuZRiXn3ht68vDSbc4OpcktvC0WtbmCbBcqUOTZbyIK/9DausFA+Z51ja7TUbFvA+r2UXgPvgFlQBhu3QZjqamgYv8mAEznsnHvHo9M64nCrx01Zw7i2fc6pAo1ZbsTm+yampvM3QfPPtchVYghco4iujJcQLCfG8Ni2zs7jCbVM8KPkb2Czxcocp3C6W5dB+HVdwJKv/aYK86hzz7Z6HPpc8+gzz6F3NMfzz7jkWk80GUcxVRWAEDRxuWYq0rQRvWlJjWZyt9/wmf4TM5tWYnXgCmE3vcm7ea+giaiT1NdXrPwGTlLlPZ0MHF3XYBGJee+G3uilLeOfw65FJ67M47SHV9hOpft7HDqyPv8edJenkn+Ny8jc/ch6KbHkXs2bj3GoJseQ9MxhuKfPyftldso378J925D8J9wHwDG/LPkrHic9NdmU7D6JbwH34A+8zhYrfiNnk3xj/9Dl/Y7QTc9ilTt1pSX6TBynxDcu8cjkYvVeBypdWSCVkCllHHTqChnh9Eknps3EMpyarsJXIREroTzrTybxURNajI2ox6JTG7vK74sqQyFX3sUfu2B2plByoDa2ZuG3BRsZiOGvNTa7f71v/2o2kfj1m0IxVs/QhnUCQB9xlH02SeRKjUofFrGmHb/CXeDVPQtO5roY3YRaqWc6aMj2bIng5IK545euBZDe7ejd7gXWR88jysVKFK1iyLwhkfQZx7Hqq9G3aEbUrUblupyDPlnUYV2xTN2LIqAUAAUfu0JmDwfY3EO5bvWIPfwpcP9tTMW05fcjtVQgz7rFNqIWAImPYAu7TDaLgMA0Gf9pXtEIsV/wt2U7VqDubzIXuY06OYnkXv6YzObMJUVNt/NaCRtVD/UoV2RinHLDidazC5EJpVwz7QYZ4fRaFq1nEdn9ODcDyuwVLjW2GxLVQmmklw04b3xiB2NVONG1fEkcr94HpuhBoVvCB69R6FuV/utRe7ug0fvUWgjYi95zqL1y6g8vA2JXIlHr1FgMVG+byPFP31aZz/PPuORKjWU7UoAoCZlP+V7N6BqF4VUpaVo43tYdZWOuvQmIVGoCJj0d6RiPb9mIbHZbK7TrBHQG828+PFeDp1ueUOp/rsgnmBjJgVfv+jsUIQm5jvmzvMjSsS45eYgWswuRq2U89jf+qJRtayvi1OGhtMpQE3RWlGgqLVR+IeKpNzMRGJ2QRqljLuntpwZgb6eauZd34XCtW+6/FdyoYEkUgKnPYJEJkZhNCeRmF2QSilnRJ9QenT2c3YoV+XVBwZQc2o3ujMHnR2K0MS846ej8A0RFeSambjbLkqllPPE7L6oFK49NOmO67vhp4Vzmz90dihCE1MGd8Z7yI3igZ8TiMTswtw1SubP6O3sMC4pLMiDm4Z1dIkCRVfiM3wmnZ/9Dk2nXs4OxU6qdqPT458TMnuRs0OpRyJXEnzzU2JlEidpWU+Y2hiVUsagmBBG9w3l5wOuNYMO4D/39qfy4GYM2aecHcplSdXueA2cjKEgA136YQBkHr4EXP8AqvZRyDQeAJx9cXqd44Jvew5VUCekai0WXTWG7JMU//g/zOW1Y459Rs5CG9kXxfmiRYbCDEp++QLD+Wne6rAetLv9hXrxFHz3GtUnd2PVV1N5+Be8+l+PJrwXurTDDrsHDeV33TykWg8kbW2ZHRchWswuTq2S88DNvekQ5OHsUOp45NZYNJZKSn5Z5exQrsi95wikSg3Vx3fat8k0Hih8Q+yz9S5G7ul3vsbFL2Cz4NZ1EAFT5v9x3h7DAKg6uRtzxTk0Yd0JmfksMnefOufR55ymfO96+4+pJM/+WtWxXwHwiBvfJNfaFLRd+uPeY5gYheFEosXcAijlMp6/exAPvvYzBmPjKqE1pR6d/RgVG0zOJ0+5VIGiS9FG9QNAl3Hcvs1YmEHWe/NRBnVC2zn2osdlv7/gj3OcHUjwzU+i8A60byv47jWM+WcBkCjUdHxkBVKVFlX7LtSc2mPfT5eaTOnO1Rd9D0NuClaToTYGiRRs1sZeZpNQ+IYQOG2BSMpOJhJzCyCVSvB2V7Hg1jhe/Wy/U2ORS+H5OXGU/roaUwOKyjuTKrAjAKbihncHeQ+dgdzDF21kX2xWS51ynReSMgASkEhr/3OyVNatre01cAreQ27CXFlC1dEdlP767R8faDYrppJ8VEEdUfiG2KdrO4NEoSb4tueQyEVSdjbRldFCqJQy+ncL4qaRV1fE3VH+764BSMrzKN/VcuoJX6jcZjXoGnysR+/RePYZj9zTD1NxLoY/J+MLJFICpzyERK6g6vhvGHLPnH/BhrEwg+qTu6k+uRuZuzc+w2bgM/yWOofbjDXn43RvcHxNR0LQ9CeQuXmLoXEuQPwLtCBqlZxZ10UzKMY5lciG9AwhNsKHwu+W4EoFiq7Eqq8GQKrSNPjYrHceIO3VWRRt+gBlQAeCb/kHkj99zZfIlQTf8jRuXQdRnbKfwj/NfNRnHif7w0cpWv8OhYn/pXhr7VJSbl0H1XkPiVJbG6ehusHxNRWfkbNQd+gqit+7CJGYWxjV+SnbkaHezfq+aqWcx26N4dwPK1vckkjGwgwAFH6hV32MRPHH2F2byUDN6do+Y5nGHZnWC6ht4Yb87Xm0kX2pPPwLBd+8ApY/+tzl3pcoJ/rn8jQSKQrfYKxGHabi3KuOrym59xyJ14BJYryyCxF9zC2QWinn3/cN5uGl2ygqa/jX88Z46f6BmHNPU3X452Z5v6ZUc+YAmvBeaDp2x5BTO7RPqvHAb8ydSDV/dB8ETK4dcVG0/m3cug3GZ+jN6LNPYTMb0YTXjn82nsuxD5cLvuUZ1KFdsegqsepr8Bt7Z+37pSajO3sIn6EzUHfoij77JBKpHG10bVnQqqM77O+paheFVKGi+uRupzz400T2wX/iveJhn4sRLeYWSqOS85+/x+OmcXwNg0nx4YQHqSlc+6bD38sRKo9sw2rU4dZ9mH2bVKnGo/co3Lr0t2/z6D2qdtVrwFSci6W6HG1kX9x7jgCJlIpDP5H35R/jkmUevrX/q/HAa8AkvAZMxmvAZNTtuwBQfXov5qpStJH90EYPwFxWSPHWTyj77Xv7Odx7DAWg4uAPDrv+S1G170LQjY+JpOyCRNnPFsxkspBXXM0Ty3ZSo3fMsDUfDxUfPzOSosQ3qElx7oiQa+EzfCY+w2aQ98Ui+yQTZ5Oq3Qib/z6G/LPkff5cs763wj+U9nNeQqrSNuv7CldHJOYWzmiykF1YxdPv/IrO0PTJ+cMnR+BWeJiiFtpaFuqTewXQ/q5XkWrcxaKqLkr8q7RwSoWM0EB3XnwgHpWyaQsezZ7QFX83G+c2v9+k5xWcR+4TTPu5ryBVu4mk7MLEv0wroFTICAv2YPH9Q5pspe3QQHduHtGRwu+XYjO6doEi4eoo/NrTfu7LSDUeSMSCqi5NJOZWQqWQER7ixcsPDsVNfe2DbV66rz+VyVvRZ51ogugEZ1MEhNFuzku1LWUxgcTliX+hVkSllNGxnSdvLByJr2fjx6Q+fEtvtNZqSn75vOmCE5xGGRxO+ztfRKrSiu6LFkL8K7UySrmMAB8Nbz46ktDAhk/x7R7uy5g+IRR8+2qdyRJCy6SJ7EO72xefT8qihGdLIRJzKySXSfF0U7J0wXCiw3yufMCF46Twrzl9KN35DaaiTAdGKDQHz/6TCbrpcTGjrwUSibmVkkolaNUKFj8whGGx7a7qmGfn9EdSmU/57pZToEi4CIkU/4n34TvyNjF5pIUSibmVUyvlPHxrHPfe0BOp9NJfZQf3DKFPlG9tgSIn1wQWGk+iVBNy23O4xwwXLeUWTEwwaSP0RjPpeRX8e+UeKqqNdV5TK+V88dwoyn/6hMrff3JShMK1UgZ2JPjWZ5FqPESVuBZOJOY2xGS2Uq038a8Pd5GaXW7f/vpDQ2hvzSX/y/rr0wktg0fcePzGzUEiV4qHfK2ASMxtkMFo5vNNJ0jceZYJgzpx3+RIst6bj7WmwtmhCQ0kUagJnPowms69RddFKyIScxulN5hJyy0nqp2Wc2vfpOb0PmeHJDSQKiSCoJufRKrxFF0XrYyox9xGqVVyIjt4I8Mq1nhrYSQyBT6j/oZnn/Gi66KVEi1mAatRjz7jGEUb3sVSXebscITLULWPJuimR88/4BMfqK2VSMwCADazCZvVQsn2L6nYt1EMmXMxEoUa39Gz8eg9WiTkNkAkZqEOq1GPpaqMwnVvYcg+5exwBCS4xwzHb/xcJHKlSMpthEjMwkVZTQZqTu+jeOtHWKrLr3yA0ORU7bsQMOkB5F6BYsRFGyMSs3BJNrMJm81KxYHNlP32HVZ9tbNDahNknv74j7sLTUSseLjXRonELFyR1WQEm5Xy3YmU7VkrCuc7iNwrAJ/ht+LWbQgSqQyJTAyaaqtEYhaumtVkAKuF0l+/peLgFmxGnbNDahXk3kH4jJiJW/QgJFKpSMiCSMxCw1nPt5grD/9M+a61mCuKnBxRy6QM7Ih3/HS0Uf1FQhbqEIlZaLTaPmgbuvQjlP32LYac084OyfVJpGi79Md7yHSUAaFIpHIkMrH+nlCXSMzCNbNZrdjMRixVpZQf2EzV0R2i7sZfyDz98ewzHs8+1yGRSpGqtM4OSXBhIjELTcpq1INUiiHrJOUHNlNz5kCbXaJKqvXEvdsQPOLGofCrXaxAKhc1LYQrE4lZcBiroQYkUmrOHKT6+G/UpB1q9SM6pGp3tF3649lnPKqgcGxWixiDLDSYSMyCw9lsNmxGHRKZAkP+WaqO7qAm5UDreGgokaJqF4U2sg9uXQeh8A7CZjEjVWmcHZnQgonELDQ7q1EPEik2ow591glqzh5Cn3US07lswLX/HCUyBcqgTqjaRaKN6oe6QzewWmongohRFUITEYlZcDqrUQ82G0gkGPLPYsg+jbEwHWNRJqbiXGwWk1PikijVKHxDUAZ0RB3WDXWH7ii8A7GajEhksiatWzF69GjOnTuH7E8jNDZv3kxQUBBZWVmMGzeOW2+9lUWLFgEQFxdn30+n06FUKu3HLlq0iKlTp3LmzBmWLFnCvn37sFqtxMTEsHDhQvr06QNAdnY2Y8aMQautfRDp4+PDzJkzuffeey8ZZ3R0NFu2bKFjx472bcuWLSMjI4MlS5bYz3ns2DHk8j8+qJ5++mmCgoJYuHAh33//Pc888wx33nkn//jHP+z7/Pjjjzz44IPceOONvPzyy/bt1dXVDB06lL59+7JixYp6902n0/HTTz/Zr+Obb75h7dq1fPbZZ0DtN7aVK1eyevVq8vPz8fX1ZcqUKTz00EMola7Z5y8+4gWn+3MfrCasO+rQrlhNeiSARK7CUlOO6Vw2xuIcTCV5WCqKMVeVYKkqw1Jdhs1svPTJL/mmMmRaD2RaL2RaL6RaT+Re/igDO6EM6IDcKwCpQnX+3JI6XRMyB7WMly9fzpAhQ+ptT0xMxMvLi02bNvHss8+iVCpJTk62vz569GgWL15c59jMzExuu+02Zs2axcsvv4xcLuf7779n3rx5fPTRR3US+759+5DL5Rw5coTbb7+dHj16EB8f75BrvCAsLIxNmzbx5JNP2hN4QkICnTp1qrfvli1bUCqVJCUlUVRUREBAQJ3XrVYrn376Kffff/9F32vx4sXs3LmTV155hZ49e5KWlsYzzzzDmTNneO+995r82pqCSMyCy5FIpcj+NJxM7uGL3MMXTXgvrGYTWGrHT0ukUiRyJSDBZjFjs5iwmY21PyZD7cHnxwlLpDKQ1v6vRKFCIlPUtsQtFmzUttalcgUSmaJuLE7unrDZbCQkJLBgwQLefvttfv75ZyZMmHDF45YtW0ZsbCwLFy60b7vjjjtITU1lyZIlfPHFF/WO6dmzJ5GRkZw4ccLhidnf3x+tVsuvv/7KyJEjKSsrIzk5malTp1JaWlpn3zVr1jBz5kx27NjB2rVrmTdvXp3X582bx4oVK5g1axaenp51XktPT2fVqlV8/fXX9OrVC4CoqCiWLVvGuHHj2LVrF4MHD3botTaG1NkBCEJDSOUKpCotMrUbUqWmNtFKpUgVSmRqN+TuPii8g1AGhNX++LVD4R2E3NMfubsPMq0nUoXq/DEqpOrac8lU2npJ2RUcOHCA/Px8Jk2axMSJE0lISLiq45KSki6awCdOnMjBgwfR6+uPjjl06BApKSl1uikc6YYbbrBfz4YNGxgzZky9roWcnBz27t3LlClTmDJlykWvPyYmhgEDBrBy5cp6r+3atYvg4GB7Ur4gJCSE2NhYkpKSmux6mpJIzILgIh588EH69etHv379+Pvf/w7UthaHDx+Ol5cXkydPZufOnRQXF1/xXKWlpfW+8gMEBARgtVopL/+jlOugQYPo1asXt956K7NmzWLs2LGXPfeNN95oj7Nfv3588MEHDbzSWuPGjWPv3r1UVlaSmJjItGnT6u2TmJhIdHQ0kZGRTJo0iTNnznD8+PF6+z388MN8/vnnlJSU1Nl+qfsAtffir61zVyESsyC4iHfeeYf9+/ezf/9+3n33XfR6PZs3b2bKlClA7QO/kJAQ1q1bd8Vz+fj4UFRUfzhiUVERUqm0zlf+3bt3k5yczNNPP82ePXswmS7/sHXNmjX2OPfv31/nYeGF/mKzue6kIpPJhEJR9xuJWq1mxIgRvPvuu5SVldG3b99675WYmGi//qCgIPr378+aNWvq7delSxdGjhxZ70PiUvcBau+Fj4/PZa/VWURiFgQXtXXrVqqqqli0aBHx8fHEx8dTUFBwVd0ZgwcPZvPmzfW2b9q0idjYWDSauuOsZTIZc+fORaVSsWrVqkbHHBAQgEKhICcnp872nJwc2rVrV2//G264gY8//pipU6fWe+3gwYOkp6fzwQcf2K//8OHDrF+/vl7ih9pW8+rVqykoKLBvGzRoEHl5eRw+fLjOvnl5eRw6dMgl+5dBJGZBcFkJCQlMnz6ddevWkZCQQEJCAl9++SUnT57k1KnLL/s1f/58kpOTeeONNygrK6OqqorPPvuMxMREHn/88Used++997JixQoMBkOjYpbJZIwfP5433niD0tJSTCYT69ev58yZMwwfPrze/gMGDODjjz9m9uzZ9V5LSEggPj6eDRs22K9/3bp16PV6duzYUW//jh07cv3119uHyQGEh4czc+ZMHn/8cQ4dOoTFYiElJYWHHnqIIUOGXHQUjCsQiVkQXFBBQQG7du3izjvvJCAgwP4TExPDsGHDrthq7tSpE6tWreLkyZOMHj2aYcOGsWXLFlasWHHRLoMLRo4ciZeXF6tXr2507M8//zxeXl5MnTqVIUOG8Pnnn/P+++/j7+9fb1+JRMLgwYPx9vaus91gMLBp0yZmz55d5/o7dOjAtGnTLnn9Dz74IDU1NXW2Pffcc9x888088cQTxMXFcffddzNgwACWLVvW6Gt0NDHBRBAEwcWIFrMgCIKLEYlZEATBxYjELAiC4GJEYhYEQXAxIjELgiC4GJGYBUEQXIxIzIIgCC5GJGZBEAQXIxKzIAiCixGJWRAEwcWIxCwIguBi/h+MKewHT9JBywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "\n",
    "def func(pct, allvals):\n",
    "    absolute = int(pct/100.*np.sum(allvals))\n",
    "    return \"{:.1f}%\\n({:d})\".format(pct, absolute)\n",
    "\n",
    "\n",
    "_, _, autopcts = ax1.pie(new_values, labels=new_labels, autopct=lambda pct: func(pct, new_values))\n",
    "\n",
    "\n",
    "plt.setp(autopcts, **{'color':'white', 'weight':'bold', 'fontsize':12})\n",
    "\n",
    "\n",
    "ax1.axis('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecionando os top 10 fatores contribuintes das ocorrências"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>codigo_ocorrencia3</th>\n",
       "      <th>ocorrencia_classificacao</th>\n",
       "      <th>ocorrencia_cidade</th>\n",
       "      <th>ocorrencia_uf</th>\n",
       "      <th>ocorrencia_aerodromo</th>\n",
       "      <th>ocorrencia_dia</th>\n",
       "      <th>Dia</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Ano</th>\n",
       "      <th>fator_nome</th>\n",
       "      <th>fator_aspecto</th>\n",
       "      <th>fator_condicionante</th>\n",
       "      <th>fator_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>APLICAÇÃO DE COMANDOS</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>JULGAMENTO DE PILOTAGEM</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>PLANEJAMENTO DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39527</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>LUCAS DO RIO VERDE</td>\n",
       "      <td>MT</td>\n",
       "      <td>****</td>\n",
       "      <td>04/01/2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>PROCESSO DECISÓRIO</td>\n",
       "      <td>ASPECTO PSICOLÓGICO</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>FATOR HUMANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39707</td>\n",
       "      <td>INCIDENTE GRAVE</td>\n",
       "      <td>CANUTAMA</td>\n",
       "      <td>AM</td>\n",
       "      <td>****</td>\n",
       "      <td>09/01/2010</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>INDISCIPLINA DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4154</th>\n",
       "      <td>79198</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>PATROCÍNIO</td>\n",
       "      <td>MG</td>\n",
       "      <td>****</td>\n",
       "      <td>19/02/2020</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>JULGAMENTO DE PILOTAGEM</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4155</th>\n",
       "      <td>79198</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>PATROCÍNIO</td>\n",
       "      <td>MG</td>\n",
       "      <td>****</td>\n",
       "      <td>19/02/2020</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>PLANEJAMENTO DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4158</th>\n",
       "      <td>79397</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>CORRENTINA</td>\n",
       "      <td>BA</td>\n",
       "      <td>****</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>JULGAMENTO DE PILOTAGEM</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4159</th>\n",
       "      <td>79397</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>CORRENTINA</td>\n",
       "      <td>BA</td>\n",
       "      <td>****</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>PERCEPÇÃO</td>\n",
       "      <td>ASPECTO PSICOLÓGICO</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>FATOR HUMANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4160</th>\n",
       "      <td>79397</td>\n",
       "      <td>ACIDENTE</td>\n",
       "      <td>CORRENTINA</td>\n",
       "      <td>BA</td>\n",
       "      <td>****</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>PLANEJAMENTO DE VOO</td>\n",
       "      <td>DESEMPENHO DO SER HUMANO</td>\n",
       "      <td>OPERAÇÃO DA AERONAVE</td>\n",
       "      <td>FATOR OPERACIONAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2645 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codigo_ocorrencia3 ocorrencia_classificacao   ocorrencia_cidade  \\\n",
       "0                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "1                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "2                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "3                  39527                 ACIDENTE  LUCAS DO RIO VERDE   \n",
       "4                  39707          INCIDENTE GRAVE            CANUTAMA   \n",
       "...                  ...                      ...                 ...   \n",
       "4154               79198                 ACIDENTE          PATROCÍNIO   \n",
       "4155               79198                 ACIDENTE          PATROCÍNIO   \n",
       "4158               79397                 ACIDENTE          CORRENTINA   \n",
       "4159               79397                 ACIDENTE          CORRENTINA   \n",
       "4160               79397                 ACIDENTE          CORRENTINA   \n",
       "\n",
       "     ocorrencia_uf ocorrencia_aerodromo ocorrencia_dia  Dia  Mes   Ano  \\\n",
       "0               MT                 ****     04/01/2010    4    1  2010   \n",
       "1               MT                 ****     04/01/2010    4    1  2010   \n",
       "2               MT                 ****     04/01/2010    4    1  2010   \n",
       "3               MT                 ****     04/01/2010    4    1  2010   \n",
       "4               AM                 ****     09/01/2010    9    1  2010   \n",
       "...            ...                  ...            ...  ...  ...   ...   \n",
       "4154            MG                 ****     19/02/2020   19    2  2020   \n",
       "4155            MG                 ****     19/02/2020   19    2  2020   \n",
       "4158            BA                 ****     12/06/2020   12    6  2020   \n",
       "4159            BA                 ****     12/06/2020   12    6  2020   \n",
       "4160            BA                 ****     12/06/2020   12    6  2020   \n",
       "\n",
       "                   fator_nome             fator_aspecto   fator_condicionante  \\\n",
       "0       APLICAÇÃO DE COMANDOS  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "1     JULGAMENTO DE PILOTAGEM  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "2         PLANEJAMENTO DE VOO  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "3          PROCESSO DECISÓRIO       ASPECTO PSICOLÓGICO            INDIVIDUAL   \n",
       "4         INDISCIPLINA DE VOO  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "...                       ...                       ...                   ...   \n",
       "4154  JULGAMENTO DE PILOTAGEM  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "4155      PLANEJAMENTO DE VOO  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "4158  JULGAMENTO DE PILOTAGEM  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "4159                PERCEPÇÃO       ASPECTO PSICOLÓGICO            INDIVIDUAL   \n",
       "4160      PLANEJAMENTO DE VOO  DESEMPENHO DO SER HUMANO  OPERAÇÃO DA AERONAVE   \n",
       "\n",
       "             fator_area  \n",
       "0     FATOR OPERACIONAL  \n",
       "1     FATOR OPERACIONAL  \n",
       "2     FATOR OPERACIONAL  \n",
       "3          FATOR HUMANO  \n",
       "4     FATOR OPERACIONAL  \n",
       "...                 ...  \n",
       "4154  FATOR OPERACIONAL  \n",
       "4155  FATOR OPERACIONAL  \n",
       "4158  FATOR OPERACIONAL  \n",
       "4159       FATOR HUMANO  \n",
       "4160  FATOR OPERACIONAL  \n",
       "\n",
       "[2645 rows x 13 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_fatores = merge_fator_contribuinte.query('fator_nome in @filter_fatores.index.tolist()')\n",
    "top_10_fatores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Panorama das ocorrências por Fatores Contribuintes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "\n",
    "rc={'font.size': 12, 'axes.labelsize': 24, 'legend.fontsize': 40, \n",
    "    'axes.titlesize': 24, 'xtick.labelsize': 12, 'ytick.labelsize': 12}\n",
    "\n",
    "sns.set(rc=rc)\n",
    "\n",
    "ax = sns.countplot(x= top_10_fatores.fator_nome ,data=merge_fator_contribuinte, hue=merge_fator_contribuinte.ocorrencia_classificacao)\n",
    "plt.legend(fontsize='x-large')\n",
    "plt.xlabel(\"Fatores Contribuintes\")\n",
    "plt.ylabel(\"Contagem Classificação\")\n",
    "plt.title(\"Classificação das ocorrências por Fatores Contribuintes\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend(bbox_to_anchor=(1, 1), loc=1, borderaxespad=0., prop={'size': 20})\n",
    "\n",
    "\n",
    "for p in ax.patches:\n",
    "        ax.annotate(p.get_height(), (p.get_x()+0.01, p.get_height()+5))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}