FirstOcomonNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise de dados do Ocomon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Os comando usados aqui seguem o tutorial da HackerLab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Os dados aqui importados já estão tratatos e com os devidos novos campos adicionados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bibliotecas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate(\"{0:.2f}\".format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Carregando o arquivo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import DataFrame\n",
    "# if csv don't are split per \",\", then we've to add a new args to method, \n",
    "# ex: read_csv('x.csv', sep=';')\n",
    "\n",
    "#the pattern code to decode file in read_csv() method is utf-8\n",
    "# if the file don't use this enconde, use enconding='cp1252'\n",
    "df = pd.read_csv('ocomon_FINAL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>DESCRIB</th>\n",
       "      <th>PROB_ORIG</th>\n",
       "      <th>PROB</th>\n",
       "      <th>ESPC</th>\n",
       "      <th>EQUIP</th>\n",
       "      <th>I_NAME</th>\n",
       "      <th>R_NAME</th>\n",
       "      <th>C_NAME</th>\n",
       "      <th>EQUIP_ID</th>\n",
       "      <th>HAS_VALUE</th>\n",
       "      <th>...</th>\n",
       "      <th>HOUR_OPEN</th>\n",
       "      <th>MIN_OPEN</th>\n",
       "      <th>DATA_OPEN</th>\n",
       "      <th>CLOSE_YEAR_4</th>\n",
       "      <th>CLOSE_MONTH_4</th>\n",
       "      <th>CLOSE_DAY_4</th>\n",
       "      <th>HOUR_CLOSE</th>\n",
       "      <th>MIN_CLOSE</th>\n",
       "      <th>DATA_CLOSE</th>\n",
       "      <th>STI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foi solicitado a manutencao de pontos de reded...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>internet</td>\n",
       "      <td>teresina</td>\n",
       "      <td>jaclason machado veras</td>\n",
       "      <td>julia sousa santos</td>\n",
       "      <td>-999999</td>\n",
       "      <td>NP</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>18/4/2012</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>26/4/2012</td>\n",
       "      <td>sti_cca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>computador nao liga  sujeira e mau na memoria ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hardware</td>\n",
       "      <td>ram</td>\n",
       "      <td>computador</td>\n",
       "      <td>teresina</td>\n",
       "      <td>secretaria do mestrado de piliticas publicas</td>\n",
       "      <td>luis carlos dos santos farias</td>\n",
       "      <td>140981</td>\n",
       "      <td>P</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>23/4/2014</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>23/4/2014</td>\n",
       "      <td>sti_cchl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>meu computador esta ligando o monitor tb mas a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hardware</td>\n",
       "      <td>monitor</td>\n",
       "      <td>monitor</td>\n",
       "      <td>teresina</td>\n",
       "      <td>gabinete do reitor 5511</td>\n",
       "      <td>constanca dolores lopes monteiro</td>\n",
       "      <td>-999999</td>\n",
       "      <td>P</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>43</td>\n",
       "      <td>24/10/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>24/10/2011</td>\n",
       "      <td>sti_central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>desabilite o envio de email como o servico de ...</td>\n",
       "      <td>utilize esta descricao de problema para proble...</td>\n",
       "      <td>N.A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e-mail</td>\n",
       "      <td>teresina</td>\n",
       "      <td>guilherme</td>\n",
       "      <td>guilherme avelino</td>\n",
       "      <td>-999999</td>\n",
       "      <td>NP</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>24/10/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>26/10/2011</td>\n",
       "      <td>sti_central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>instalacao de impressoras e remocao de virus  ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hardware</td>\n",
       "      <td>instalacao</td>\n",
       "      <td>impressora</td>\n",
       "      <td>teresina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bolsista nti</td>\n",
       "      <td>-999999</td>\n",
       "      <td>NP</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>24/10/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>26/10/2011</td>\n",
       "      <td>sti_central</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             DESCRIB  \\\n",
       "0  foi solicitado a manutencao de pontos de reded...   \n",
       "1  computador nao liga  sujeira e mau na memoria ...   \n",
       "2  meu computador esta ligando o monitor tb mas a...   \n",
       "3  desabilite o envio de email como o servico de ...   \n",
       "4  instalacao de impressoras e remocao de virus  ...   \n",
       "\n",
       "                                           PROB_ORIG      PROB        ESPC  \\\n",
       "0                                                NaN       N.A         NaN   \n",
       "1                                                NaN  hardware         ram   \n",
       "2                                                NaN  hardware     monitor   \n",
       "3  utilize esta descricao de problema para proble...       N.A         NaN   \n",
       "4                                                NaN  hardware  instalacao   \n",
       "\n",
       "        EQUIP    I_NAME                                        R_NAME  \\\n",
       "0    internet  teresina                        jaclason machado veras   \n",
       "1  computador  teresina  secretaria do mestrado de piliticas publicas   \n",
       "2     monitor  teresina                       gabinete do reitor 5511   \n",
       "3      e-mail  teresina                                     guilherme   \n",
       "4  impressora  teresina                                           NaN   \n",
       "\n",
       "                             C_NAME  EQUIP_ID HAS_VALUE  ... HOUR_OPEN  \\\n",
       "0                julia sousa santos   -999999        NP  ...         9   \n",
       "1     luis carlos dos santos farias    140981         P  ...         8   \n",
       "2  constanca dolores lopes monteiro   -999999         P  ...        15   \n",
       "3                 guilherme avelino   -999999        NP  ...        13   \n",
       "4                      bolsista nti   -999999        NP  ...        12   \n",
       "\n",
       "  MIN_OPEN   DATA_OPEN  CLOSE_YEAR_4  CLOSE_MONTH_4  CLOSE_DAY_4  HOUR_CLOSE  \\\n",
       "0       15   18/4/2012             1              2            4           9   \n",
       "1       13   23/4/2014             2              2            4           9   \n",
       "2       43  24/10/2011             1              4            4          15   \n",
       "3       20  24/10/2011             1              4            4          17   \n",
       "4       13  24/10/2011             1              4            4           9   \n",
       "\n",
       "  MIN_CLOSE  DATA_CLOSE          STI  \n",
       "0         0   26/4/2012      sti_cca  \n",
       "1         4   23/4/2014     sti_cchl  \n",
       "2        59  24/10/2011  sti_central  \n",
       "3         6  26/10/2011  sti_central  \n",
       "4        11  26/10/2011  sti_central  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the 5 first line from dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#show the last 5 line from the dataframe\n",
    "df[df['OPEN_YEAR_4'] > 2][['DESCRIB','HAS_VALUE','ESPC']].sample(15).to_csv(r'analysis3.csv', index = False)\n",
    "df[(df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_4'] > 2)][['DESCRIB','HAS_VALUE','ESPC']].sample(15).to_csv(r'analysis2.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ESPC\n",
       "bateria              4\n",
       "estabilizador        3\n",
       "fonte              826\n",
       "hd                 109\n",
       "instalacao        1111\n",
       "monitor              3\n",
       "mouse                1\n",
       "nobreak           1052\n",
       "placa              419\n",
       "placa de rede       49\n",
       "placa de video       9\n",
       "processador          1\n",
       "ram                353\n",
       "software          4993\n",
       "usb                  2\n",
       "Name: ESPC, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('ESPC')['ESPC'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DESCRIB</th>\n",
       "      <th>HAS_VALUE</th>\n",
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       "      <th>EQUIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>pc apitando daa abertura da maquina recolocaca...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>temos um computador na copese que hj nao ligou...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>possivel problema na placa de rede solucao par...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>capacitor estufados  solucao para este problem...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>no break sem carga  solucao para este problema...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nobreak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15495</th>\n",
       "      <td>trocar bateria bateria trocada manutencao de c...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15544</th>\n",
       "      <td>o computador nao esta ligando  foi realizado a...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726</th>\n",
       "      <td>troca de bateria foi realizada a troca da bate...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15756</th>\n",
       "      <td>cpu esta problemasrecebido por joao pedro a so...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15783</th>\n",
       "      <td>bateria fraca ocasionando problema ao ligar o ...</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>computador</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 DESCRIB HAS_VALUE ESPC  \\\n",
       "569    pc apitando daa abertura da maquina recolocaca...         P  NaN   \n",
       "850    temos um computador na copese que hj nao ligou...         P  NaN   \n",
       "912    possivel problema na placa de rede solucao par...         P  NaN   \n",
       "1307   capacitor estufados  solucao para este problem...         P  NaN   \n",
       "1371   no break sem carga  solucao para este problema...         P  NaN   \n",
       "...                                                  ...       ...  ...   \n",
       "15495  trocar bateria bateria trocada manutencao de c...         P  NaN   \n",
       "15544  o computador nao esta ligando  foi realizado a...         P  NaN   \n",
       "15726  troca de bateria foi realizada a troca da bate...         P  NaN   \n",
       "15756  cpu esta problemasrecebido por joao pedro a so...         P  NaN   \n",
       "15783  bateria fraca ocasionando problema ao ligar o ...         P  NaN   \n",
       "\n",
       "            EQUIP  \n",
       "569    computador  \n",
       "850    computador  \n",
       "912    computador  \n",
       "1307   computador  \n",
       "1371      nobreak  \n",
       "...           ...  \n",
       "15495  computador  \n",
       "15544  computador  \n",
       "15726  computador  \n",
       "15756  computador  \n",
       "15783  computador  \n",
       "\n",
       "[102 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HAS_VALUE'] == 'P') & (df['ESPC'].isna())][['DESCRIB','HAS_VALUE','ESPC','EQUIP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DESCRIB</th>\n",
       "      <th>HAS_VALUE</th>\n",
       "      <th>ESPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>proreitoria de pesquis e posgraduacaosilmarlu...</td>\n",
       "      <td>P</td>\n",
       "      <td>instalacao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                DESCRIB HAS_VALUE        ESPC\n",
       "4897   proreitoria de pesquis e posgraduacaosilmarlu...         P  instalacao"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'instalacao')][['DESCRIB','HAS_VALUE','ESPC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>problema na placamae troca de placamae e insta...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>troca de placamae e instalacao de drivers e pr...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>nao tem nenhum programa instalado troca de mem...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>formatar e instalar linux  substituicao de pla...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>foi efetuada uma solicitacao de suporte tecnic...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15562</th>\n",
       "      <td>nao ligawindows corrompidolocal unepesaberto p...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15635</th>\n",
       "      <td>problemas no adaptador de video vga so e possi...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15701</th>\n",
       "      <td>nao liga  foi realizada a substituicao do hd f...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15711</th>\n",
       "      <td>nao ligatrocar bateriaaberto por gerisson troc...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15751</th>\n",
       "      <td>computador nao liga formatar fazer backup senh...</td>\n",
       "      <td>P</td>\n",
       "      <td>software</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 DESCRIB HAS_VALUE      ESPC\n",
       "660    problema na placamae troca de placamae e insta...         P  software\n",
       "742    troca de placamae e instalacao de drivers e pr...         P  software\n",
       "1192   nao tem nenhum programa instalado troca de mem...         P  software\n",
       "1617   formatar e instalar linux  substituicao de pla...         P  software\n",
       "2913   foi efetuada uma solicitacao de suporte tecnic...         P  software\n",
       "...                                                  ...       ...       ...\n",
       "15562  nao ligawindows corrompidolocal unepesaberto p...         P  software\n",
       "15635  problemas no adaptador de video vga so e possi...         P  software\n",
       "15701  nao liga  foi realizada a substituicao do hd f...         P  software\n",
       "15711  nao ligatrocar bateriaaberto por gerisson troc...         P  software\n",
       "15751  computador nao liga formatar fazer backup senh...         P  software\n",
       "\n",
       "[71 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'software')][['DESCRIB','HAS_VALUE','ESPC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[(df['HAS_VALUE'] == 'NP') \n",
    "   & ((df['ESPC'] == 'ram')\n",
    "   | (df['ESPC'] == 'hd')\n",
    "   | (df['ESPC'] == 'fonte')\n",
    "   | (df['ESPC'] == 'placa')\n",
    "   | (df['ESPC'] == 'nobreak')\n",
    "   | (df['ESPC'] == 'estabilizador')\n",
    "   | (df['ESPC'] == 'placa de video')\n",
    "   | (df['ESPC'] == 'placa de rede'))][['DESCRIB','HAS_VALUE','ESPC']].to_csv(r'analysis_err_NP.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[(df['HAS_VALUE'] == 'P') & (df['ESPC'].isna())][['DESCRIB','HAS_VALUE','ESPC', 'EQUIP']].to_csv(r'analysis_err.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>DESCRIB</th>\n",
       "      <th>HAS_VALUE</th>\n",
       "      <th>ESPC</th>\n",
       "      <th>EQUIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [DESCRIB, HAS_VALUE, ESPC, EQUIP]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'bateria')][['DESCRIB','HAS_VALUE','ESPC', 'EQUIP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.164283002151626"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((207+4+131)*100)/15802"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DESCRIB          15802\n",
       "PROB_ORIG         5853\n",
       "PROB             15802\n",
       "ESPC              8935\n",
       "EQUIP            15692\n",
       "I_NAME           15466\n",
       "R_NAME           15433\n",
       "C_NAME           15802\n",
       "EQUIP_ID         15802\n",
       "HAS_VALUE        15802\n",
       "DEPARTMENT       15774\n",
       "LOCALE           15583\n",
       "OPEN_YEAR_4      15802\n",
       "OPEN_MONTH_4     15802\n",
       "OPEN_DAY_4       15802\n",
       "HOUR_OPEN        15802\n",
       "MIN_OPEN         15802\n",
       "DATA_OPEN        15802\n",
       "CLOSE_YEAR_4     15802\n",
       "CLOSE_MONTH_4    15802\n",
       "CLOSE_DAY_4      15802\n",
       "HOUR_CLOSE       15802\n",
       "MIN_CLOSE        15802\n",
       "DATA_CLOSE       15802\n",
       "STI              15774\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show how many registres has in each column\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>EQUIP_ID</th>\n",
       "      <th>OPEN_YEAR_4</th>\n",
       "      <th>OPEN_MONTH_4</th>\n",
       "      <th>OPEN_DAY_4</th>\n",
       "      <th>HOUR_OPEN</th>\n",
       "      <th>MIN_OPEN</th>\n",
       "      <th>CLOSE_YEAR_4</th>\n",
       "      <th>CLOSE_MONTH_4</th>\n",
       "      <th>CLOSE_DAY_4</th>\n",
       "      <th>HOUR_CLOSE</th>\n",
       "      <th>MIN_CLOSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.580200e+04</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "      <td>15802.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.535997e+07</td>\n",
       "      <td>2.175990</td>\n",
       "      <td>2.384951</td>\n",
       "      <td>2.667384</td>\n",
       "      <td>12.067397</td>\n",
       "      <td>29.576699</td>\n",
       "      <td>1.736742</td>\n",
       "      <td>2.093532</td>\n",
       "      <td>2.304898</td>\n",
       "      <td>10.966523</td>\n",
       "      <td>25.963802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.321736e+08</td>\n",
       "      <td>1.002167</td>\n",
       "      <td>1.131484</td>\n",
       "      <td>1.134959</td>\n",
       "      <td>2.984860</td>\n",
       "      <td>17.241296</td>\n",
       "      <td>1.282926</td>\n",
       "      <td>1.496906</td>\n",
       "      <td>1.547816</td>\n",
       "      <td>4.987646</td>\n",
       "      <td>18.575372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9.999990e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-9.999990e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.943000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.424095e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.147484e+09</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           EQUIP_ID   OPEN_YEAR_4  OPEN_MONTH_4    OPEN_DAY_4     HOUR_OPEN  \\\n",
       "count  1.580200e+04  15802.000000  15802.000000  15802.000000  15802.000000   \n",
       "mean   2.535997e+07      2.175990      2.384951      2.667384     12.067397   \n",
       "std    2.321736e+08      1.002167      1.131484      1.134959      2.984860   \n",
       "min   -9.999990e+05      1.000000      1.000000      1.000000      0.000000   \n",
       "25%   -9.999990e+05      1.000000      1.000000      2.000000      9.000000   \n",
       "50%    9.943000e+03      2.000000      2.000000      3.000000     11.000000   \n",
       "75%    1.424095e+05      3.000000      3.000000      4.000000     15.000000   \n",
       "max    2.147484e+09      4.000000      4.000000      4.000000     23.000000   \n",
       "\n",
       "           MIN_OPEN  CLOSE_YEAR_4  CLOSE_MONTH_4   CLOSE_DAY_4    HOUR_CLOSE  \\\n",
       "count  15802.000000  15802.000000   15802.000000  15802.000000  15802.000000   \n",
       "mean      29.576699      1.736742       2.093532      2.304898     10.966523   \n",
       "std       17.241296      1.282926       1.496906      1.547816      4.987646   \n",
       "min        0.000000     -1.000000      -1.000000     -1.000000     -1.000000   \n",
       "25%       15.000000      1.000000       1.000000      1.000000      9.000000   \n",
       "50%       30.000000      2.000000       2.000000      3.000000     11.000000   \n",
       "75%       44.000000      2.000000       3.000000      4.000000     15.000000   \n",
       "max       59.000000      4.000000       4.000000      4.000000     23.000000   \n",
       "\n",
       "          MIN_CLOSE  \n",
       "count  15802.000000  \n",
       "mean      25.963802  \n",
       "std       18.575372  \n",
       "min       -1.000000  \n",
       "25%        9.000000  \n",
       "50%       26.000000  \n",
       "75%       42.000000  \n",
       "max       59.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describer the numerics collums\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#show the type that pandas indentified to each collums\n",
    "# df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_open = []\n",
    "months_open = []\n",
    "days_open = []\n",
    "\n",
    "years_close = []\n",
    "months_close = []\n",
    "days_close = []\n",
    "for D_open, D_close, H_close in df[['DATA_OPEN','DATA_CLOSE','HOUR_CLOSE']].values.tolist():\n",
    "    dtO = datetime.strptime(D_open, '%d/%m/%Y')\n",
    "    years_open.append(dtO.year)\n",
    "    months_open.append(dtO.month)\n",
    "    days_open.append(dtO.day)\n",
    "    if H_close == -1:\n",
    "        years_close.append('NaN')\n",
    "        months_close.append('NaN')\n",
    "        days_close.append('NaN')\n",
    "    else:\n",
    "        dtC = datetime.strptime(D_close, '%d/%m/%Y')\n",
    "        years_close.append(dtC.year)\n",
    "        months_close.append(dtC.month)\n",
    "        days_close.append(dtC.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['OPEN_YEAR_1'] = years_open\n",
    "df['OPEN_MONTH_1'] = months_open\n",
    "df['OPEN_DAY_1'] = days_open\n",
    "\n",
    "df['CLOSE_YEAR_1'] = years_close\n",
    "df['CLOSE_MONTH_1'] = months_close\n",
    "df['CLOSE_DAY_1'] = days_close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4492\n",
       "2    6334\n",
       "3    2679\n",
       "4    2297\n",
       "Name: OPEN_YEAR_4, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many problems were opens in each year_4\n",
    "df['OPEN_YEAR_4'].value_counts(sort=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ae20246d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#show the graphic bars to above result \n",
    "df['OPEN_YEAR_4'].value_counts().sort_index().plot.bar()\n",
    "sns.countplot(df['OPEN_YEAR_4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    3708\n",
       "2012    3680\n",
       "2014    2626\n",
       "2015    1567\n",
       "2017    1165\n",
       "2016    1112\n",
       "2011     812\n",
       "2018     772\n",
       "2019     360\n",
       "Name: OPEN_YEAR_1, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many problems were opens in each year\n",
    "df['OPEN_YEAR_1'].value_counts(sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "# sns.countplot(df['OPEN_YEAR_1'])\n",
    "plt.title(\"Volume de cadastro de ocorrências por ano\")\n",
    "plt.xlabel(\"Ano\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")\n",
    "df['OPEN_YEAR_1'].value_counts().sort_index().plot.bar()\n",
    "plt.legend()\n",
    "plt.savefig('foo1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apartir de 2015 o gráfico mostra um redução e estabilização. Antes disso o número de ocorrências eram muito acima dos valores atuais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4659\n",
       "2    3986\n",
       "4    3585\n",
       "3    3572\n",
       "Name: OPEN_MONTH_4, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many problems were opens in each months\n",
    "df['OPEN_MONTH_4'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ae1632650>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# show the graphic bars to above result\n",
    "# df['D_MOTH_OPEN'].apply(lambda x: 0 if x == 99 else x).value_counts().sort_index().plot.bar()\n",
    "df['OPEN_MONTH_4'].value_counts().sort_index().plot.bar()\n",
    "sns.countplot(df['OPEN_MONTH_4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     1748\n",
       "11    1592\n",
       "1     1487\n",
       "4     1462\n",
       "2     1424\n",
       "5     1411\n",
       "9     1244\n",
       "8     1239\n",
       "6     1113\n",
       "7     1089\n",
       "10    1064\n",
       "12     929\n",
       "Name: OPEN_MONTH_1, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many problems were opens in each months\n",
    "df['OPEN_MONTH_1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ae15c02d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['OPEN_MONTH_1'].value_counts().sort_index().plot.bar()\n",
    "sns.countplot(df['OPEN_MONTH_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ae14f3290>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAS/UlEQVR4nO3dfZBld13n8feHJFBCUuSpM+SpGR9iJLgkYNcEN7tbQUwchkjUoiTZLcy6wfGBlFJapVG3zBZWUbEstXTjmhrJFMHCgCiBsRIgIz5ELB4yEycwYRITssEMEzIDExMiVrmD3/3jnvFemtszPff09O3+9ftVdavP+Z3fuefX30k+9/Tv3ntOqgpJUrueN+0BSJKOL4Nekhpn0EtS4wx6SWqcQS9JjTPoJalxJ057AOOceeaZtX79+mkPQ5JWjZ07d365qmbGbVuRQb9+/Xp27Ngx7WFI0qqR5AsLbXPqRpIaZ9BLUuMMeklqnEEvSY0z6CWpcUcN+iTnJ/mrJHuSPJjk57r205NsT/JI9/O0Bfa/ruvzSJLrlvoXkCQd2WLO6A8Bv1BVLwNeDbw1yUXAjcDHquoC4GPd+jdIcjpwE3ApsAG4aaEXBEnS8XHUoK+qJ6vq/m75q8Ae4FzgauD2rtvtwA+N2f0HgO1VdbCqnga2AxuXYuCSpMU5pi9MJVkPvBL4FLCuqp6EwYtBkrPG7HIu8MTI+t6ubdxzbwY2A8zOzh7LsL7J+hvv6rX/Unn85tdPewiStPg3Y5OcDPwZ8Laqenaxu41pG3tLq6raUlVzVTU3MzP2W7ySpAksKuiTnMQg5N9TVR/omp9Kcna3/Wxg/5hd9wLnj6yfB+ybfLiSpGO1mE/dBLgN2FNVvz2yaRtw+FM01wEfGrP7R4Erk5zWvQl7ZdcmSVomizmjvwx4M/B9SXZ1j03AzcAVSR4BrujWSTKX5J0AVXUQ+HXgvu7x9q5NkrRMjvpmbFV9nPFz7QCvHdN/B/CWkfWtwNZJByhJ6sdvxkpS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGnfUG48k2QpcBeyvqu/u2t4HXNh1ORX4p6q6ZMy+jwNfBb4OHKqquSUatyRpkY4a9MC7gFuAdx9uqKo3HV5O8lvAM0fY/zVV9eVJByhJ6mcxtxK8N8n6cdu6G4f/KPB9SzssSdJS6TtH/5+Bp6rqkQW2F3BPkp1JNvc8liRpAouZujmSa4E7jrD9sqral+QsYHuSh6rq3nEduxeCzQCzs7M9hyVJOmziM/okJwI/ArxvoT5Vta/7uR+4E9hwhL5bqmququZmZmYmHZYkaZ4+UzffDzxUVXvHbUzyoiSnHF4GrgR29zieJGkCRw36JHcAnwAuTLI3yfXdpmuYN22T5Jwkd3er64CPJ3kA+DRwV1V9ZOmGLklajMV86ubaBdr/+5i2fcCmbvkx4OKe45Mk9eQ3YyWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxi7mV4NYk+5PsHmn7X0m+mGRX99i0wL4bkzyc5NEkNy7lwCVJi7OYM/p3ARvHtP9OVV3SPe6evzHJCcDvA68DLgKuTXJRn8FKko7dUYO+qu4FDk7w3BuAR6vqsar6V+C9wNUTPI8kqYc+c/Q3JPlMN7Vz2pjt5wJPjKzv7dokScto0qD/A+DbgUuAJ4HfGtMnY9pqoSdMsjnJjiQ7Dhw4MOGwJEnzTRT0VfVUVX29qv4N+EMG0zTz7QXOH1k/D9h3hOfcUlVzVTU3MzMzybAkSWNMFPRJzh5Z/WFg95hu9wEXJPnWJM8HrgG2TXI8SdLkTjxahyR3AJcDZybZC9wEXJ7kEgZTMY8DP9n1PQd4Z1VtqqpDSW4APgqcAGytqgePy28hSVrQUYO+qq4d03zbAn33AZtG1u8Gvumjl5Kk5eM3YyWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxRw36JFuT7E+ye6TtN5M8lOQzSe5McuoC+z6e5LNJdiXZsZQDlyQtzmLO6N8FbJzXth347qp6BfAPwC8fYf/XVNUlVTU32RAlSX0cNeir6l7g4Ly2e6rqULf6SeC84zA2SdISWIo5+v8BfHiBbQXck2Rnks1LcCxJ0jE6sc/OSX4VOAS8Z4Eul1XVviRnAduTPNT9hTDuuTYDmwFmZ2f7DEuSNGLiM/ok1wFXAf+tqmpcn6ra1/3cD9wJbFjo+apqS1XNVdXczMzMpMOSJM0zUdAn2Qj8EvCGqvraAn1elOSUw8vAlcDucX0lScfPYj5eeQfwCeDCJHuTXA/cApzCYDpmV5Jbu77nJLm723Ud8PEkDwCfBu6qqo8cl99CkrSgo87RV9W1Y5pvW6DvPmBTt/wYcHGv0UmSeuv1ZqxWvvU33jXtIQDw+M2vn/YQpDXLSyBIUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapyXQNCa4eUgtFZ5Ri9JjTPoJalxBr0kNc6gl6TGGfSS1LhFBX2SrUn2J9k90nZ6ku1JHul+nrbAvtd1fR7pbiguSVpGiz2jfxewcV7bjcDHquoC4GPd+jdIcjpwE3ApsAG4aaEXBEnS8bGooK+qe4GD85qvBm7vlm8HfmjMrj8AbK+qg1X1NLCdb37BkCQdR33m6NdV1ZMA3c+zxvQ5F3hiZH1v1yZJWibH+83YjGmrsR2TzUl2JNlx4MCB4zwsSVo7+gT9U0nOBuh+7h/TZy9w/sj6ecC+cU9WVVuqaq6q5mZmZnoMS5I0qk/QbwMOf4rmOuBDY/p8FLgyyWndm7BXdm2SpGWy2I9X3gF8Argwyd4k1wM3A1ckeQS4olsnyVySdwJU1UHg14H7usfbuzZJ0jJZ1NUrq+raBTa9dkzfHcBbRta3AlsnGp0kqTe/GStJjTPoJalxBr0kNc6gl6TGGfSS1DjvGSutQd4/d23xjF6SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrcxEGf5MIku0YezyZ527w+lyd5ZqTPr/UfsiTpWEx8UbOqehi4BCDJCcAXgTvHdP3bqrpq0uNIkvpZqqmb1wKfr6ovLNHzSZKWyFIF/TXAHQts+94kDyT5cJKXL9HxJEmL1DvokzwfeAPw/jGb7wdeWlUXA/8b+OARnmdzkh1Jdhw4cKDvsCRJnaU4o38dcH9VPTV/Q1U9W1XPdct3AyclOXPck1TVlqqaq6q5mZmZJRiWJAmWJuivZYFpmyQvSZJueUN3vK8swTElSYvU61aCSV4IXAH85EjbTwFU1a3AG4GfTnII+BfgmqqqPseUJB2bXkFfVV8DzpjXduvI8i3ALX2OIUnqx2/GSlLjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuN6B32Sx5N8NsmuJDvGbE+S30vyaJLPJHlV32NKkhav160ER7ymqr68wLbXARd0j0uBP+h+SpKWwXJM3VwNvLsGPgmcmuTsZTiuJImlCfoC7kmyM8nmMdvPBZ4YWd/btUmSlsFSTN1cVlX7kpwFbE/yUFXdO7I9Y/ap+Q3di8RmgNnZ2SUYliQJluCMvqr2dT/3A3cCG+Z12QucP7J+HrBvzPNsqaq5qpqbmZnpOyxJUqdX0Cd5UZJTDi8DVwK753XbBvxY9+mbVwPPVNWTfY4rSVq8vlM364A7kxx+rj+uqo8k+SmAqroVuBvYBDwKfA348Z7HlCQdg15BX1WPARePab91ZLmAt/Y5jiRpcn4zVpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekho3cdAnOT/JXyXZk+TBJD83ps/lSZ5Jsqt7/Fq/4UqSjlWfWwkeAn6hqu7vbhC+M8n2qvrcvH5/W1VX9TiOJKmHic/oq+rJqrq/W/4qsAc4d6kGJklaGksyR59kPfBK4FNjNn9vkgeSfDjJy5fieJKkxeszdQNAkpOBPwPeVlXPztt8P/DSqnouySbgg8AFCzzPZmAzwOzsbN9hSZI6vc7ok5zEIOTfU1UfmL+9qp6tque65buBk5KcOe65qmpLVc1V1dzMzEyfYUmSRvT51E2A24A9VfXbC/R5SdePJBu6431l0mNKko5dn6mby4A3A59Nsqtr+xVgFqCqbgXeCPx0kkPAvwDXVFX1OKYk6RhNHPRV9XEgR+lzC3DLpMeQJPXX+81YSVrN1t9417SHAMDjN7/+uD23l0CQpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDWu783BNyZ5OMmjSW4cs/0FSd7Xbf9UkvV9jidJOnZ9bg5+AvD7wOuAi4Brk1w0r9v1wNNV9R3A7wC/MenxJEmT6XNGvwF4tKoeq6p/Bd4LXD2vz9XA7d3ynwKvTXLE+8xKkpZWqmqyHZM3Ahur6i3d+puBS6vqhpE+u7s+e7v1z3d9vjzm+TYDm7vVC4GHJxrY0jkT+KZxrlHWYshaDFmLoZVQi5dW1cy4DX1uDj7uzHz+q8Zi+gwaq7YAW3qMZ0kl2VFVc9Mex0pgLYasxZC1GFrptegzdbMXOH9k/Txg30J9kpwIvBg42OOYkqRj1Cfo7wMuSPKtSZ4PXANsm9dnG3Bdt/xG4C9r0rkiSdJEJp66qapDSW4APgqcAGytqgeTvB3YUVXbgNuAP0ryKIMz+WuWYtDLZMVMI60A1mLIWgxZi6EVXYuJ34yVJK0OfjNWkhpn0EtS4wx6SWqcQS9JjevzhalmJflPDC7xsLuq7pn2eJZTkkuBPVX1bJJvAW4EXgV8DnhHVT0z1QEuoyQ/C9xZVU9MeyzTNvIR6n1V9RdJ/ivwH4E9wJaq+n9THeAyS/LtwA8z+J7QIeAR4I6V+v+Hn7oBkny6qjZ0yz8BvBW4E7gS+POqunma41tOSR4ELu4+PrsF+BrddYq69h+Z6gCXUZJngH8GPg/cAby/qg5Md1TTkeQ9DE4MXwj8E3Ay8AEG/12kqq47wu5N6U4AfhD4G2ATsAt4mkHw/0xV/fX0RjeeQQ8k+fuqemW3fB+wqaoOJHkR8Mmq+g/THeHySbKnql7WLd9fVa8a2barqi6Z3uiWV5K/B74H+H7gTcAbgJ0MQv8DVfXVKQ5vWSX5TFW9ovuG+xeBc6rq691FCh+oqldMeYjLJslngUu63/+FwN1VdXmSWeBDh7NkJXGOfuB5SU5LcgaDF78DAFX1zwz+LFtLdif58W75gSRzAEm+E1hTf54DVVX/VlX3VNX1wDnA/wE2Ao9Nd2jL7nnd9M0pDM7qX9y1vwA4aWqjmp7D094vYFATquofWaG1cI5+4MUMztQCVJKXVNWXkpzM+AuztewtwO8m+Z8Mrsb3iSRPAE9029aSb/i37+ahtwHbuvcv1pLbgIcYfAv+V4H3J3kMeDWDS5SvJe8E7kvySeC/0N1nI8kMK/RaXk7dHEH3Z9m6qvq/0x7LcktyCvBtDE4G9lbVU1Me0rJL8p1V9Q/THsdKkeQcgKral+RUBlNa/1hVn57uyJZfkpcDL2PwgY2Hpj2eozHojyLJyVX13LTHsRJYiyFrMWQthlZqLZyjP7rPTXsAK4i1GLIWQ9ZiaEXWwjl6IMnPL7SJwcfI1gxrMWQthqzF0GqshWf0A+8ATmPw7vno42TWXo2sxZC1GLIWQ6uuFp7RD9wPfLCqds7fkGStfdLEWgxZiyFrMbTqauGbsUCSC4GD4771mGTdWvrEibUYshZD1mJoNdbCoJekxq3I+aTlluTFSW5O8lCSr3SPPV3bqdMe33KyFkPWYshaDK3GWhj0A3/C4KJEl1fVGVV1BvCaru39Ux3Z8rMWQ9ZiyFoMrbpaOHUDJHm4qi481m0tshZD1mLIWgytxlp4Rj/whSS/mGTd4YYk65L8EoNrvKwl1mLIWgxZi6FVVwuDfuBNwBnA3yR5OslB4K+B04EfnebApsBaDFmLIWsxtOpq4dRNJ8l3AecxuP78cyPtG6vqI9Mb2fKzFkPWYshaDK22WnhGz7/fMeZDwA0Mrsd+9cjmd0xnVNNhLYasxZC1GFqNtfCbsQM/AXxPVT2XZD3wp0nWV9XvsvauR28thqzFkLUYWnW1MOgHTjj851dVPZ7kcgb/eC9lhf7DHUfWYshaDFmLoVVXC6duBr6U5N/vhdr9I14FnAmsmfvFdqzFkLUYshZDq64WvhkLJDkPOFRVXxqz7bKq+rspDGsqrMWQtRiyFkOrsRYGvSQ1zqkbSWqcQS9JjTPoJalxBr0kNc6gl6TG/X+9saGRuc15aAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'hd')]['OPEN_YEAR_1'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ae1462850>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAN4klEQVR4nO3dfazdBX3H8fdHKk7A8VCunVDnxQVRt/nYIJPFMDALChO26HRbTGPQ/jGdbi7R7iHxPwLJMucf25JGtvQP4gMMBwuL0zEx2TLRW8QhFAerWMqDXAeouD+U+d0f53S/Wm7paXvvPfd77vuVNL3nqffbb+HdX3/3nHNTVUiS+nnWtAeQJB0dAy5JTRlwSWrKgEtSUwZckpoy4JLU1IbV/GSnn356zc/Pr+anlKT2du3a9Z2qmjv4+lUN+Pz8PAsLC6v5KSWpvSTfWup6T6FIUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWpqVV/Isxzmt9887RG4/6pLpj2CJHkELkldGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaavdmVhr4xl4ja2EPsDZ2ofXFI3BJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJampiQKe5A+S3JXk60k+keSnkpyV5LYk9yb5VJLjV3pYSdLgsAFPcibwfmBLVf0CcBzwDuBq4KNVdTbwOHDFSg4qSfpJk55C2QA8N8kG4ATgYeBC4Prx7TuBy5d/PEnSoRw24FX1IPBnwF5G4f4usAt4oqqeGt9tH3DmSg0pSXq6SU6hnApcBpwFnAGcCLxpibvWIR6/LclCkoXFxcVjmVWSdIBJTqG8EfhmVS1W1Y+AG4DXA6eMT6kAbAYeWurBVbWjqrZU1Za5ubllGVqSNFnA9wLnJTkhSYCLgLuBLwBvHd9nK3DjyowoSVrKJOfAb2P0xcrbgTvHj9kBfBj4YJL7gI3ANSs4pyTpIBsOfxeoqo8AHzno6j3Aucs+kSRpIr4SU5KaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpqYm+K72kHua33zztEQC4/6pLpj3CuuARuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMTBTzJKUmuT3JPkt1JfinJaUk+n+Te8c+nrvSwkqTBpEfgHwM+W1UvBV4J7Aa2A7dU1dnALePLkqRVctiAJ/lp4A3ANQBV9cOqegK4DNg5vttO4PKVGlKS9HSTHIG/GFgE/jbJV5N8PMmJwKaqehhg/PPzV3BOSdJBJgn4BuA1wF9X1auBH3AEp0uSbEuykGRhcXHxKMeUJB1skoDvA/ZV1W3jy9czCvq3k7wAYPzzo0s9uKp2VNWWqtoyNze3HDNLkpgg4FX1CPBAknPGV10E3A3cBGwdX7cVuHFFJpQkLWnSb2r8e8C1SY4H9gDvYhT/Tye5AtgLvG1lRpQkLWWigFfVHcCWJW66aHnHkSRNyldiSlJTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKa2jDpHZMcBywAD1bVpUnOAj4JnAbcDryzqn64MmNK0pGZ337ztEcA4P6rLlmxX/tIjsA/AOw+4PLVwEer6mzgceCK5RxMkvTMJgp4ks3AJcDHx5cDXAhcP77LTuDylRhQkrS0SY/A/wL4EPDj8eWNwBNV9dT48j7gzGWeTZL0DA4b8CSXAo9W1a4Dr17irnWIx29LspBkYXFx8SjHlCQdbJIj8POBtyS5n9EXLS9kdER+SpL9XwTdDDy01IOrakdVbamqLXNzc8swsiQJJgh4Vf1RVW2uqnngHcC/VNXvAF8A3jq+21bgxhWbUpL0NMfyPPAPAx9Mch+jc+LXLM9IkqRJTPw8cICquhW4dfzxHuDc5R9JkjQJX4kpSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSU4cNeJIXJvlCkt1J7krygfH1pyX5fJJ7xz+fuvLjSpL2m+QI/CngD6vqZcB5wHuTvBzYDtxSVWcDt4wvS5JWyWEDXlUPV9Xt44+/D+wGzgQuA3aO77YTuHylhpQkPd0RnQNPMg+8GrgN2FRVD8Mo8sDzl3s4SdKhTRzwJCcBfwf8flV97wgety3JQpKFxcXFo5lRkrSEiQKe5NmM4n1tVd0wvvrbSV4wvv0FwKNLPbaqdlTVlqraMjc3txwzS5KY7FkoAa4BdlfVnx9w003A1vHHW4Ebl388SdKhbJjgPucD7wTuTHLH+Lo/Bq4CPp3kCmAv8LaVGVGStJTDBryq/hXIIW6+aHnHkSRNyldiSlJTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1NQxBTzJxUm+keS+JNuXayhJ0uEddcCTHAf8JfAm4OXAbyV5+XINJkl6ZsdyBH4ucF9V7amqHwKfBC5bnrEkSYeTqjq6ByZvBS6uqnePL78TeF1Vve+g+20Dto0vngN84+jHXRanA9+Z8gxrhbsYuIuBuxislV28qKrmDr5ywzH8glniuqf9bVBVO4Adx/B5llWSharaMu051gJ3MXAXA3cxWOu7OJZTKPuAFx5weTPw0LGNI0ma1LEE/CvA2UnOSnI88A7gpuUZS5J0OEd9CqWqnkryPuCfgOOAv6mqu5ZtspWzZk7nrAHuYuAuBu5isKZ3cdRfxJQkTZevxJSkpgy4JDVlwCWpKQMuSU0dywt52knyy4zeAuDrVfW5ac+zmpK8H/hMVT0w7VmmLcnrgN1V9b0kzwW2A68B7gaurKrvTnXAVXTAU4Afqqp/TvLbwOuB3cCOqvrRVAdcZUl+Dvh1Rq9xeQq4F/jEWv1vYqafhZLky1V17vjj9wDvBT4D/CrwD1V11TTnW01Jvgv8APgv4BPAdVW1ON2ppiPJXcArx0+F3QH8D3A9cNH4+t+Y6oCrKMm1jA7kTgCeAE4CbmC0i1TV1imOt6rGBzm/BnwReDNwB/A4o6D/blXdOr3pljbrAf9qVb16/PFXgDdX1WKSE4EvVdUvTnfC1ZPkq8BrgTcCbwfeAuxiFPMbqur7UxxvVSXZXVUvG398e1W95oDb7qiqV01vutWV5D+q6hVJNgAPAmdU1f8mCfC1qnrFlEdcNUnuBF41/v2fAPxjVV2Q5GeBG/e3ZC2Z9XPgz0pyapKNjP6yWgSoqh8w+ufRelJV9eOq+lxVXQGcAfwVcDGwZ7qjrbqvJ3nX+OOvJdkCkOQlwLo6ZcDo/5HjgecxOgo/eXz9c4BnT22q6dl/Wvk5jHZCVe1lje5i1s+Bn8zoKDNAJfmZqnokyUks/WZcs+wnfr/jc5s3ATeNzwOvJ+8GPpbkTxm909y/J3kAeGB823pyDXAPo1dT/wlwXZI9wHmM3iJ6Pfk48JUkXwLeAFwNkGQOeGyagx3KTJ9COZTxP482VdU3pz3Laknykqr6z2nPsZYkeR7wYkYHMvuq6ttTHmkqkpwBUFUPJTmF0Wm2vVX15elOtvqS/DzwMkZPdLhn2vMczroMOECSk6rqyWnPsRa4i4G7GLiLwVrdxayfA38md097gDXEXQzcxcBdDNbkLmb6HHiSDx7qJkZPl1o33MXAXQzcxaDjLmb9CPxK4FRGX00+8MdJzP7v/WDuYuAuBu5i0G4XM30EDtwO/H1V7Tr4hiTr7dkG7mLgLgbuYtBuFzP9Rcwk5wCPLfWKwySb1tOzDtzFwF0M3MWg4y5mOuCSNMvW5Hmd5ZLk5CRXJbknyX+Pf+weX3fKtOdbTe5i4C4G7mLQcRczHXDg04zejOaCqtpYVRuBXxlfd91UJ1t97mLgLgbuYtBuFzN9CiXJN6rqnCO9bRa5i4G7GLiLQcddzPoR+LeSfCjJpv1XJNmU5MOM3vdiPXEXA3cxcBeDdruY9YC/HdgIfDHJ40keA24FTgN+c5qDTYG7GLiLgbsYtNvFTJ9CAUjyUmAzo/f/fvKA6y+uqs9Ob7LV5y4G7mLgLgbddjHTR+AZfYeNG4H3MXoP6MsOuPnK6Uw1He5i4C4G7mLQcRez/krM9wCvraonk8wD1yeZr6qPsf7eD9xdDNzFwF0M2u1i1gN+3P5/BlXV/UkuYPSH8iLW6B/ICnIXA3cxcBeDdruY6VMowCNJ/v/7G47/cC4FTgfWzffDHHMXA3cxcBeDdruY6S9iJtkMPFVVjyxx2/lV9W9TGGsq3MXAXQzcxaDjLmY64JI0y2b9FIokzSwDLklNGXBJasqAS1JTBlySmvo/gfyRoGMiJ7YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'fonte')]['OPEN_YEAR_1'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Setor')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df[(df['OPEN_YEAR_1'] >= 2015) \n",
    "   & (df['HAS_VALUE'] == 'P') \n",
    "   & (df['EQUIP'] == 'nobreak')]['OPEN_YEAR_1'].value_counts().plot.bar()\n",
    "plt.title(\"Quantidade de problemas\")\n",
    "plt.xlabel(\"Ano\")\n",
    "plt.ylabel(\"Setor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ESPC\n",
       "fonte             383\n",
       "hd                 54\n",
       "nobreak           640\n",
       "placa             132\n",
       "placa de rede      13\n",
       "placa de video      3\n",
       "ram                61\n",
       "software           39\n",
       "Name: ESPC, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P')].groupby(['ESPC'])['ESPC'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EQUIP\n",
       "computador       739\n",
       "estabilizador      1\n",
       "impressora         2\n",
       "nobreak          639\n",
       "Name: EQUIP, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P')].groupby(['EQUIP'])['EQUIP'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min of troubles in hd since 2015:  2\n",
      "min of troubles in fonte since 2015:  42\n",
      "min of troubles in ram since 2015:  1\n",
      "min of troubles in placa since 2015:  13\n",
      "min of troubles in bateria since 2015:  nan\n",
      "min of troubles in placa de rede since 2015:  1\n",
      "min of troubles in placa de video since 2015:  1\n",
      "min of troubles in nobreak since 2015:  5\n",
      "min of troubles in cabo since 2015:  nan\n"
     ]
    }
   ],
   "source": [
    "print('min of troubles in hd since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'hd')]['OPEN_YEAR_1'].value_counts().min())\n",
    "print('min of troubles in fonte since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'fonte')]['OPEN_YEAR_1'].value_counts().min())\n",
    "print('min of troubles in ram since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'ram')]['OPEN_YEAR_1'].value_counts().min())\n",
    "print('min of troubles in placa since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'placa')]['OPEN_YEAR_1'].value_counts().min())\n",
    "print('min of troubles in bateria since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'bateria')]['OPEN_YEAR_1'].value_counts().min())\n",
    "print('min of troubles in placa de rede since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'placa de rede')]['OPEN_YEAR_1'].value_counts().min())\n",
    "print('min of troubles in placa de video since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'placa de video')]['OPEN_YEAR_1'].value_counts().min())\n",
    "print('min of troubles in nobreak since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['EQUIP'] == 'nobreak')]['OPEN_YEAR_1'].value_counts().min())\n",
    "print('min of troubles in cabo since 2015: ', df[(df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P') & (df['ESPC'] == 'cabo')]['OPEN_YEAR_1'].value_counts().min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    5133\n",
       "2    3809\n",
       "3    3570\n",
       "1    3290\n",
       "Name: OPEN_DAY_4, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many problems were opens in each days\n",
    "df['OPEN_DAY_4'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ae1432390>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#show the graphic bars to above result \n",
    "df['OPEN_DAY_4'].value_counts().sort_index().plot.bar()\n",
    "sns.countplot(df['OPEN_DAY_4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ae13beb50>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#show the graphic bars to above result \n",
    "df['OPEN_DAY_1'].value_counts().sort_index().plot.bar()\n",
    "sns.countplot(df['OPEN_DAY_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = df['HAS_VALUE'].value_counts()\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências precificáveis por horário\")\n",
    "plt.xlabel(\"Tipo de Ocorrência\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.01012530059486"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((8105+2958)*100)/15802"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PERCENT OF MISSING PRICING VALUES IN ALL DATASET\n",
    "(df['HAS_VALUE'].isna().sum() / df['DESCRIB'].count()) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show the graphic bars to above result \n",
    "ndf = round(df[((df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015))]['HOUR_OPEN'].value_counts().sort_index()/5/12)\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências precificáveis por horário\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show the graphic bars to above result \n",
    "ndf = round(df[((df['HAS_VALUE'] != 'P') & (df['OPEN_YEAR_1'] >= 2015))]['HOUR_OPEN'].value_counts().sort_index()/5/12)\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências não precificáveis por horário\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#show the graphic bars to above result \n",
    "ndf = round(df[(df['OPEN_YEAR_1'] >= 2015)]['HOUR_OPEN'].value_counts().sort_index()/5/12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZF7PvvvvSrVs3dtllFz772c+yZMmSrZapqqpi1aq/3xZv9erVDBgwgH322YeNGzeyZcuWVuXlHJeZWSlx4mXWxaxdu7Zl+q677mL48OFbLTNq1CgaGhpYsWIFmzdvpra2lpqaGiRxwgkncMcddwAwd+5cTjvttLKOy8yspLR3umMpPXw5CdtZTZo0Kd7//vdHRUVF7LfffvHjH/84zj777Bg+fHh84AMfiE984hOxZs2aiIhobGyMk08+ueW599xzTwwePDgOOuig+OY3v9lS/pe//CVGjRoVBx98cEyYMCHefvvtsonLzKwU0MHlJLrELYNGjhwZS5cuLXYYZmZmZtsk6dGIGJmtzl2NZmZmZgXixMvMzMysQJx4mZmZmRVIl7hJtpklBl1yT6eta+WVp3TKekoxJjOzUuUWLzMzM7MCceJlZmZmViBOvMzMzMwKxImXmZmZWYE48TIzMzMrECdeZmZmZgXixMvMzMysQJx4mZmZmRWIEy8zMzOzAnHiZWZmZlYgTrzMzMzMCsSJl5mZmVmBOPEyMzMzK5C8JV6SbpL0oqQns9R9WVJI2idf2zczMzMrNfls8boFOKltoaSBwFjg+Txu28zMzKzk5C3xiogHgZezVH0f+AoQ+dq2mZmZWSkq6BgvSTVAY0T8IYdlz5e0VNLSdevWFSA6MzMzs/wqWOIlqSdwKTAzl+Uj4oaIGBkRI/v27Zvf4MzMzMwKoJAtXgcDBwJ/kLQSqAKWSXp/AWMwMzMzK5qKQm0oIp4A+jXPp8nXyIh4qVAxmJmZmRVTPi8nMQ94GBgiabWkafnalpmZmVlXkLcWr4g4axv1g/K1bTMzM7NS5CvXm5mZmRWIEy8zMzOzAnHiZWZmZlYgTrzMzMzMCsSJl5mZmVmBOPEyMzMzKxAnXmZmZmYF4sTLzMzMrECceJmZmZkViBMvMzMzswJx4mVmZmZWIE68zMzMzArEiZeZmZlZgTjxMjMzMysQJ15mZmZmBeLEy8zMzKxAnHiZmZmZFYgTLzMzsyymTp1Kv379GD58eEvZv/7rv3LooYcyYsQITj/9dDZu3Jj1ufX19QwZMoTq6mquvPLKlvIVK1YwevRoBg8ezJlnnsnmzZvzvh9WWpx4mZmZZTFlyhTq6+tblY0dO5Ynn3ySP/7xjxxyyCFcccUVWz2vqamJCy+8kPnz57N8+XLmzZvH8uXLAZgxYwYXX3wxDQ0N9OnThzlz5hRkX6x0OPEyMzPL4rjjjqOysrJV2cc//nEqKioAGDNmDKtXr97qeUuWLKG6upqDDjqI7t27M2nSJOrq6ogI7r//fiZMmADA5MmT+fnPf57/HbGS4sTLzMxsO9x0002cfPLJW5U3NjYycODAlvmqqioaGxtZv349vXv3bkncmstt5+LEy8zM7D361re+RUVFBZ/5zGe2qouIrcoktVtuO5eKYgdgZmbWlcydO5df/OIXLFq0KGviVFVVxapVq1rmV69ezYABA9hnn33YuHEjW7ZsoaKioqXcdi5u8TIzM8tRfX093/nOd7j77rvp2bNn1mVGjRpFQ0MDK1asYPPmzdTW1lJTU4MkTjjhBO644w4gSeBOO+20QoZvJcCJl5mZWRZnnXUWxxxzDM888wxVVVXMmTOHz3/+87z22muMHTuWI444gunTpwOwZs0axo8fD0BFRQXXXnst48aNY+jQoUycOJFhw4YB8J3vfIerrrqK6upq1q9fz7Rp04q2f1YcytbnXGpGjhwZS5cuLXYYZkU36JJ7Om1dK688pVPWU4oxmZkVk6RHI2Jktrq8tXhJuknSi5KezCj7D0l/kvRHSXdJ6p2v7ZuZmZmVmnx2Nd4CnNSm7D5geESMAP4MfDWP2zczMzMrKXlLvCLiQeDlNmULI2JLOvsIUJWv7ZuZmZmVmmJeTmIq8LP2KiWdD5wPsP/++xcqJjMzs614LKN1lqKc1SjpUmAL8N/tLRMRN0TEyIgY2bdv38IFZ2ZmZpYnBW/xkjQZOBX4aHSFUyrNzMzMOklBEy9JJwEzgI9ExJuF3LaZmZlZseXzchLzgIeBIZJWS5oGXAvsBdwn6XFJ1+Vr+2ZmZmalJp9nNZ4VEf0jYteIqIqIORFRHREDI+KI9DE9X9s3s53L1KlT6devH8OHD28pu/322xk2bBi77LILHV2Eub6+niFDhlBdXc2VV17ZUr5ixQpGjx7N4MGDOfPMM9m8eXNe98HMyt82Ey9JH5a0Rzp9tqSrJB2Q/9DMzHI3ZcoU6uvrW5UNHz6cO++8k+OOO67d5zU1NXHhhRcyf/58li9fzrx581i+fDkAM2bM4OKLL6ahoYE+ffowZ86cvO6DmZW/XFq8fgS8Kelw4CvAX4Fb8xqVmdl7dNxxx1FZWdmqbOjQoQwZMqTD5y1ZsoTq6moOOuggunfvzqRJk6irqyMiuP/++5kwYQIAkydP5uc//3ne4jeznUMuideW9OzD04BrIuIaknFaZmZdXmNjIwMHDmyZr6qqorGxkfXr19O7d28qKipalZuZ7Yhczmp8TdJXgbOB4yR1A3bNb1hmZoWR7ao2ktotNzPbEbm0eJ0JbAKmRcQLwH7Af+Q1KjOzAqmqqmLVqlUt86tXr2bAgAHss88+bNy4kS1btrQqNzPbEdtMvCLihYi4KiJ+k84/HxEe42VmZWHUqFE0NDSwYsUKNm/eTG1tLTU1NUjihBNO4I477gBg7ty5nHbaaUWO1sy6ulzOahwj6feSXpe0WVKTpFcKEZyZWa7OOussjjnmGJ555hmqqqqYM2cOd911F1VVVTz88MOccsopjBs3DoA1a9Ywfvx4ACoqKrj22msZN24cQ4cOZeLEiQwbNgyA73znO1x11VVUV1ezfv16pk2bVrT9M7PykMsYr2uBScDtwEjgXGBwPoMyM3uv5s2bl7X89NNP36pswIAB3HvvvS3z48ePb0nEMh100EEsWbKk84I0s51eTrcMiohnJXWLiCbgZkkP5TkuMzMzs7KTS+L1pqTuwOOSvgusBfbIb1hmZmZm5SeXsxrPAboBnwfeAAYCZ+QzKDMzM7NytM0Wr4j4azr5FjArv+GYmXWOQZfc0ynrWXnlKZ2yHjMz6CDxknRbREyU9ASw1ZUEI2JEXiMzMzMzKzMdtXh9If17aiECMTMzMyt37SZeEbE2ndwFWBsRbwNI2h3YtwCxmZmZmZWVXAbX3w68mzHflJaZmZmZ2XuQS+JVERGbm2fS6e75C8nMzMysPOWSeK2TVNM8I+k04KX8hWRmZmZWnnK5gOp04L8lXQsIWEVy2yAzMzMzew9yuY7XX4AxkvYEFBGv5T8sMzMzs/KzzcRLUg+SK9UPAiokARAR38hrZGZmZmZlJpeuxjrgFeBRYFN+wzEzMzMrX7kkXlURcVLeIzEzMzMrc7mc1fiQpA/kPRIzMzOzMpdLi9exwBRJK0i6GgWE79VoZmZm9t7kknidvD0rlnQTyX0eX4yI4WlZJfAzkoH6K4GJEbFhe9ZvZmZm1tVss6sxIv4KDAROTKffzOV5wC1A27FhlwCLImIwsCidNzMzM9sptJtASTo0/Xs5MAP4alq1K/DTba04Ih4EXm5TfBowN52eC3zyPcZrZmZm1mVlTbwknQhMS2dPB2qANwAiYg2w13Zub9+IWJuuZy3Qr70FJZ0vaamkpevWrdvOzZmZmZmVjvZavHYjTbSAzRERQABI2qMQgUXEDRExMiJG9u3btxCbNDMzM8urrIlXRNwL/CKdvU3S9UBvSZ8FfgncuJ3b+5uk/gDp3xe3cz1mZmZmXU67ZzVGxNL0739KGgu8CgwBZkbEfdu5vbuBycCV6d+67VyPmZmZWZfT4eUkJHUDFkTEx4D3lGxJmgccD+wjaTVwOUnCdZukacDzwKe3J2gzMzOzrqjDxCsimiS9KalXRLzyXlYcEWe1U/XR97IeMzMzs3KRy/W43gaekDRH0n81P/IdmJmZmXXsmmuuYfjw4QwbNoyrr756q/qI4KKLLqK6upoRI0awbNmylrq5c+cyePBgBg8ezNy5c7d6ruVHLleuvyd9mJmZWYl48sknufHGG1myZAndu3fnpJNO4pRTTmHw4MEty8yfP5+GhgYaGhpYvHgxF1xwAYsXL+bll19m1qxZLF26FEkcddRR1NTU0KdPnyLu0c6hwxavdIzX2IiY2/ZRoPjMzMwsi6effpoxY8bQs2dPKioq+MhHPsJdd93Vapm6ujrOPfdcJDFmzBg2btzI2rVrWbBgAWPHjqWyspI+ffowduxY6uvri7QnO5cOE6+IaAL6SupeoHjMzMwsB8OHD+fBBx9k/fr1vPnmm9x7772sWrWq1TKNjY0MHDiwZb6qqorGxsZ2yy3/culqXAn8TtLd/P2iqkTEVfkKyszMzDo2dOhQZsyYwdixY9lzzz05/PDDqaho/bOeXP+8NUntllv+5TK4fg3JxVR3IblVUPPDzMzMimjatGksW7aMBx98kMrKylbjuyBpycpsBVu9ejUDBgxot9zyb5stXhExC0DSXslsvJ73qMzMzGybXnzxRfr168fzzz/PnXfeycMPP9yqvqamhmuvvZZJkyaxePFievXqRf/+/Rk3bhxf+9rX2LBhAwALFy7kiiuuKMYu7HS2mXhJGg78BKhM518Czo2Ip/Icm5mZmXXgjDPOYP369ey6667Mnj2bPn36cN111wEwffp0xo8fz7333kt1dTU9e/bk5ptvBqCyspLLLruMUaNGATBz5kwqKyuLth87k1zGeN0AfDEifgUg6XiSezV+KI9xmZmZ2Tb85je/2aps+vTpLdOSmD17dtbnTp06lalTp+YtNssulzFeezQnXQAR8QCwR94iMjMzMytTubR4PSfpMpLuRoCzgRX5C8nMzMysPOXS4jUV6AvcmT72Ac7LZ1BmZmZm5SiXsxo3ABcVIBYzMzN7DwZd0jl39Ft55Smdsh7btm22eEm6T1LvjPk+khbkNywzMzOz8pNLV+M+EbGxeSZtAeuXv5DMzMzMylMuide7kvZvnpF0ALD1vQbMzMzMrEO5nNV4KfBbSb9O548Dzs9fSGZmZmblaZstXhFRDxwJ/Cx9HBURHuNlZmZ58f3vf59hw4YxfPhwzjrrLN5+++1W9Zs2beLMM8+kurqa0aNHs3Llypa6K664gurqaoYMGcKCBf6pstKTS1cjJFepPz59jMlXMGZmtnNrbGzkv/7rv1i6dClPPvkkTU1N1NbWtlpmzpw59OnTh2effZaLL76YGTNmALB8+XJqa2t56qmnqK+v53Of+xxNTU3F2A2zduVyVuOVwBeA5enjC5J8J00zM8uLLVu28NZbb7FlyxbefPNNBgwY0Kq+rq6OyZMnAzBhwgQWLVpERFBXV8ekSZPo0aMHBx54INXV1SxZsqQYu2DWrlxavMYDYyPipoi4CTgJ8AU/zMys0+233358+ctfZv/996d///706tWLj3/8462WaWxsZODAgQBUVFTQq1cv1q9f36ocoKqqisbGxoLGb7YtuXY19s6Y7pWPQMzMzDZs2EBdXR0rVqxgzZo1vPHGG/z0pz9ttUzE1ifWS2q33KyU5JJ4XQE8JukWSXOBR4Fv5zcsMzPbGf3yl7/kwAMPpG/fvuy666586lOf4qGHHmq1TFVVFatWrQKSbslXXnmFysrKVuUAq1ev3qqb0qzYcjmrcR7JgPrmezUeExG1HT/LzMzsvdt///155JFHePPNN4kIFi1axNChQ1stU1NTw9y5cwG44447OPHEE5FETU0NtbW1bNq0iRUrVtDQ0MDRRx9djN0wa1cu1/EiItYCd+c5FjMz28mNHj2aCRMmcOSRR1JRUcEHP/hBzj//fGbOnMnIkSOpqalh2rRpnHPOOVRXV1NZWdly1uOwYcOYOHEihx12GBUVFcyePZtu3boVeY/MWssp8TIzMyuUWbNmMWvWrFZl3/jGN1qmd9ttN26//fasz7300ku59NJL8xqf2Y7IdXB9p5J0saSnJD0paZ6k3YoRh5mZmVkh5ZR4STpW0nnpdF9JB27vBiXtB1wEjIyI4UA3YNL2rs/MzMysq8jlAqqXAzOAr6ZFuwI/bf8ZOakAdpdUAfQE1uD4KOIAAByISURBVOzg+szMzMxKXi5jvE4HPggsA4iINZL22t4NRkSjpP8EngfeAhZGxMK2y0k6n/Rm3Pvvv//2bs7MzLqYQZfc02nrWnmlr/dtpSWXrsbNkVyVLgAk7bEjG5TUBzgNOBAYAOwh6ey2y0XEDRExMiJG9u3bd0c2aWZmZlYSckm8bpN0PdBb0meBXwI37sA2PwasiIh1EfEOybXBPrQD6zMzMzPrErbZ1RgR/ylpLPAqMASYGRH37cA2nwfGSOpJ0tX4UWDpDqzPzMzMrEvI9QKq9wE7kmxlrmuxpDtIxoxtAR4DbuiMdZuZmZmVsna7GiW9JunV9h47stGIuDwiDo2I4RFxTkRs2pH1mXW2Z555hiOOOKLlsffee3P11Ve3WiYiuOiii6iurmbEiBEsW7aspW7u3LkMHjyYwYMHt9zaxMzMrN0Wr4jYC0DSN4AXgJ8AAj4DbPdZjWZdwZAhQ3j88ccBaGpqYr/99uP0009vtcz8+fNpaGigoaGBxYsXc8EFF7B48WJefvllZs2axdKlS5HEUUcdRU1NDX369CnGrpiZWQnJZXD9uIj4YUS8FhGvRsSPgDPyHZhZqVi0aBEHH3wwBxxwQKvyuro6zj33XCQxZswYNm7cyNq1a1mwYAFjx46lsrKSPn36MHbsWOrr64sUvZmZlZJcEq8mSZ+R1E3SLpI+AzTlOzCzUlFbW8tZZ521VXljYyMDBw5sma+qqqKxsbHdcjMzs1wSr/8HTAT+lj4+nZaZlb3Nmzdz99138+lPf3qruuTydq1JarfczMxsm4lXRKyMiNMiYp+I6BsRn4yIlQWIzazo5s+fz5FHHsm+++67VV1VVRWrVq1qmV+9ejUDBgxot9zMzCynm2Sb7azmzZuXtZsRoKamhltvvZWI4JFHHqFXr17079+fcePGsXDhQjZs2MCGDRtYuHAh48aNK3DkZmZWinK6jpfZzujNN9/kvvvu4/rrr28pu+666wCYPn0648eP595776W6upqePXty8803A1BZWclll13GqFGjAJg5cyaVlZWF3wEzMys5TrzM2tGzZ0/Wr1/fqmz69Okt05KYPXt21udOnTqVqVOn5jU+MzPrerbZ1ShpX0lzJM1P5w+TNC3/oZmZmZmVl1zGeN0CLACaRwf/GfiXfAVkZmZmVq5ySbz2iYjbgHcBImILvo6XmZmZ2XuWyxivNyS9DwgASWOAV/IalVkJGHTJPZ22rpVXntJp6zIzs64rl8Tri8DdwMGSfgf0BSbkNSozMzOzMrTNxCsilkn6CDCE5CbZz0TEO3mPzMzMzKzMtJt4SfpUO1WHpLdFuTNPMZmZmZmVpY5avD6R/u0HfAi4P50/AXgAcOJlZmZm9h60m3hFxHkAkn4BHBYRa9P5/kD2q0aamZmZWbtyuZzEoOakK/U34JA8xWNmZmZWtnI5q/EBSQuAeSSXlJgE/CqvUZmZmZmVoVzOavx8OtD+H9KiGyLirvyGZWZmZlZ+crpJdnoGowfTm5mZme2AXG6SPUbS7yW9LmmzpCZJrxYiODMzM7Nyksvg+muBs4AGYHfgH4Ef5DMoMzMzs3KUa1fjs5K6RUQTcLOkh/Icl5mZmVnZySXxelNSd+BxSd8F1gJ75DcsMzMzs/KTS1fjOUA34PPAG8BA4Iwd2aik3pLukPQnSU9LOmZH1mdmZmbWFeRyOYm/ppNvAbM6abvXAPURMSFtTevZSes1MzMzK1kd3ST7CZILpmYVESO2Z4OS9gaOA6ak69kMbN6edZmZmZl1JR21eJ2a/r0w/fuT9O9ngDd3YJsHAetIBukfDjwKfCEi3tiBdZqZmZmVvHbHeEXEX9Nuxg9HxFci4on0cQkwbge2WQEcCfwoIj5IMm7skrYLSTpf0lJJS9etW7cDmzMzMzMrDbkMrt9D0rHNM5I+xI6d1bgaWB0Ri9P5O0gSsVYi4oaIGBkRI/v27bsDmzMzMzMrDblcTmIacJOkXun8RmDq9m4wIl6QtErSkIh4BvgosHx712dmZmbWVeRyVuOjwOHpoHhFxCudsN1/Bv47PaPxOeC8TlinmZmZWUnr6KzGsyPip5K+2KYcgIi4ans3GhGPAyO39/lmZmZmXVFHLV7N47j2ylLX7mUmzMzMzCy7dhOviLg+nfxlRPwus07Sh/MalZmZmVkZyuWsxh/kWGZmZmZmHehojNcxwIeAvm3Gee1Ncu9GMzMzM3sPOhrj1R3YM10mc5zXq8CEfAZlZmZmVo46GuP1a+DXkm7JuFG2mZmZmW2nXC6g2kPSDcCgzOUj4sR8BWVmZmZWjnJJvG4HrgN+DDTlNxwzMzOz8pVL4rUlIn6U90jMzMzMylwul5P4P0mfk9RfUmXzI++RmZmZmZWZXFq8Jqd//zWjLICDOj8cMzMzs/KVy02yDyxEIGZmZmblLpcWLyQNBw4Ddmsui4hb8xWUmZmZWTnaZuIl6XLgeJLE617gZOC3gBMvMzMzs/cgl8H1E4CPAi9ExHnA4UCPvEZlZmZmVoZySbzeioh3gS2S9gZexAPrzczMzN6zXMZ4LZXUG7gReBR4HViS16jMzMzMylAuZzV+Lp28TlI9sHdE/DG/YZmZmZmVn1wG1x+XrSwiHsxPSGZmZmblKZeuxswLp+4GHE3S5eibZJuZmZm9B7l0NX4ic17SQOC7eYvIzMzMrEzlclZjW6uB4Z0diJmZmVm5y2WM1w9I7s0ISaJ2BPCHfAZlZmZmVo5yupxExvQWYF5E/C5P8ZiZmZmVrVwSr9uB6nT6mYjYlMd4zMzMzMpWu2O8JO0q6WpgFXAzMBd4TtIlaf0HCxOimZmZWXnoaHD994A9gUERcVREfBAYChwk6UfAnTuyYUndJD0m6Rc7sh4zMzOzrqKjrsbxwOCIaB5YT0S8KukC4CXg5B3c9heAp4G9d3A9ZmZmZl1CRy1e72YmXc0ioglYFxGPbO9GJVUBpwA/3t51mJmZmXU1HSVeyyWd27ZQ0tkkLVU74mrgK8C77S0g6XxJSyUtXbdu3Q5uzszMzKz4OupqvBC4U9JUklsEBTAK2B04fXs3KOlU4MWIeFTS8e0tFxE3ADcAjBw5cquWNzMzM7Oupt3EKyIagdGSTgSGAQLmR8SiHdzmh4EaSeNJ7v24t6SfRsTZO7heMzMzs5KWy70a7wfu76wNRsRXga8CpC1eX3bSZWZmZjuD7blXo5mZmZlth6ImXhHxQEScWswYzMzMrHO8/fbbHH300Rx++OEMGzaMyy+/fKtlNm3axJlnnkl1dTWjR49m5cqVLXVXXHEF1dXVDBkyhAULFhQw8sLJ5ZZBZmZmZtvUo0cP7r//fvbcc0/eeecdjj32WE4++WTGjBnTssycOXPo06cPzz77LLW1tcyYMYOf/exnLF++nNraWp566inWrFnDxz72Mf785z/TrVu3Iu5R53NXo5mZmXUKSey5554AvPPOO7zzzjtIarVMXV0dkydPBmDChAksWrSIiKCuro5JkybRo0cPDjzwQKqrq1myZEnB9yHfnHiZmZlZp2lqauKII46gX79+jB07ltGjR7eqb2xsZODAgQBUVFTQq1cv1q9f36ocoKqqisbGxoLGXghOvMzMzKzTdOvWjccff5zVq1ezZMkSnnzyyVb1WW6Kg6R2y8uNEy8zMzPrdL179+b444+nvr6+VXlVVRWrVq0CYMuWLbzyyitUVla2KgdYvXo1AwYMKGjMheDEy8zMzDrFunXr2LhxIwBvvfUWv/zlLzn00ENbLVNTU8PcuXMBuOOOOzjxxBORRE1NDbW1tWzatIkVK1bQ0NDA0UcfXfB9yDef1WhmZmadYu3atUyePJmmpibeffddJk6cyKmnnsrMmTMZOXIkNTU1TJs2jXPOOYfq6moqKyupra0FYNiwYUycOJHDDjuMiooKZs+eXXZnNIITLzMzM+skI0aM4LHHHtuq/Bvf+EbL9G677cbtt9+e9fmXXnopl156ad7iKwXuajQzMzMrECdeZmZmZgXixMvMzMysQDzGy8zMzDrNoEvu6ZT1rLzylE5ZT6lxi5eZmZlZgTjxMjMzMysQJ15mZmZmBeLEy8zMzKxAnHiZmRXAqlWrOOGEExg6dCjDhg3jmmuu2WqZiOCiiy6iurqaESNGsGzZspa6uXPnMnjwYAYPHtxyuxUz63p8VqOZWQFUVFTwve99jyOPPJLXXnuNo446irFjx3LYYYe1LDN//nwaGhpoaGhg8eLFXHDBBSxevJiXX36ZWbNmsXTpUiRx1FFHUVNTQ58+fYq4R2a2PdziZWZWAP379+fII48EYK+99mLo0KE0Nja2Wqauro5zzz0XSYwZM4aNGzeydu1aFixYwNixY6msrKRPnz6MHTuW+vr6YuyGme0gJ15mZgW2cuVKHnvsMUaPHt2qvLGxkYEDB7bMV1VV0djY2G65mXU9TrzMzAro9ddf54wzzuDqq69m7733blUXEVstL6ndcjPrepx4mZkVyDvvvMMZZ5zBZz7zGT71qU9tVV9VVcWqVata5levXs2AAQPaLTezrseJl5lZAUQE06ZNY+jQoXzxi1/MukxNTQ233norEcEjjzxCr1696N+/P+PGjWPhwoVs2LCBDRs2sHDhQsaNG1fgPTCzzuCzGs3MCuB3v/sdP/nJT/jABz7AEUccAcC3v/1tnn/+eQCmT5/O+PHjuffee6murqZnz57cfPPNAFRWVnLZZZcxatQoAGbOnEllZWVxdsTMdogTLzOzAjj22GOzjtXKJInZs2dnrZs6dSpTp07NR2hmVkAF72qUNFDSryQ9LekpSV8odAxmZmZmxVCMFq8twJciYpmkvYBHJd0XEcuLEIuZmZlZwRS8xSsi1kbEsnT6NeBpYL9Cx2FmZmZWaEU9q1HSIOCDwOJixmFmZmZWCEUbXC9pT+B/gX+JiFez1J8PnA+w//77Fzg6M7PON+iSezplPSuvPKVT1mNmhVeUFi9Ju5IkXf8dEXdmWyYiboiIkRExsm/fvoUN0MzMzCwPinFWo4A5wNMRcVWht29mZmZWLMVo8fowcA5woqTH08f4IsRhZmZmVlAFH+MVEb8FfHdXMzMz2+n4Xo1WdFOnTqVfv34MHz48a31EcNFFF1FdXc2IESNYtmxZS93cuXMZPHgwgwcPZu7cuYUK2aws+LNnVnhOvKzopkyZQn19fbv18+fPp6GhgYaGBm644QYuuOACAF5++WVmzZrF4sWLWbJkCbNmzWLDhg2FCtusy/Nnz6zwnHhZ0R133HEd3vC3rq6Oc889F0mMGTOGjRs3snbtWhYsWMDYsWOprKykT58+jB07tsMfETNrzZ89s8Jz4mUlr7GxkYEDB7bMV1VV0djY2G65mXUOf/bMOp8TLyt5EbFVmaR2y82sc/izZ9b5nHhZyauqqmLVqlUt86tXr2bAgAHtlptZ5/Bnz6zzOfGykldTU8Ott95KRPDII4/Qq1cv+vfvz7hx41i4cCEbNmxgw4YNLFy4kHHjxhU7XLOy4c+eWecr2r0azZqdddZZPPDAA7z00ktUVVUxa9Ys3nnnHQCmT5/O+PHjuffee6murqZnz57cfPPNAFRWVnLZZZcxatQoAGbOnNnhQGEza82fPbPCc+JlRTdv3rwO6yUxe/bsrHVTp05l6tSp+QjLrOz5s2dWeO5qNDMzMysQJ15mZmZmBeLEy8zMzKxAPMbLSsKgS+7plPWsvPKUTlmP2c6isz574M+fWS7c4mVmZmZWIE68zMzMzArEiZeZmZlZgTjxMjMzMyuQskm86uvrGTJkCNXV1Vx55ZVb1W/atIkzzzyT6upqRo8ezcqVK1vqrrjiCqqrqxkyZAgLFiwo65hKOS4zM7N8KKXfvbJIvJqamrjwwguZP38+y5cvZ968eSxfvrzVMnPmzKFPnz48++yzXHzxxcyYMQOA5cuXU1tby1NPPUV9fT2f+9znaGpqKsuYSjkuMzOzfCi1372ySLyWLFlCdXU1Bx10EN27d2fSpEnU1dW1Wqauro7JkycDMGHCBBYtWkREUFdXx6RJk+jRowcHHngg1dXVLFmypCxjKuW4zMzM8qHUfvfKIvFqbGxk4MCBLfNVVVU0Nja2u0xFRQW9evVi/fr1OT23XGIq5bjMzMzyodR+98oi8YqIrcok5bRMLs8tl5hKOS4zM7N8KLXfvbJIvKqqqli1alXL/OrVqxkwYEC7y2zZsoVXXnmFysrKnJ5bLjGVclxmZmb5UGq/e2WReI0aNYqGhgZWrFjB5s2bqa2tpaamptUyNTU1zJ07F4A77riDE088EUnU1NRQW1vLpk2bWLFiBQ0NDRx99NFlGVMpx2VmZpYPpfa7Vxb3aqyoqODaa69l3LhxNDU1MXXqVIYNG8bMmTMZOXIkNTU1TJs2jXPOOYfq6moqKyupra0FYNiwYUycOJHDDjuMiooKZs+eTbdu3coyplKOy8zMLB9K7XdP2fovS83IkSNj6dKlxQ7D8qgUb5JdijcPdky5K+djqhRjgvI+pkoxJijvY6or33Rd0qMRMTJbXVG6GiWdJOkZSc9KuqQYMZiZmZkVWsETL0ndgNnAycBhwFmSDit0HGZmZmaFVowWr6OBZyPiuYjYDNQCpxUhDjMzM7OCKvgYL0kTgJMi4h/T+XOA0RHx+TbLnQ+cn84OAZ7ppBD2AV7qpHV1FseUG8eUu1KMyzHlxjHlrhTjcky5KfeYDoiIvtkqinFWY7Yrj22V/UXEDcANnb5xaWl7A96KxTHlxjHlrhTjcky5cUy5K8W4HFNuduaYitHVuBoYmDFfBawpQhxmZmZmBVWMxOv3wGBJB0rqDkwC7i5CHGZmZmYFVfCuxojYIunzwAKgG3BTRDxVwBA6vfuyEzim3Dim3JViXI4pN44pd6UYl2PKzU4bU5e4gKqZmZlZOSiLezWamZmZdQVOvMzMzMwKxImXmZmZWYEU4zpeBSPpUJKr4u9Hcq2wNcDdEfF0kWPaD1gcEa9nlJ8UEfVFimk08HREvCppd+AS4EhgOfDtiHilCDFdBNwVEasKve32ZJyFuyYifinp/wEfAp4GboiId4oU18HA6SSXadkCNADzivG+mZlZx8q2xUvSDJLbEQlYQnIZCwHzinVj7jSZqAP+GXhSUuatkr5djJhSNwFvptPXAL2A76RlNxcppn8HFkv6jaTPScp6BeACuxk4BfiCpJ8AnwYWA6OAHxcjoPSYug7YLY1jd5IE7GFJxxcjJjMza1/ZntUo6c/AsLatEGmrxVMRMbgIMT0BHBMRr0saBNwB/CQirpH0WER8sNAxpXE9HRFD0+llEXFkRt3jEXFEEWJ6DDgK+BhwJlADPArMA+6MiNeKENMfI2KEpAqgERgQEU2SBPwhIkYUIaYngCPSOHoC90bE8ZL2B+qKdUyZFYKkfhHxYrHjKHWS3hcR64sdhyXKtsULeBcYkKW8f1pXDN2auxcjYiVwPHCypKvIfiulQnlS0nnp9B8kjQSQdAhQlO4zICLi3YhYGBHTSN7LHwInAc8VKaZd0sR9L6AnScsgQA9g1yLFBH8fMtCDJDYi4nmKGJOkXpKulPQnSevTx9NpWe9ixdUeSfOLtN29JV0h6Sdp13Vm3Q+LFNP7Jf1I0mxJ75P0dUlPSLpNUv9ixJTGVdnm8T5giaQ+kiqLFNNJGdO9JM2R9EdJ/yNp3yLFdKWkfdLpkZKeI+k9+KukjxQppmWS/i0dFlES0tfmV5J+KmmgpPskvSLp95Ly+g9rOSde/wIskjRf0g3pox5YBHyhSDG9IKml9ShNwk4luTHnB4oUE8A/Ah+R9BfgMJJuqueAG9O6YmiViEbEOxFxd0ScBexfpJjmAH8CHgcuBW6XdCNJN3ZtkWL6MfB7STcADwPXAqRdsy8XKSaA24ANwPER8b6IeB9wQlp2ezECknRkO4+jgIK36qZuJjnW/xeYJOl/JfVI68YUKaZbSMZ3rgJ+BbxF0sX+G5Ju7WJ5iaTVu/mxlGS87LJ0uhgyh4h8D1gLfILkO+H6okQEp0RE842e/wM4MyKqgbEkMRZDH6A38CtJSyRdLClbw0gh/RD4LnAP8BBwfUT0IhnjnNd/esq2qxFA0i7A0SQfTpHcJ/L3EdFUpHiqgC0R8UKWug9HxO+KEFZmDHsBB5G0oKyOiL8VMZZDIuLPxdp+e5q/LCJiTdpy8zHg+YhYUsSYhgFDgScj4k/FiiOTpGciYsh7rctzTE3Ar8neujwmInYvcEhbdeVLuhQYT9K1fl9mt38BY2oZ9iDp+YjYP6OuKEMP0m1/meTz9q8R8URatiIiDixGPOn2W4ZmZHkvizVM40/A8PQuMY9ExJiMuiciouD/5Ld5nf4BOAv4FMmJSfMiouBXsd/GcZ7XoT9lfVZjRLwLPFLsOJpFxOoO6oqadKUxvAb8odhxAJRi0gVJwpUxvZFknF5RpbfcKuRtt3LxV0lfAeY2J/Bp18sUkpaUYnga+KeIaGhbIalYMfWQtEv6XUVEfEvSauBBYM8ixZTZE3Jrm7puhQwkU0T8p6Ra4Pvp+3U5ydnqxdRP0hdJkvm9JSn+3ppRrB6l2cC9kq4E6iVdDdwJfJSktb6oIuI3wG8k/TNJK9yZFOf2QW9L+jjJkJGQ9MmI+HnaHZvXxpmyTrzMrGjOJGmy/7WkfmnZ34C7Sc4GLYav0/6P4T8XMI5M/wecCPyyuSAi5kr6G/CDIsVUJ2nPiHg9Iv6tuVBSNfBMkWICWv55/bSkTwD3kYy1LKYbScdVAnNJho2sk/R+ipTkRMQPlJx0cwFwCMnv/CHAz0nOFi+Grf6RTnue6tNHMUwn6Wp8FxgHXCDpFpITpz6bzw2XdVejmZUeSedFRLEuU5KVY8pNKcWk5JqDB0fEk6UUVzPHlJudMSYnXmZWUG3HU5QCx5SbUowJSjMux5SbnTEmdzWaWaeT9Mf2qoBinWbvmHJQijFBacblmHLjmFpz4mVm+bAvybiJDW3KRXLqdjE4ptyUYkxQmnE5ptw4pgxOvMwsH34B7BkRWw0wlvRA4cMBHFOuSjEmKM24HFNuHFPm+j3Gy8zMzKwwyvnK9WZmZmYlxYmXme30JHWT9AUlN0A3M8sbJ15m1iVJer3N/BRJ127n6r4IvB4RW9rZ1gBJRb9LgZl1ff7vzsx2SpIq0vvZ7QK8EBE/6WC5NcCEwkZoZuXILV5mVnYkHSBpkaQ/pn/3T8tvkXSVpF8B35F0NPBb4IuSHpI0JF1uiqTbJf0fsFDSIElPpnW7SbpZ0hOSHpN0QrH208y6Hrd4mVlXtbukzFPBK0nuBQlwLXBret/DqcB/AZ9M6w4BPhYRTZL2Bo5LW77GAd8GzkiXOwYYEREvSxqUsZ0LASLiA5IOJUnMDomIt/Owj2ZWZpx4mVlX9VZEHNE8I2kKMDKdPQb4VDr9E5Kb4Ta7Pb1BLyQ3OL5R0n4kF058X8Zy90XEy1m2eyzpDawj4k+S/kqSzLV3JWwzsxbuajSznUHmBQvfyJj+JvCriDgWOBvYrZ3lMqmTYzOznYgTLzMrRw8Bk9Lpz5CM48qmD7AunZ6S47ofTNeJpEOA/YFntitKM9vpOPEys3J0EXBeeiPcc4AvtLPcfwBXSPoduX8f/hDoJukJ4GfAlIjYtKMBm9nOwbcMMjMzMysQt3iZmZmZFYgTLzMzM7MCceJlZmZmViBOvMzMzMwKxImXmZmZWYE48TIzMzMrECdeZmZmZgXy/wFGPBCaLN2ckwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_central'))]['HOUR_OPEN'].value_counts().sort_index())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média STI_CENTRAL\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZsycHH3xw1vlf//rXOeKII/jCF77QYl62y5hIarXczMysI3DiZVvtqaee4v7776dv376MHTuWefPm8dWvfhWAyZMns3LlSq677rqs61ZUVFBf/9Ft25YtW0bv3r2pqKjYpGmysdzMzKwjcOJlW+3qq69m2bJlLF26lNmzZ3PUUUfx85//nFtuuYVHHnmEWbNmZW2CBBgxYgRz585l9erVrF69mrlz5zJixAh69epFly5dePrpp4kIbr/9do4//vgCH5mZmVl+OPGybe6cc87h9ddf57DDDmPw4MFceeWVAMyfP7+pE355eTmXXnopQ4cOZejQoVx22WWUl5cDcNNNN3HmmWdSWVnJ3nvvzTHHHFO0YzEzM9uWSuKWQUOGDAlfx8vMzMxKgaQFETEk2zzXeJmZmZkViBMvMzMzswJx4mVmZmZWICVxr0Zr//pe9GDetr10yrF527aZmVkhucbLzMzMrECceJmZmZkViBMvMzMzswJx4mVmZmZWIE68zMzMzArEiZeZmZlZgTjxMjMzMysQJ15mZmZmBZL3xEtSJ0nPSnognf6MpGck1Un6haSd8h2DmZmZWXtQiBqv84HFGdPXANdHRH9gNTCxADGYmZmZFV1eEy9JFcCxwC3ptICjgHvSRWYCJ+QzBjMzM7P2It81XjcA3wM+TKc/AayJiA3p9DJgrzzHYGZmZtYu5C3xknQc8EZELMgszrJotLL+2ZLmS5q/cuXKvMRoZmZmVkj5rPH6PDBK0lJgNkkT4w1AV0ll6TIVwPJsK0fE9IgYEhFDevTokccwzczMzAojb4lXRPx7RFRERF9gLDAvIsYBjwMnp4uNB+bkKwYzMzOz9qQY1/GaBFwo6RWSPl8zihCDmZmZWcGVbX6Rjy8ingCeSJ//FTikEPs1MzMza0985XozMzOzAnHiZWZm7c4//vEPDjnkEA488EAGDhzI5ZdfDsAXvvAFBg8ezODBg+nduzcnnJD9UpAzZ86kf//+9O/fn5kzZzaVL1iwgAMOOIDKykq+9a1vEZF1YL1Z3hSkqdHMzGxL7LzzzsybN4/OnTuzfv16Dj/8cI455hh++9vfNi0zevRojj/++BbrvvXWW0yePJn58+cjiYMPPphRo0bRrVs3zj33XKZPn86hhx7KyJEjqamp4Zhjjinkodl2zjVeZmbW7kiic+fOAKxfv57169eT3PwksXbtWubNm5e1xuuRRx6hurqa8vJyunXrRnV1NTU1NaxYsYJ33nmHww47DEmcfvrp3HfffQU7JjNw4mVmZu3Uxo0bGTx4MD179qS6upphw4Y1zbv33nv54he/yB577NFivYaGBvr06dM0XVFRQUNDAw0NDVRUVLQoNyskJ15mZtYuderUiYULF7Js2TJqa2t58cUXm+bNmjWLU089Net62fptSWq13KyQnHiZmVm71rVrV4488khqamoAWLVqFbW1tRx77LFZl6+oqKC+vr5petmyZfTu3ZuKigqWLVvWotyskJx4mZWY1kZ7RQQXX3wx++yzD1VVVfzf//t/s67v0V5WClauXMmaNWsAeP/99/n1r3/NvvvuC8Ddd9/Ncccdxy677JJ13REjRjB37lxWr17N6tWrmTt3LiNGjKBXr1506dKFp59+mojg9ttvz9o53yyfPKrRrMS0Ntpr8eLF1NfX89JLL7HDDjvwxhtvtFjXo72sVKxYsYLx48ezceNGPvzwQ8aMGcNxxx0HwOzZs7nooos2WX7+/Pn85Cc/4ZZbbqG8vJxLL72UoUOHAnDZZZdRXl4OwE033cQZZ5zB+++/zzHHHONz3ArOiZdZiWlttNdNN93EnXfeyQ47JBXZPXv2bLFu5mgvoGm015FHHtk02gtoGu3lf0pWLIMGDeLZZ5/NOu+JJ55oUTZkyBBuueWWpukJEyYwYcKErMtl9hUzKzQ3NZqVoGyjvf7yl7/wi1/8giFDhnDMMcdQV1fXYj2P9jIzKy4nXmYlKNtorw8++IBddtmF+fPnc9ZZZ2X9te/RXmZmxeXEy6yEZY72qqioYPTo0QCceOKJPP/88y2W92gvM7Pich8vsxKzcuVKdtxxR7p27do02mvSpEmccMIJzJs3jwkTJvDkk0+yzz77tFh3xIgRfP/732f16tUAzJ07l6uvvpry8vKm0V7Dhg3j9ttv55vf/GahD82shb4XPZi3bS+dkv1yFGb55MTLrMS0Ntrr8MMPZ9y4cVx//fV07ty5qaOxR3uZmbUfKoVr9QwZMiTmz59f7DCsDf5Vamb54O8WK0WSFkTEkGzz3MfLzMzMrECceJmZmZkViBMvMzMzswJx53qzEuW+L2Zmpcc1XmZmZmYF4sTLzMzMrEDylnhJ2kVSraTnJP1Z0uS0/DZJSyQtTB+D8xWDmZmZWXuSzz5eHwBHRcS7knYEfifp4XTedyPinjzu28zMzKzdyVviFcmVWd9NJ3dMH+3/aq1mZmZmeZLXPl6SOklaCLwBPBoRz6SzfiDpeUnXS9q5lXXPljRf0vyVK1fmM0wzMzOzgshr4hURGyNiMFABHCJpf+DfgX2BoUA5MKmVdadHxJCIGNKjR498hmlmZmZWEAUZ1RgRa4AngKMjYkUkPgB+BhxSiBjMzMzMii2foxp7SOqaPt8V+BLwkqReaZmAE4AX8xWDmZmZWXuSz1GNvYCZkjqRJHh3RcQDkuZJ6gEIWAick8cYzMzMzNqNfI5qfB74bJbyo/K1TzMzM7P2zFeuNzMzMysQJ15mZmZmBeLEy8zMzKxAnHiZmZmZFYgTLzMzM7MCceJlZmZmViBOvMzMzMwKxImXmZmZWYE48TIzMzMrkM0mXpI+L2n39PlXJV0n6dP5D83MzMysY8mlxusm4O+SDgS+B7wK3J7XqMzMzMw6oFwSrw0REcDxwNSImAp0yW9YZmZmZh1PLjfJXivp34GvAkdI6gTsmN+wzMzMzDqeXGq8TgE+ACZGxGvAXsB/5jUqMzMzsw5oszVeabJ1Xcb033AfLzMzM7MtlsuoxkMl/VHSu5LWSdoo6e1CBGdmZmbWkeTS1DgNOBWoA3YFzgRuzGdQZmZmZh1RLp3riYhXJHWKiI3AzyT9Ps9xmZmZmXU4uSRef5e0E7BQ0o+AFcDu+Q3LzMzMrOPJpanxNKAT8A3gPaAPMDqfQZnlW319PcOHD6eqqoqBAwcydepUAJ577jkOO+wwDjjgAL7yla/wzjvvZF2/pqaGAQMGUFlZyZQpU5rKlyxZwrBhw+jfvz+nnHIK69atK8jxmJlZadhs4hURr0bE+xHxTkRMjogLI+KVQgRnli9lZWVce+21LF68mKeffpobb7yRRYsWceaZZzJlyhReeOEFTjzxRP7zP1teOWXjxo2cd955PPzwwyxatIhZs2axaNEiACZNmsQFF1xAXV0d3bp1Y8aMGYU+NDMza8daTbwk3ZX+fUHS880fhQvRbNvr1asXBx10EABdunShqqqKhoYGXn75ZY444ggAqqur+eUvf9li3draWiorK+nXrx877bQTY8eOZc6cOUQE8+bN4+STTwZg/Pjx3HfffYU7KDMza/faqvE6P/17HPCVLI82SdpFUq2k5yT9WdLktPwzkp6RVCfpF2n/MbOiWbp0Kc8++yzDhg1j//335/777wfg7rvvpr6+vsXyDQ0N9OnTp2m6oqKChoYGVq1aRdeuXSkrK9uk3MzMrFGriVdErMhY5vW0yfFV4A1AOWz7A+CoiDgQGAwcLelQ4Brg+ojoD6wGJn6cAzD7ON59911Gjx7NDTfcwB577MGtt97KjTfeyMEHH8zatWvZaaeWvwuSW5duSlKr5WZmZo1y6Vx/N/BhxvTGtKxNkXg3ndwxfQRwFHBPWj4TOCHnaM22ofXr1zN69GjGjRvHSSedBMC+++7L3LlzWbBgAaeeeip77713i/UqKio2qQlbtmwZvXv3pnv37qxZs4YNGzZsUm5mZtYol8SrLCKahmalz3NqHpTUSdJCklqyR4G/AGsiYkO6yDKSez9mW/dsSfMlzV+5cmUuuzPLWUQwceJEqqqquPDCC5vK33jjDQA+/PBDrrrqKs4555wW6w4dOpS6ujqWLFnCunXrmD17NqNGjUISw4cP5557kt8VM2fO5Pjjjy/MAZmZWUnIJfFaKWlU44Sk44E3c9l4RGyMiMFABXAIUJVtsVbWnR4RQyJiSI8ePXLZnVnOnnrqKe644w7mzZvH4MGDGTx4MA899BCzZs1in332Yd9996V379587WtfA2D58uWMHDkSSEZETps2jREjRlBVVcWYMWMYOHAgANdccw3XXXcdlZWVrFq1iokT3ZJuZmYfyeUCqucA/yNpGknfrnrg9C3ZSUSskfQEcCjQVVJZWutVASzfspDNPr7DDz88a58sgPPPP79FWe/evXnooYeapkeOHNmUiGXq168ftbW12y5QMzPrUHK5jtdfIuJQYD9gv4j4XC7X8ZLUQ1LX9PmuwJeAxcDjwMnpYuOBOVsbvJmZmVkp2WyNl6SdSa5U3xcoaxylFRFXbmbVXsBMSZ1IEry7IuIBSYuA2ZKuAp4FfIVJMzMz2y7k0tQ4B3gbWEByiYicRMTzwGezlP+VpL+XmZmZ2XYll8SrIiKOznskZkXQ96IH87btpVOOzdu2zcysNOUyqvH3kg7IeyRmZmZmHVwuNV6HA2dIWkLS1CiS66MOymtkZmZmZh1MLonXMXmPwszMzGw7kMvlJF4F+pDcd/FV4O+5rGdmZmZmm2o1gZK0b/r3cmAS8O/prB2Bn+c/NDMzM7OOJWviJekooPFeJycCo4D3ACJiOdClINGZmZmZdSCt1XjtQppoAesiubdKAEjavRCBmZmZmXU0WTvXR8RDkt5IJ++SdDPJPRbPAiYAPy1UgGZmZmYdRaujGiNifvr3vyRVA+8AA4DLIuLRAsVnZmZm1mG0eTmJ9D6Lj0TElwAnW2ZmZmYfQ5uXhYiIjcDfJe1ZoHjMzMzMOqxcLqD6D+AFSY/yUYd7IuJbeYvKzMzMrAPK5UKoDwKXAr8BFmQ8zMzMLEN9fT3Dhw+nqqqKgQMHMnXqVACuuOIK9tprLwYPHszgwYN56KGHsq5fU1PDgAEDqKysZMqUKU3lS5YsYdiwYfTv359TTjmFdevWFeR4bNtrM/FK+3hVR8TM5o8CxWdmZlYyysrKuPbaa1m8eDFPP/00N954I4sWLQLgggsuYOHChSxcuJCRI0e2WHfjxo2cd955PPzwwyxatIhZs2Y1rTtp0iQuuOAC6urq6NatGzNmzCjocdm2k0sfrx6SdipQPGZmZiWrV69eHHTQQQB06dKFqqoqGhoaclq3traWyspK+vXrx0477cTYsWOZM2cOEcG8efM4+eSTARg/fjz33Xdf3o7B8iuXpsalwFOSLpV0YeMjz3GZmZmVtKVLl/Lss88ybNgwAKZNm8agQYOYMGECq1evbrF8Q0MDffr0aZquqKigoaGBVatW0bVrV8rKyjYpt9KUS+K1HHggXbZLxsPMzMyyePfddxk9ejQ33HADe+yxB+eeey5/+ctfWLhwIb169eI73/lOi3WSm8RsSlKr5VaaNjuqMSImA0jqkkzGu3mPyszMrEStX7+e0aNHM27cOE466SQAPvnJTzbNP+usszjuuONarFdRUUF9fX3T9LJly+jduzfdu3dnzZo1bNiwgbKysqZyK02brfGStL+kZ4EXgT9LWiBpYP5DMzMzKy0RwcSJE6mqquLCCz/qlbNixYqm5/feey/7779/i3WHDh1KXV0dS5YsYd26dcyePZtRo0YhieHDh3PPPfcAMHPmTI4//vj8H4zlRS5NjdOBCyPi0xHxaeA7+F6NZmZmLTz11FPccccdzJs3b5NLR3zve9/jgAMOYNCgQTz++ONcf/31ACxfvrxphGNZWRnTpk1jxIgRVFVVMWbMGAYOTOo5rrnmGq677joqKytZtWoVEydOLNox2seTywVUd4+IxxsnIuIJSbvnMSYzM7OSdPjhh2ftk5Xt8hEAvXv33uSaXiNHjsy6bL9+/aitrd12gVrR5FLj9dd0RGPf9HEJsGRzK0nqI+lxSYsl/VnS+Wn5FZIaJC1MH9nPRjMzM7MOJp/bs/EAAB7hSURBVJcarwnAZOBX6fRvgK/lsN4G4DsR8ae0Y/6C9LZDANdHxH9tcbRmZmZmJSyXUY2rgS2+L2NErABWpM/XSloM7LXFEZqZmZl1EJtNvNJaqn+JiDXpdDdgdkSMyHUnkvoCnwWeAT4PfEPS6cB8klqxFleSk3Q2cDbApz71qVx3ZWZmqfr6ek4//XRee+01dthhB84++2zOP/98Lr30UubMmcMOO+xAz549ue2227JenmDmzJlcddVVAFxyySWMHz8egAULFnDGGWfw/vvvM3LkSKZOnerrSjXT96IH87btpVOOzdu2Lf9y6ePVvTHpgqYasJ657kBSZ+CXwLcj4h3gJmBvYDBJjdi12daLiOkRMSQihvTo0SPX3ZmZWaq1+wZ+97vf5fnnn2fhwoUcd9xxXHnllS3Wfeutt5g8eTLPPPMMtbW1TJ48uelq6+eeey7Tp0+nrq6Ouro6ampqCn1oZiUrl8TrQ0lNVU6SPg20HLKRhaQdSZKu/4mIXwFExOsRsTEiPiS5LMUhWx62mZltTmv3Ddxjjz2alnnvvfey1lY98sgjVFdXU15eTrdu3aiurqampoYVK1bwzjvvcNhhhyGJ008/3fcNNNsCuXSuvxj4naQn0+kjSJsA26LkkzwDWBwR12WU90r7fwGcSHJhVjMzy6Pm9w28+OKLuf3229lzzz15/PHHWyzf2n0DGxoaqKioaFFuZrnZbI1XRNQABwG/SB8HR8QjOWz788BpwFHNLh3xI0kvSHoeGA5csPXhm5nZ5jS/byDAD37wA+rr6xk3bhzTpk1rsY7vG2iWH7k0NQJ8DjgyfRyaywoR8buIUEQMiojB6eOhiDgtIg5Iy0dl1H6Zmdk2lu2+gZn+9V//lV/+8pctylu7b2BFRQXLli1rUW5mucnlXo1TgPOBRenjfElX5zuw7Ul9fT3Dhw+nqqqKgQMHMnXqVAC++93vsu+++zJo0CBOPPFE1qxZk3X9mpoaBgwYQGVlJVOmTGkqX7JkCcOGDaN///6ccsoprFu3riDHY2btQ2v3Dayrq2t6fv/997Pvvvu2WHfEiBHMnTuX1atXs3r1aubOncuIESPo1asXXbp04emnnyYiuP32233fQLMtkEuN10igOiJujYhbgaMBj2XdhlobeVRdXc2LL77I888/zz777MPVV7fMdzdu3Mh5553Hww8/zKJFi5g1axaLFi0CYNKkSVxwwQXU1dXRrVs3ZsyYUehDM7Miau2+gRdddBH7778/gwYNYu7cuU0/9ubPn8+ZZ54JQHl5OZdeeilDhw5l6NChXHbZZZSXlwNw0003ceaZZ1JZWcnee+/NMcccU7RjNCs1uXSuB+gKvJU+3zNPsWy3evXqRa9evYBNRx59+ctfblrm0EMPbbozfaba2loqKyvp168fAGPHjmXOnDlUVVUxb9487rzzTgDGjx/PFVdcwbnnnluAIzKz9mBL7xs4ZMgQbrnllqbpCRMmMGHChKzLvfiix0WZbY1caryuBp6VdJukmcAC4If5DWv71XzkUaNbb70166/K1kYerVq1iq5du1JWVrZJuZmZmRVPLrcMmiXpCWAoIGBSRLyW78C2R9lGHkEy+qisrIxx48a1WMcjj8zMzEpHTk2N6cjD+/Mcy3attZFHM2fO5IEHHuCxxx7Lmji1NvKoe/furFmzhg0bNlBWVuaRR2ZmZu1Arn28LI9aG3lUU1PDNddcw5NPPsluu+2Wdd2hQ4dSV1fHkiVL2GuvvZg9ezZ33nknkhg+fDj33HMPY8eOZebMmR55ZLad8n0DzdqPXK/jZXnU2sijb3zjG6xdu5bq6moGDx7MOeecA8Dy5cubOseWlZUxbdo0RowYQVVVFWPGjGHgwIEAXHPNNVx33XVUVlayatUqJk6cWLRjNDMzsxxrvCQdDvSPiJ9J6gF0jogl+Q1t+7GlI4969+7NQw89tMly2Zbt168ftbW12y5QMzMz+1hyuYDq5cAk4N/Toh2Bn+czKDMzM7OOKJemxhOBUcB7ABGxHOiSz6DMzMzMOqJcEq91kbSDBYCk3fMbkpmZmVnHlEsfr7sk3Qx0lXQWMAH4aX7D2j555JGZmVnHlssFVP9LUjXwDjAAuCwiHs17ZGZmZmYdTK4XUH0UcLJlZmZm9jG0mnhJWkvaryubiNijtXlmZmZm1lKriVdEdAGQdCXwGnAHyb0ax+FRjWZmZmZbLJdRjSMi4r8jYm1EvBMRNwGj8x2YmZmZWUeTS+K1UdI4SZ0k7SBpHLAx34GZmZmZdTS5JF7/CowBXk8f/5KWmZmZmdkWyOVyEkuB4/MfipmZmVnHlkuNl5mZmZltA3lLvCT1kfS4pMWS/izp/LS8XNKjkurSv93yFYOZmZlZe5LPGq8NwHciogo4FDhP0n7ARcBjEdEfeCydNjMzM+vwNpt4SfqkpBmSHk6n95M0cXPrRcSKiPhT+nwtsBjYi6S/2Mx0sZnACVsbvJmZmVkpyaXG6zbgEaB3Ov3/gG9vyU4k9QU+CzwDfDIiVkCSnAE9W1nnbEnzJc1fuXLlluzOzMzMrF3KJfHqHhF3AR8CRMQGtuA6XpI6A78Evh0R7+S6XkRMj4ghETGkR48eua5mZmZm1m7lkni9J+kTpPdtlHQo8HYuG5e0I0nS9T8R8au0+HVJvdL5vYA3tjhqMzMzsxKUS+J1IXA/sLekp4DbgW9ubiVJAmYAiyPiuoxZ9wPj0+fjgTlbFLGZmZlZicrlAqp/kvTPwACSm2S/HBHrc9j254HTgBckLUzLvg9MAe5KO+j/jeRK+GZmZmYdXquJl6STWpm1jyQymg6ziojfkSRq2Xwxx/jMzMzMOoy2ary+kv7tCXwOmJdODweeANpMvMzMzMxsU60mXhHxNQBJDwD7NV4CIu0Qf2NhwjMzMzPrOHLpXN+3MelKvQ7sk6d4zMzMzDqszXauB56Q9Agwi+SSEmOBx/MalZmZmVkHlMuoxm+kHe2/kBZNj4h78xuWmZmZWceTS41X4whGd6Y3MzMz+xhyuUn2oZL+KOldSeskbZSU861/zMzMzCyRS+f6acCpQB2wK3Am8ON8BmVmZmbWEeXa1PiKpE4RsRH4maTf5zkuMzMzsw4nl8Tr75J2AhZK+hGwAtg9v2GZmZmZdTy5NDWeBnQCvgG8B/QBRuczKDMzM7OOKJfLSbyaPn0fmJzfcMzMzMw6rrZukv0CyQVTs4qIQXmJyMzMzKyDaqvG67j073np3zvSv+OAv+ctIjMzM7MOqq2bZL8KIOnzEfH5jFkXSXoKuDLfwZmZmZl1JLl0rt9d0uGNE5I+h0c1mpmZmW2xXC4nMRG4VdKe6fQaYEL+QjIzMzPrmHIZ1bgAOFDSHoAi4u38h2VmZmbW8bQ1qvGrEfFzSRc2KwcgIq7Lc2xmZmZmHUpbNV6N/bi6ZJnX6mUmzMzMzCy7tkY13pw+/XVEPJU5T9Lns6xiZmZmZm3IZVTjj3MsMzMzM7M2tNXH6zDgc0CPZv289iC5d2ObJN1KchHWNyJi/7TsCuAsYGW62Pcj4qGtC93MzMystLRV47UT0JkkOeuS8XgHODmHbd8GHJ2l/PqIGJw+nHSZmZnZdqOtPl5PAk9Kui3jRtk5i4jfSOr7MWIzMzMz61ByuYDqzpKmA30zl4+Io7Zyn9+QdDowH/hORKzOtpCks4GzAT71qU9t5a7MzMzM2o9cOtffDTwLXAJ8N+OxNW4C9gYGAyuAa1tbMCKmR8SQiBjSo0ePrdydmZmZWfuRS43Xhoi4aVvsLCJeb3wu6afAA9tiu2ZmZmalIJcar/+V9HVJvSSVNz62ZmeSemVMngi8uDXbMTMzMytFudR4jU//ZjYvBtCvrZUkzQKOBLpLWgZcDhwpaXC6/lLg37YwXjMzM7OSlctNsj+zNRuOiFOzFM/Ymm2ZmZmZdQS51HghaX9gP2CXxrKIuD1fQZmZmZl1RJtNvCRdTtJkuB/wEHAM8DvAiZeZmZnZFsilc/3JwBeB1yLia8CBwM55jcrMzMysA8ol8Xo/Ij4ENkjaA3iDzXSsNzMzM7OWcunjNV9SV+CnwALgXaA2r1GZmZmZdUC5jGr8evr0J5JqgD0i4vn8hmVmZmbW8Wy2qVHSEY0P4FNA1/S5mZmZdRATJkygZ8+e7L///k1ld999NwMHDmSHHXZg/vz5ra5bU1PDgAEDqKysZMqUKU3lS5YsYdiwYfTv359TTjmFdevW5fUYSkEufbwy7894KfC/wBV5jMnMzMwK7IwzzqCmpmaTsv33359f/epXHHFE6/UtGzdu5LzzzuPhhx9m0aJFzJo1i0WLFgEwadIkLrjgAurq6ujWrRszZvhynptNvCLiKxmPamB/4PXNrWdmZmal44gjjqC8fNM7AlZVVTFgwIA216utraWyspJ+/fqx0047MXbsWObMmUNEMG/ePE4++WQAxo8fz3333Ze3+EtFLjVezS0jSb7MzMxsO9fQ0ECfPn2apisqKmhoaGDVqlV07dqVsrKyTcq3d7lcQPXHJPdWhCRRGww8l8+gzMzMrDRERIsySa2Wb+9yupxExvMNwKyIeCpP8ZiZmVkJqaiooL6+vml62bJl9O7dm+7du7NmzRo2bNhAWVlZU/n2LpemxruBZ9PHPU66zMzMrNHQoUOpq6tjyZIlrFu3jtmzZzNq1CgkMXz4cO655x4AZs6cyfHHH1/kaIuv1cRL0o6SbgDqgZ8BM4G/Sroonf/ZwoRoZmZm+Xbqqady2GGH8fLLL1NRUcGMGTO49957qaio4A9/+APHHnssI0aMAGD58uWMHDkSgLKyMqZNm8aIESOoqqpizJgxDBw4EIBrrrmG6667jsrKSlatWsXEiROLdnztRVtNjdcCuwF9I2ItQHrLoP+SdBNwNPCZ/IdoZmZm+TZr1qys5SeeeGKLst69e/PQQw81TY8cObIpEcvUr18/amt9s5tMbSVeI4H+kdE7LiLekXQu8CZwTL6DMzMzM+tI2urj9WFkGZIQERuBlRHxdP7CMjMzM+t42kq8Fkk6vXmhpK8Ci/MXkpmZmVnH1FZT43nAryRNABaQXMtrKLAr0LLB18zMzEpe34sezNu2l045Nm/bLhWtJl4R0QAMk3QUMBAQ8HBEPFao4MzMzMw6ks1eQDUi5gHzChCLmZmZWYe2NfdqzImkWyW9IenFjLJySY9Kqkv/dsvX/s3MzMzam7wlXsBtJNf6ynQR8FhE9AceS6fNzMzMtgt5S7wi4jfAW82Kjye5Aj7p3xPytX8zMzOz9iafNV7ZfDIiVgCkf3u2tqCksyXNlzR/5cqVBQvQzMzMLF8KnXjlLCKmR8SQiBjSo0ePYodjZmZm9rEVOvF6XVIvgPTvGwXev5mZmVnRFDrxuh8Ynz4fD8wp8P7NzMzMiiafl5OYBfwBGCBpmaSJwBSgWlIdUJ1Om5mZmW0XNnsB1a0VEae2MuuL+dqnmZmZWXvWbjvXm5mZmXU0TrzMzMzMCsSJl5mZmVmBOPEyMzMzKxAnXmZmZmYF4sTLzMzMrECceJmZmZkViBMvMzMzswJx4mVmZmZWIE68zMzMzArEiZeZmZlZgTjxMjMzMysQJ15mZmZmBeLEy8zMzKxAnHiZmZmZFYgTLzMzM7MCceJlZmZmViBOvMzMzMwKxImXmZmZWYE48TIzMzMrECdeZmZmZgXixMvMzMysQMqKsVNJS4G1wEZgQ0QMKUYcZmZmZoVUlMQrNTwi3izi/s3MzMwKyk2NZmZmZgVSrMQrgLmSFkg6O9sCks6WNF/S/JUrVxY4PDMzM7Ntr1iJ1+cj4iDgGOA8SUc0XyAipkfEkIgY0qNHjy3ewT/+8Q8OOeQQDjzwQAYOHMjll1/eYpkPPviAU045hcrKSoYNG8bSpUub5l199dVUVlYyYMAAHnnkkS3ev5m1zp9PM9teFSXxiojl6d83gHuBQ7b1PnbeeWfmzZvHc889x8KFC6mpqeHpp5/eZJkZM2bQrVs3XnnlFS644AImTZoEwKJFi5g9ezZ//vOfqamp4etf/zobN27c1iGabbf8+TSz7VXBEy9Ju0vq0vgc+DLwYh72Q+fOnQFYv34969evR9Imy8yZM4fx48cDcPLJJ/PYY48REcyZM4exY8ey884785nPfIbKykpqa2u3dYhm2y1/Ps1se1WMGq9PAr+T9BxQCzwYETX52NHGjRsZPHgwPXv2pLq6mmHDhm0yv6GhgT59+gBQVlbGnnvuyapVqzYpB6ioqKChoSEfIZptt/z5NLPtUcETr4j4a0QcmD4GRsQP8rWvTp06sXDhQpYtW0ZtbS0vvrhpxVpEtFhHUqvlZrbt+PNpZtuj7eJyEl27duXII4+kpmbTirWKigrq6+sB2LBhA2+//Tbl5eWblAMsW7aM3r17FzRms+2FP59mtj3psInXypUrWbNmDQDvv/8+v/71r9l33303WWbUqFHMnDkTgHvuuYejjjoKSYwaNYrZs2fzwQcfsGTJEurq6jjkkG3e/99su1XKn8/6+nqGDx9OVVUVAwcOZOrUqS2WiQi+9a1vUVlZyaBBg/jTn/7UNG/mzJn079+f/v37Nx2fmW0bpfD5LOaV6/NqxYoVjB8/no0bN/Lhhx8yZswYjjvuOC677DKGDBnCqFGjmDhxIqeddhqVlZWUl5cze/ZsAAYOHMiYMWPYb7/9KCsr48Ybb6RTp05FPiKzjqOUP59lZWVce+21HHTQQaxdu5aDDz6Y6upq9ttvv6ZlHn74Yerq6qirq+OZZ57h3HPP5ZlnnuGtt95i8uTJzJ8/H0kcfPDBjBo1im7duhUsfrOOrBQ+nx028Ro0aBDPPvtsi/Irr7yy6fkuu+zC3XffnXX9iy++mIsvvjhv8Zltz0r589mrVy969eoFQJcuXaiqqqKhoWGTL/Y5c+Zw+umnI4lDDz2UNWvWsGLFCp544gmqq6spLy8HoLq6mpqaGk499dSiHItZR1MKn88O29RoZpZvS5cu5dlnn21zRCZ8NPLSIzLNCqe9fj6deJmZbYV3332X0aNHc8MNN7DHHntsMs8jMs2Kqz1/Pp14mZltofXr1zN69GjGjRvHSSed1GJ+ayMvPSLTLP/a++ezQ/bx6nvRg3nb9tIpx+Zt22bbg1L/fEYEEydOpKqqigsvvDDrMqNGjWLatGmMHTuWZ555hj333JNevXoxYsQIvv/977N69WoA5s6dy9VXX533mM22F6Xw+eyQiZeZWb489dRT3HHHHRxwwAEMHjwYgB/+8If87W9/A+Ccc85h5MiRPPTQQ1RWVrLbbrvxs5/9DIDy8nIuvfRShg4dCsBll13W1JHXzD6+Uvh8OvEyM9sChx9+eNa+IJkkceONN2adN2HCBCZMmJCP0My2e6Xw+XQfLzMzM7MCceJlZmZmViBOvMzMzMwKxH28zMxyVOojMs06slL5fLrGy8zMzKxAnHiZmZmZFYgTLzMzM7MCceJlZmZmViBOvMzMzMwKxImXmZmZWYE48TIzMzMrECdeZmZmZgVSlMRL0tGSXpb0iqSLihGDmZmZWaEVPPGS1Am4ETgG2A84VdJ+hY7DzMzMrNCKUeN1CPBKRPw1ItYBs4HjixCHmZmZWUEpIgq7Q+lk4OiIODOdPg0YFhHfaLbc2cDZ6eQA4OU8hdQdeDNP2863Uo29VOOG0o29VOOG0o29VOOG0o29VOOG0o29VOOG/Mb+6YjokW1GMW6SrSxlLbK/iJgOTM97MNL8iBiS7/3kQ6nGXqpxQ+nGXqpxQ+nGXqpxQ+nGXqpxQ+nGXqpxQ/FiL0ZT4zKgT8Z0BbC8CHGYmZmZFVQxEq8/Av0lfUbSTsBY4P4ixGFmZmZWUAVvaoyIDZK+ATwCdAJujYg/FzqODHlvzsyjUo29VOOG0o29VOOG0o29VOOG0o29VOOG0o29VOOGIsVe8M71ZmZmZtsrX7nezMzMrECceJmZmZkViBMvMzMzswIpxnW8ikbSt4B7I6K+2LFsiYzRn8sj4teS/hX4HLAYmB4R64sa4GZI2hs4keQyIhuAOmBWRLxd1MDMzMwKbLvqXC/pbeA94C/ALODuiFhZ3Kg2T9L/kCTJuwFrgM7Ar4AvkryH44sYXpvSZPcrwJPASGAhsJokEft6RDxRvOjMzMwKa3tLvJ4FDga+BJwCjAIWkCRhv4qItUUMr1WSno+IQZLKgAagd0RslCTguYgYVOQQWyXpBWBwGu9uwEMRcaSkTwFzIuKzRQ7RzJqR1DMi3ih2HNsTSZ+IiFXFjsPyb3vr4xUR8WFEzI2IiUBv4L+Bo4G/Fje0Nu2QNjd2Ian12jMt3xnYsWhR5a6xSXtnkmMgIv5GO49d0p6Spkh6SdKq9LE4Leta7Pi2hqSHix1DWyTtIelqSXekTeqZ8/67WHFtjqR/knSTpBslfULSFZJekHSXpF7Fjq8tksqbPT4B1ErqJqm82PG1RtLRGc/3lDRD0vOS7pT0yWLGtjnpd0j39PkQSX8FnpH0qqR/LnJ4rZL0J0mXpN1HSkr6Oj8u6eeS+kh6VNLbkv4oqaAVANtb4rXJfSIjYn1E3B8RpwKfKlJMuZgBvETSTHcxcLekn5LcBWB2MQPLwS3AHyVNB/4ATAOQ1AN4q5iB5eAukmbRIyPiExHxCWB4WnZ3USNrg6SDWnkcDAwudnyb8TOSz+kvgbGSfilp53TeocULa7NuAxYB9cDjwPvAscBvgZ8UL6ycvElS89/4mA/sBfwpfd5e/TDj+bXACpJuDX8Ebi5KRLk7NiIab878n8ApEVEJVJMcS3vVDegKPC6pVtIFknoXO6gc/TfwI+BB4PfAzRGxJ3BROq9gtremxn0i4v8VO46t0XhyR8TytLblS8DfIqK2uJFtnqSBQBXwYkS8VOx4ciXp5YgYsKXzik3SRpI+ddluSH9oROxa4JByJmlhRAzOmL6YpG/gKODRiDioaMG1QdKzjc3mkv4WEZ/KmLfJMbU3kv4PyffJdyPihbRsSUR8priRtU3SnxrPhyznTXt/zV8C9k/v5PJ0RByaMe+FiDigiOG1qtlr/gXgVOAkkoFesyKi3V7FfjOf0aZ5hbBdjWos1aQLkoQr4/ka4J4ihrNF0ltCFfO2UFvrVUnfA2ZGxOsAaRPGGSQ1G+3VYuDfIqKu+QxJ7TlugJ0l7RARHwJExA8kLQN+QzKopL3KbD24vdm8ToUMZEtFxH9Jmg1cn54flwOl8Iu8p6QLSX5g7CFJ8VFNQntvzbkReEjSFKBG0g18NGBqYVEjy1FE/Bb4raRvktTUnUL7vn3QPyR9maSrTkg6ISLuS5t2NxYykO0q8TLbQqeQVEM/KalnWvY6yU3d/6VoUW3eFbT+j+ebBYxja/wvcBTw68aCiJgp6XXgx0WLavPmSOocEe9GxCWNhZIqgZeLGFdOImIZ8C+SvgI8StKXtL37KWmfUWAm0B1YKemfaOfJS0T8OB14dC6wD8n/4n2A+4D/KGZsm9Gi8iIiNgI16aM9O4ekqfFDYARwrqTbSAasnVXIQLarpkazbUXS1yLiZ8WOY0uVatxQurGXWtySdgX2jogXSy32RqUaN5Ru7KUaNxQ+dideZluheR+BUlGqcUPpxl6qcUPpxl6qcUPpxl6qcUPhY3dTo1krJD3f2iyg3Q5XL9W4oXRjL9W4oXRjL9W4oXRjL9W4oX3F7sTLrHWfJOkLsLpZuUiGI7dXpRo3lG7spRo3lG7spRo3lG7spRo3tKPYnXiZte4BoHNEtOioK+mJwoeTs1KNG0o39lKNG0o39lKNG0o39lKNG9pR7O7jZWZmZlYg7f1aJ2ZmZmYdhhMvM9vuSeok6XwlN6I3M8sbJ15mVpIkvfv/27uDFxujMI7j3x8WY6Owo8ashg1ZSE1NSilbYaFQl62i/A0iKQtNNhZkVpqlHWlKzFZhwdJGSlkRih6Le+imGU2Dl7n3+9ncc+77dM55N7fnnvfe8/zU7yWZWeFw54H3VfVlibm2JFk11SIk/b/8didpJCVZ12rlrQHeVNXsL+JeA0e7XaGkYeSOl6Shk2RbkgdJnrbX8fb+rSRXk8wDl5PsBR4B55MsJNne4npJ5pLcBe4lmUjyvF0bS3IzybMkT5Ls/1f3KWn1ccdL0mq1PsngX8M30a+jCTAD3G51Hk8D14BD7dokcKCqvibZAOxrO18HgYvAkRY3BeyqqndJJgbmOQNQVTuT7KCfmE1W1ae/cI+ShoyJl6TV6mNV7f7eSdID9rTuFHC4tWfpF8f9bq4V9oV+keUbSbbSP0hx80Dc/ap6t8i807SC3VX1Iskr+sncUidjS9IPPmqUNAoGDyz8MNC+AMxX1TRwAhhbIm5Q/vDaJI0QEy9Jw2gBONbax+n/jmsxG4G3rd1b5tgP25gkmQTGgZcrWqWkkWPiJWkYnQVOtcK4J4FzS8RdAS4leczyPw+vA2uTPAPuAL2q+vy7C5Y0GiwZJEmS1BF3vCRJkjpi4iVJktQREy9JkqSOmHhJkiR1xMRLkiSpIyZekiRJHTHxkiRJ6sg3n+BZ4uRcNWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_cchl'))]['HOUR_OPEN'].value_counts().sort_index())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_ct'))]['HOUR_OPEN'].value_counts().sort_index())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_cce'))]['HOUR_OPEN'].value_counts().sort_index())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_cca'))]['HOUR_OPEN'].value_counts().sort_index())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_biblioteca') | (df['STI'] == 'biblioteca'))]['HOUR_OPEN'].value_counts().sort_index())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### O turno da manhã contem maior fluxo de ocorrencias, em destaque os horarios de 10 as 12. A tarde, as 15h, ocorre um pico, porem menos relevante em comparação com o turno da manhã."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show the graphic bars to above result \n",
    "ndf = round(df[((df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015))]['OPEN_MONTH_1'].value_counts().sort_index()/5)\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências precificáveis por meses em média\")\n",
    "plt.xlabel(\"Mês\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show the graphic bars to above result \n",
    "ndf = round(df[((df['HAS_VALUE'] != 'P') & (df['OPEN_YEAR_1'] >= 2015))]['OPEN_MONTH_1'].value_counts().sort_index()/5)\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências não precificáveis por meses em média\")\n",
    "plt.xlabel(\"Mês\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show the graphic bars to above result \n",
    "ndf = round(df[(df['OPEN_YEAR_1'] >= 2015)]['OPEN_MONTH_1'].value_counts().sort_index()/5)\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências gerais por meses em média\")\n",
    "plt.xlabel(\"Mês\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>DATA_OPEN</th>\n",
       "      <th>DATA_CLOSE</th>\n",
       "      <th>HOUR_OPEN</th>\n",
       "      <th>HOUR_CLOSE</th>\n",
       "      <th>MIN_OPEN</th>\n",
       "      <th>MIN_CLOSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18/4/2012</td>\n",
       "      <td>26/4/2012</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/4/2014</td>\n",
       "      <td>23/4/2014</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/10/2011</td>\n",
       "      <td>24/10/2011</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24/10/2011</td>\n",
       "      <td>26/10/2011</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24/10/2011</td>\n",
       "      <td>26/10/2011</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>11</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15797</th>\n",
       "      <td>13/9/2019</td>\n",
       "      <td>17/9/2019</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15798</th>\n",
       "      <td>13/9/2019</td>\n",
       "      <td>13/9/2019</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15799</th>\n",
       "      <td>16/9/2019</td>\n",
       "      <td>99/99/9999</td>\n",
       "      <td>16</td>\n",
       "      <td>-1</td>\n",
       "      <td>31</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15800</th>\n",
       "      <td>17/9/2019</td>\n",
       "      <td>99/99/9999</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>35</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15801</th>\n",
       "      <td>18/9/2019</td>\n",
       "      <td>99/99/9999</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>54</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15802 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATA_OPEN  DATA_CLOSE  HOUR_OPEN  HOUR_CLOSE  MIN_OPEN  MIN_CLOSE\n",
       "0       18/4/2012   26/4/2012          9           9        15          0\n",
       "1       23/4/2014   23/4/2014          8           9        13          4\n",
       "2      24/10/2011  24/10/2011         15          15        43         59\n",
       "3      24/10/2011  26/10/2011         13          17        20          6\n",
       "4      24/10/2011  26/10/2011         12           9        13         11\n",
       "...           ...         ...        ...         ...       ...        ...\n",
       "15797   13/9/2019   17/9/2019         15           8        23         21\n",
       "15798   13/9/2019   13/9/2019         16          17        21         47\n",
       "15799   16/9/2019  99/99/9999         16          -1        31         -1\n",
       "15800   17/9/2019  99/99/9999         17          -1        35         -1\n",
       "15801   18/9/2019  99/99/9999          8          -1        54         -1\n",
       "\n",
       "[15802 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['DATA_OPEN','DATA_CLOSE','HOUR_OPEN','HOUR_CLOSE','MIN_OPEN','MIN_CLOSE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "hour_efic = []\n",
    "for D_open, D_close, H_open, H_close, M_open, M_close  in df[['DATA_OPEN','DATA_CLOSE','HOUR_OPEN','HOUR_CLOSE','MIN_OPEN','MIN_CLOSE']].values.tolist():\n",
    "    if H_close == -1:\n",
    "        hour_efic.append(-1)\n",
    "    else:\n",
    "        dtO = datetime.strptime(D_open +\" \"+ str(H_open) +\" \"+ str(M_open), '%d/%m/%Y %H %M')\n",
    "        dtC = datetime.strptime(D_close +\" \"+ str(H_close) +\" \"+ str(M_close), '%d/%m/%Y %H %M')\n",
    "        hour_efic.append(((dtO - dtC).seconds/60)/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.250000\n",
       "1        23.150000\n",
       "2        23.733333\n",
       "3        20.233333\n",
       "4         3.033333\n",
       "           ...    \n",
       "15797     7.033333\n",
       "15798    22.566667\n",
       "15799    -1.000000\n",
       "15800    -1.000000\n",
       "15801    -1.000000\n",
       "Name: HOUR_EFIC, Length: 15802, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HOUR_EFIC'] = hour_efic\n",
    "df['HOUR_EFIC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016666666666666666"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# min of wasted time to solve a problem in general\n",
    "df[df['HOUR_EFIC'] > 0]['HOUR_EFIC'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STI\n",
       "biblioteca        0.016667\n",
       "campus            0.066667\n",
       "hu                0.950000\n",
       "sti_biblioteca    0.166667\n",
       "sti_cca           0.016667\n",
       "sti_cce           0.050000\n",
       "sti_cchl          0.016667\n",
       "sti_central       0.016667\n",
       "sti_ct            0.033333\n",
       "Name: HOUR_EFIC, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# min of wasted time to solve a problem in general\n",
    "df[(df['HOUR_EFIC'] > 0) & (df['STI'] != '0')].groupby(['STI'])['HOUR_EFIC'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# min of wasted time to solve a problem in general\n",
    "ndf = df[(df['HOUR_EFIC'] > 0) & (df['STI'] != '0')].groupby(['STI', 'ESPC'])['HOUR_EFIC'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STI         ESPC         \n",
       "biblioteca  fonte            0.733333\n",
       "            hd               1.666667\n",
       "            instalacao       1.466667\n",
       "            nobreak          5.433333\n",
       "            placa            1.483333\n",
       "                               ...   \n",
       "sti_ct      nobreak          2.416667\n",
       "            placa            0.033333\n",
       "            placa de rede    7.366667\n",
       "            ram              2.100000\n",
       "            software         0.050000\n",
       "Name: HOUR_EFIC, Length: 71, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ade395bd0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ndf['sti_biblioteca'].plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ade341d90>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ndf['sti_central'].plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ade524410>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ndf['sti_ct'].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Media de tempo gasto, por cada setor do STI, em relação a conclusão de problemas precificáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# min of wasted time to solve a princify problem \n",
    "ndf = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P')].groupby('STI')['HOUR_EFIC'].min()\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "plt.title(\"Tempo médio gasto para resolver problemas não precificáveis\")\n",
    "plt.xlabel(\"setor\")\n",
    "plt.ylabel(\"Tempo\")\n",
    "\n",
    "autolabel(ax.patches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RHVfPkrXFOwn4fGo6sqK+SFben6dtPwHsTdbYHt7ZObirM4GN0nnrD41WaMN7VVla8vK4mZlBNpwCWSPtfy86FjMrH9eAmZmZmeXMCZiZmZlZznwJ0szMzCxnrgEzMzMzy5kTMDMzM7Oc9ak7m6+++uoxfvz4osMwM7Ne6K233uLJJ59cND1//nzGjBnDGmssviFERDBt2jRmzZpFv379GD9+PEOGZONQv/rqq8yYMQOANddck9VWWw2zFXHXXXe9EhEN7wbTpxKw8ePHM3Xq1KLDMDOzXq6zs5O11lqLW265hXXWWTxc3BVXXMHJJ5/MFVdcwR133MGxxx7LHXfcwWuvvUZHRwfPP/88kth666259tprWXXVVQsshfV1kpre/sqXIM3MrHSuv/561ltvvSWSL4DLLruMQw45BElsv/32zJw5kxkzZnD11Vezyy67MGrUKFZddVV22WUXrrrqqoKitypwAmZmZqVz4YUXcsABByw1f/r06ay99uLbv44dO5bp06c3nW/WLk7AzMysVBYsWMDll1/Opz/96aWWNRp6SVLT+Wbt4gTMzMxK5corr2SrrbZaovF9zdixY5k2bfFtMp977jnGjBnTdL5ZuzgBMzOzUrngggsaXn4EmDx5Mueeey4Rwe23384qq6zCmmuuya677so111zD66+/zuuvv84111zDrrvumnPkViV9qhekmZlZd+bNm8e1117LaaedtmjeqaeeCsDnPvc5dt99d6644gomTJjAkCFDOOusswAYNWoU3/rWt9hmm20A+Pa3v82oUaPyL4BVRp+6FVFHR0d4GAozMzPrCyTdFREdjZYVdglS0kRJ99Y9Zkn6UlHxmJmZmeWlsEuQEfEosAWApP7AdODSouIxMzMzy0tvaYT/EeCJiGg6YqyZmZlZWfSWRvj7Axc0WiBpCjAFYNy4cXnGZGZmvdD44/9c2L6f/uEehe3byqXwGjBJA4HJwMWNlkfE6RHREREdo0c3vJ+lmZmZWZ9SeAIG7AbcHREvFh2ImZmZWR56QwJ2AE0uP5qZmZmVUaEJmKQhwC7A74uMw8zMzCxPhTbCj4h5wGpFxmBmZmaWt95wCdLMzMysUpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzgpNwCSNlHSJpEckPSzpfUXGY2ZmZpaHAQXv/2fAVRGxj6SBwJCC4zEzMzNru8ISMEkjgA8AhwFExAJgQVHxmJmZmeWlyEuQ6wIvA2dJukfSryQNLTAeMzMzs1wUmYANALYCfhkRWwJzgeO7riRpiqSpkqa+/PLLecdoZmZm1uOKTMCeA56LiDvS9CVkCdkSIuL0iOiIiI7Ro0fnGqCZmZlZOxSWgEXEC8A0SRPTrI8ADxUVj5mZmVleiu4F+X+A36QekE8Chxccj5mZmVnbFZqARcS9QEeRMZiZmZnlzSPhm5mZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpyAmZmZmeXMCZiZmZlZzpaZgEnaUdLQ9PwgST+RtE5P7FzS05Lul3SvpKk9sU0zMzOz3q6VGrBfAvMkbQ58DXgGOLcHY/hwRGwRER09uE0zMzOzXquVBOztiAhgL+BnEfEzYHh7wzIzMzMrr1YSsNmSvg4cBPxZUn9gpR7afwDXSLpL0pQe2qaZmZlZr9ZKArYfMB84MiJeANYC/m8P7X/HiNgK2A04RtIHuq4gaYqkqZKmvvzyyz20WzMzM7PiLDMBi4gXIuInEfHXNP1sRPRIG7CIeD79fQm4FNi2wTqnR0RHRHSMHj26J3ZrZmZmVqhWekFuL+nvkuZIWiCpU9IbK7pjSUMlDa89Bz4KPLCi2zUzMzPr7Qa0sM4pwP7AxUAHcAiwfg/sew3gUkm1OH4bEVf1wHbNzMzMerVWEjAi4nFJ/SOiEzhL0m0ruuOIeBLYfEW3Y2ZmZtbXtJKAzZM0ELhX0knADGBoe8MyMzMzK69WekEeDPQHvgDMBdYGPtXOoMzMzMzKbJk1YBHxTHr6JvC99oZjZmZmVn5NEzBJF0XEvpLuJxswdQkRsVlbIzMzMzMrqe5qwI5Nf/fMIxAzMzOzqmiagEXEjPS0HzAjIt4CkLQy2RASZmZmZrYcWmmEfzGwsG66M80zMzMzs+XQSgI2ICIW1CbS84HtC8nMzMys3FpJwF6WNLk2IWkv4JX2hWRmZmZWbq0MxPo54DeSTgEETCO7HZGZmZmZLYdWxgF7Athe0jBAETG7/WGZmZmZldcyEzBJg8hGvh8PDEg3zyYiTmhrZGZmZmYl1colyMuAN4C7gPntDcfMzMys/FpJwMZGxMfaHomZmZlZRbTSC/I2SZu2PRIzMzOzimilBmwScJikp8guQQoI3wvSzMzMbPm0koDt1vYozMzMzCpkmZcgI+IZYG1gp/R8XiuvMzMzM7PGmiZSkjZMf78DHAd8PS1aCTi//aGZmZmZlVPDBEzSTsCRaXJvYDIwFyAingeG5xKdmZmZWQk1qwEbTEq4gAUREUAASBqaR2BmZmZmZdWwEX5EXCHppTR5kaTTgJGSjgKOAM7IK0AzMzOzsmnaCzIipqa//yVpF2AWMBH4dkRc21MBSOoPTAWmR8SePbVdMzMzs96q22EoUnJ0dUTsDPRY0tXFscDDwIg2bd/MzMysV+l2OImI6ATmSVqlHTuXNBbYA/hVO7ZvZmZm1hu1MhDrW8D9kq5lccN8IuKLPbD/nwJfw70qzczMrEJaScD+nB49StKewEsRcZekD3Wz3hRgCsC4ceN6OgwzMzOz3LXSBmyXiDioDfveEZgsaXeyYS9GSDq/674i4nTgdICOjo5oQxxmZmZmuWqlDdhoSQN7escR8fWIGBsR44H9gb+0KdEzMzMz61VauQT5NHCrpMtZsg3YT9oVlJmZmVmZtZKAPZ8e/WhTY/mIuBG4sR3bNjMzM+ttlpmARcT3ACQNzyZjTtujMjMzMyuxbtuAAUjaRNI9wAPAg5LukrRx+0MzMzMzK6dlJmBkPRC/EhHrRMQ6wFfxvSDNzMzMllsrCdjQiLihNpHaaw1tW0RmZmZmJddKI/wnJX0LOC9NHwQ81b6QzMzMzMqtlRqwI4DRwO/TY3Xg8HYGZWZmZlZmrfSCfB3oifs+mpmZmRmt9YK8VtLIuulVJV3d3rDMzMzMyquVS5CrR8TM2kSqEXtX+0IyMzMzK7dWErCFksbVJiStA/im2GZmZmbLqZVekN8EbpF0U5r+ADClfSGZmZmZlVsrjfCvkrQVsH2a9eWIeKW9YZmZmZmVVys1YAA7kNV81fypDbGYmZmZVUIrvSB/CBwLPJQex0o6sd2BmZmZmZVVKzVguwNbRMRCAEnnAPcAX29nYGZmZmZl1UovSICRdc9XaUcgZmZmZlXRSg3YicA9km4ARNYWzLVfZmZmZsuplV6QF0i6EdiGLAE7LiJeaHdgZmZmZmXVUi/IiJgBXN7mWMzMzMwqodU2YGZmZmbWQ5yAmZmZmeWspQRM0iRJh6fnoyW9p71hmZmZmZVXKwOxfgc4jsU9H1cCzl/RHUsaLOlOSf+Q9KCk763oNs3MzMz6glZqwPYGJgNzASLieWB4D+x7PrBTRGwObAF8TNL2y3iNmZmZWZ/XSgK2ICICCABJQ3tix5GZkyZXSo/oiW2bmZmZ9WatJGAXSToNGCnpKOA64Iye2Lmk/pLuBV4Cro2IO3piu2ZmZma9WSsDsf6XpF2AWcBE4NsRcW1P7DwiOoEtJI0ELpW0SUQ8UL+OpCnAFIBx48b1xG7NzMzMCtXqQKzXAj2SdDXZ/sw02v7HgAe6LDsdOB2go6PDlyjNzMysz2uagEmaTTdtsiJixIrsWNJo4J8p+VoZ2Bn40Yps08zMzKwvaJqARcRwAEknAC8A55HdC/JAeqYX5JrAOZL6k7VFuygi/tQD2zUzMzPr1Vq5BLlrRGxXN/1LSXcAJ63IjiPiPmDLFdmGmZmZWV/USi/ITkkHph6L/SQdCHS2OzAzMzOzsmolAfsXYF/gxfT4dJpnZmZmZsuhlWEongb2an8oZmZmZtXQ0s24zczMzKznOAEzMzMzy5kTMDMzM7OcLTMBk7SGpDMlXZmmN5J0ZPtDMzMzMyunVmrAzgauBsak6f8FvtSugMzMzMzKrpUEbPWIuAhYCBARb+NxwMzMzMyWWysJ2FxJq5HuCylpe+CNtkZlZmZmVmKt3IroK8DlwHqSbgVGA/u0NSozMzOzEmtlINa7JX0QmEh2M+5HI+KfbY/MzMzMrKSaJmCSPtlk0QaSiIjftykmMzMzs1Lrrgbs4+nvu4AdgL+k6Q8DNwJOwMzMzMyWQ9MELCIOB5D0J2CjiJiRptcEfp5PeGZmZmbl00ovyPG15Ct5EdigTfGYmZmZlV4rvSBvlHQ1cAHZUBT7Aze0NSozMzOzEmulF+QXUoP896dZp0fEpe0Ny8zMzKy8WqkBq/V4dKN7MzMzsx7Qys24t5f0d0lzJC2Q1ClpVh7BmZmZmZVRK43wTwEOAB4DVgY+A5zczqDMzMzMyqzVS5CPS+ofEZ3AWZJua3NcZmZmZqXVSgI2T9JA4F5JJwEzgKErumNJawPnAu8GFpI17v/Zim7XzMzMrLdr5RLkwUB/4AvAXGBt4FM9sO+3ga9GxHuB7YFjJG3UA9s1MzMz69VaGYbimfT0TeB7PbXjNLjrjPR8tqSHgbWAh3pqH2ZmZma9UXc3476fbODVhiJis54KQtJ4YEvgjp7appmZmVlv1V0N2J7p7zHp73np74HAvJ4KQNIw4H+AL0XEUsNbSJoCTAEYN25cT+3WzMzMrDBN24BFxDPp8uOOEfG1iLg/PY4Hdu2JnUtaiSz5+k0a7LVRHKdHREdEdIwePbondmtmZmZWqFYa4Q+VNKk2IWkHeqYXpIAzgYcj4icruj0zMzOzvqKVYSiOBH4taZU0PRM4ogf2vSNZD8v7Jd2b5n0jIq7ogW2bmZmZ9Vqt9IK8C9hc0ghAEfFGT+w4Im4B1BPbMjMzM+tLuusFeVBEnC/pK13mA+DLhmZmZmbLp7sasFo7r+ENljUdnsLMzMzMutc0AYuI09LT6yLi1vplknZsa1RmZmZmJdZKL8iTW5xnZmZmZi3org3Y+4AdgNFd2oGNILs3pJmZmZkth+7agA0EhqV16tuBzQL2aWdQZmZmZmXWXRuwm4CbJJ1dd0NuMzMzM1tBrQzEOkjS6cD4+vUjYqd2BWVmZmZWZq0kYBcDpwK/AjrbG46ZmZlZ+bWSgL0dEb9seyRmZmZmFdHKMBR/lHS0pDUljao92h6ZmZmZWUm1UgN2aPr7b3XzAli358MxMzMzK79Wbsb9njwCMTMzM6uKVmrAkLQJsBEwuDYvIs5tV1BmZmZmZbbMBEzSd4APkSVgVwC7AbcATsDMzMzMlkMrjfD3AT4CvBARhwObA4PaGpWZmZlZibWSgL0ZEQuBtyWNAF7CDfDNzMzMllsrbcCmShoJnAHcBcwB7mxrVGZmZmYl1kovyKPT01MlXQWMiIj72huWmZmZWXm10gj/A43mRcTN7QnJzMzMrNxauQRZPwDrYGBbskuRvhm3mZmZ2XJo5RLkx+unJa0NnNS2iMzMzMxKrpVekF09B2zSEzuX9GtJL0l6oCe2Z2ZmZtYXtNIG7GSyez9ClrBtAfyjh/Z/NnAKHtTVzMzMKqSlYSjqnr8NXBARt/bEziPiZknje2JbZmZmZn1FKwnYxcCE9PzRiJjfxnjMzMzMSq9pGzBJK0n6KTANOAs4B3hS0vFp+ZZ5BChpiqSpkgDCTCQAAByHSURBVKa+/PLLeezSzMzMrK26a4T/Y2AYMD4ito6ILYH3AutK+iXw+zwCjIjTI6IjIjpGjx6dxy7NzMzM2qq7S5C7A+tHRK0BPhExS9LngVeA3dodnJmZmVkZdVcDtrA++aqJiE7g5Yi4fUV3LukC4G/AREnPSTpyRbdpZmZm1tt1VwP2kKRDImKJISIkHQQ83BM7j4gDemI7ZmZmZn1JdwnYMcDvJR1BduuhALYBVgb2ziE2MzMzs1JqmoBFxHRgO0k7ARsDAq6MiOvzCs7MzMysjFq5F+RfgL/kEIuZmZlZJSzPvSDNzMzMbAU4ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwM7MSuuqqq5g4cSITJkzghz/84VLL58+fz3777ceECRPYbrvtePrppxctO/HEE5kwYQITJ07k6quvzjFqs+pwAmZmVjKdnZ0cc8wxXHnllTz00ENccMEFPPTQQ0usc+aZZ7Lqqqvy+OOP8+Uvf5njjjsOgIceeogLL7yQBx98kKuuuoqjjz6azs7OIophVmpOwMzMSubOO+9kwoQJrLvuugwcOJD999+fyy67bIl1LrvsMg499FAA9tlnH66//noigssuu4z999+fQYMG8Z73vIcJEyZw5513FlEMs1JzAmZmVjLTp09n7bXXXjQ9duxYpk+f3nSdAQMGsMoqq/Dqq6+29FozW3FOwMzMSiYilponqaV1Wnmtma04J2BmZiUzduxYpk2btmj6ueeeY8yYMU3Xefvtt3njjTcYNWpUS681sxXnBMzMrGS22WYbHnvsMZ566ikWLFjAhRdeyOTJk5dYZ/LkyZxzzjkAXHLJJey0005IYvLkyVx44YXMnz+fp556iscee4xtt922iGKYldqAogMwM7OeNWDAAE455RR23XVXOjs7OeKII9h444359re/TUdHB5MnT+bII4/k4IMPZsKECYwaNYoLL7wQgI033ph9992XjTbaiAEDBvDzn/+c/v37F1wis/JRo+v9vVVHR0dMnTq16DDMzKxA44//c2H7fvqHexS2b+t7JN0VER2NlhV6CVLSxyQ9KulxSccXGYuZmZlZXgpLwCT1B34O7AZsBBwgaaOi4jEzMzPLS5E1YNsCj0fEkxGxALgQ2KvAeMzMzMxyUWQCthYwrW76uTTPzMzMrNSK7AXZaGS/pXoESJoCTAEYN27ccu+sqo02Xe78udz5q2q5q6qq73lVj/OylrvIGrDngLXrpscCz3ddKSJOj4iOiOgYPXp0bsGZmZmZtUuRCdjfgfUlvUfSQGB/4PIC4zEzMzPLRWGXICPibUlfAK4G+gO/jogHi4rHzMzMLC+FjoQfEVcAVxQZg5mZmVnefC9IMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5wNKDqA3uCfr07jlSt+yoIXn2Dk+w9hle0+2Xi9mS/wyuUnsfCt2QxcYwKr7/kV1H8l4u1/8sqff8KCFx6n38rDGb3XcQxYZY2cS/HOudwud8P1XO5SlNuqparHeV8ut2vAgH6DhzNq588yYtvGH1zNzBvPZkTHXqw15Qz6DR7KnPuuBWDOfdfQb/BQ1vrsGYzo2IvXbzw7h6hXnMvtcjficpej3FYtVT3O+3K5nYAB/YeOZNCaG6B+/ZuuExG89ex9DNlwEgDDNvkI8/73bwDMe+x2hm3yEQCGbDiJt575BxHR/sBXkMvtcnflcpen3FYtVT3O+3K5nYC1aOGbs+g3aOiiD7n/8NXpnPMqAJ1zXqX/8NEAqF9/+g0awsI3ZxUWa09yuV1ul7u85bZqqepx3lvL7QRshSj70yhZlnKNJF8u99KLXO7yqWq5rVqqepwXX+7KNsKfffefmP2PqwF41z7fZcDw1bpdv9/KI1g4fy6xsBP160/n7FfoP2wUAP2Hr0bn7JcZMGJ1YmEnC+fPo9/g4W0vw/JwuV3u7rjcfbvcVi1VPc7LUu7KJmDDt9qT4Vvt2fL6khg8blPmPXILQzf6IHMeuJ4h628PwJD1t2POA9czaK33Mu+RWxg8bjPUS385uNytcbld7r5YbquWqh7nZSm3L0ECnXNe57mfH8qsv/+BN/72O577+aEsnD8PgBcv/g5vz86uFY/80OHMmvoHpp92FAvfnM2wzT4KwLDNPsrCN2cz/bSjmDX1D4z80GFFFeUdcbldbpe7vOW2aqnqcd6Xy62+0MuhpqOjI6ZOnbpcrx1//J97OJrWPf3DPQrbt8udP5c7f1Utt1VLVY/zvlxuSXdFREejZYXUgEn6tKQHJS2U1DAwMzMzs7Iq6hLkA8AngZsL2r+ZmZlZYQpphB8RDwO9toGfmZmZWTu5Eb6ZmZlZztrWCF/SdcC7Gyz6ZkRclta5EfjXiGjasl7SFGAKwLhx47Z+5pln2hCtmZmZWc/qrhF+2y5BRsTOPbSd04HTIesF2RPbNDMzMyuSL0GamZmZ5ayoYSj2lvQc8D7gz5KuLiIOMzMzsyIU1QvyUuDSIvZtZmZmVjRfgjQzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLmRMwMzMzs5w5ATMzMzPLWdvuBdkOkl4GiroZ5OrAKwXtu0gud7W43NXicleLy52/dSJidKMFfSoBK5Kkqc1uqFlmLne1uNzV4nJXi8vdu/gSpJmZmVnOnICZmZmZ5cwJWOtOLzqAgrjc1eJyV4vLXS0udy/iNmBmZmZmOXMNmJmZmVnOnICZmZmZ5cwJmJlZRUhaQ9KZkq5M0xtJOrLouPKQyr5neryr6HjMnIB1Q9IkSYen56MlvafomKy9JE2W9F/p8fGi48mDpK0bzKtE2SvobOBqYEya/l/gS4VFkxNJ+wJ3Ap8G9gXukLRPsVHlS9IISaNqj6LjaTdJQyR9S9IZaXp9SXsWHVc9N8JvQtJ3gA5gYkRsIGkMcHFE7FhwaG0j6X6g6QEREZvlGE7uJJ0IbAv8Js06AJgaEV8vLqr2k3Q3cGhE3J+mDwC+FBHbFRtZe0naHjgZeC8wEOgPzI2IEYUG1kaS/h4R20i6JyK2TPPujYgtio6tnST9A9glIl5K06OB6yJi82Ijaz9JnwVOAN5k8fk9ImLd4qJqP0m/A+4CDomITSStDPytNx3rA4oOoBfbG9gSuBsgIp6XNLzYkNqu9uvgmPT3vPT3QGBe/uHkbg9gi4hYCCDpHOAeoNQJGLAPcImkA4FJwCHAR4sNKRenAPsDF5P92DoEmFBoRO03V9JqpC/ilIS+UWxIuehXS76SV6nOFaB/BTaOiKrdgmi9iNgv/aAkIt6UpKKDqucErLkFERGSaieqoUUH1G4R8QyApB271PQdL+lWsl9RZTcSeC09X6XIQPISEU9K2h/4AzAN+GhEvFlwWLmIiMcl9Y+ITuAsSbcVHVObfQW4HFgv/U+PJkvAy+4qSVcDF6Tp/YArC4wnT09QjR/QXS1ItV617/D1gPnFhrQkJ2DNXSTpNGCkpKOAI4BfFRxTXoZKmhQRt0CWkAGlT0CBE4F7JN0ACPgAJa79anDJeRTZZbg7JJX+kjMwT9JA4F5JJwEzKPlxHhF3S/ogMJHsGH80Iv5ZcFhtFxH/JumTZDW8Ak6PiEsLDisvXwduk3QHdQlIRHyxuJBy8R3gKmBtSb8BdgQOKzSiLtwGrBuSdiG7FCPg6oi4tuCQcpEaZf+arAYoyC5RHB4R9xQaWA4krQlsQ/aZ3xERLxQcUttIWqe75bUa0bJK5X+RrP3Xl8mO919ExOOFBtYGKfloKiJ+n1csRUmf9/oRcZ2kIUD/iJhddFztJulO4BbgfmBhbX5EnFNYUG2WLjWOJav5257sfH57b7sM6wSsCUk/iojjljWvjCQNIrsssR6wKjCTrNFm6S9BSloVWB8YXJsXETcXF1F+Utf8+nI/W2A4bZeaFbxZ1+avPzAoIkp3uUbSWenpu4AdgL+k6Q8DN0ZEtwlaX5euYkwBRkXEepLWB06NiI8UHFrbSbotInYoOo68SborIpbq4d2bVKUR4vLYpcG83XKPohiXAR8H3gKeA+YAcwuNKAeSPgPcTNZN/3vp73eLjCkPaeiNx4CngJuAp6lG+5jrgSF10ysD1xUUS1tFxOERcThZjfZGEfGpiPgUsHHBoeXlGLJLULMAIuIxsmS0Cm6QNEXSmlUahgK4XdI2RQfRHbcB60LS54GjgXUl3Ve3aDhwazFR5W5sRHys6CAKcCzZ5cfbI+LDkjYkS8TK7j/Iqumvi4gtJX2YbAiOshscEXNqExExJ12aKrPxETGjbvpFYIOigsnR/IhYUOsEJ2kA3Qy5UzL/kv7Wt2cNoNTDUJDV7n5W0jNkFQgiu5LTa9q2OgFb2m/Jfv2fCBxfN392RLzW+CWlc5ukTWvjQlXIWxHxliQkDYqIRyRNLDqoHPwzIl6V1E9Sv4i4QdKPig4qB3MlbRURd8Oito9l7/15Y11vwCAbhuOGYkPKxU2SvgGsnNr2Hg38seCYchERVR1AvNdfsXIbsG6kNiFrUJeolrldTF2vuAFk7aCeJOs10+t+ObSDpEuBw8lGBt8JeB1YKSJ2LzSwNpN0HfAJ4IfAasBLwDZlbzeSLk9cCDyfZq0J7BcRdxUXVftJ2pushy/AzVXoDSipH3AkdZ2qgF9FRb4AJW0CbMSSbTzPLS6i/PTmtq1OwJqQ9AWy9j8vsrjnSKmTkKr3iquXuuqvAlwVEQuKjqedao3RydqEHkhW7t9ExKuFBpYDSSuxeEiGR6owJENVewNWVbqry4fIErAryGqGbomIUo//Jmky8GOy2269BKwDPBwRvabdoxOwJiQ9DmxXhS8hy6RRwR+sfRmlOx9sFBF3FBtZ+1X1S7lqNQNV6w1Y9durwaL3YHPgnojYXNIaZLV/pb7fa7r91E50adsaEVMKDm0RtwFrbhrVuEWHLfZLYKu66bkN5pVO/Zcy2dAjawGnAqX8Uq5pVjMAlDYBI+sNuC1wB2S9AdMlmrKq+u3VIGvbulDS25JGkNUGlb0BPvSBtq1OwJp7kqzB6p9ZcvTgnxQXkrWZ6tuEpJNWFf5HqvalXLMPi2sGDq/VDBQcU7tVqjdg1W+vlgYkvU/SSOAMsptTzwHuLDSwfMyUNIxsaKHfSHoJeLvgmJbgccCaexa4lmyU7OF1DyuvJyV9UdJK6XEsWSJedvPr27mV/Uu5Tm0Q1irVDHTtDXgx1egNOFTSpNqEpB0o+W2nIGu0DGwRETMj4lSy8S0PTWPCld1eZLWcXya7JdETZONb9hpV+HW/XCLie7CoHVDUjxdkpfU54P8B/06WgFxPdmmu7KraRX9qBWsGjifrDXg/8FmyS69lr/WDrMy/lrRKmp5Jdn/fKrhd0jYR8feIeLroYHK0H/DXNOhur7ztkhvhN5Ea555H1i4G4BXgkIh4sLiozHpe1bvoA0gaD4yIiPuWsWqflYbVOSciDio6lqKkmk5FRGXa90p6iGyw3V47IGk7SDqB7Obr44GpwF/JErJ7i4yrnhOwJiTdBnwzIm5I0x8CflD2sZGqTNJgskRkY5bsFVfaX8pV/FKW1G2nitrArGWUBmH9eNmHVqmRdFBEnC/pK42WV6FNb7PhhaoyrJCklYGjgH8F1oqI/gWHtIgvQTY3tJZ8AUTEjWm8JCuv84BHgF3JGuceCDxcaERtFhGdkkZLGliVL2WysYFq6n+BKk3vlG84uXoauFXS5dTd37XEiUjtnF3Z9rtVSbS6kvTvZPf/HAbcQ5aA/bXQoLpwDVgTaVT0u1ncbfkgoCMiPlFcVNZOku5J48XcFxGbpUE6r46IMn8hI+k0sqE2qvKlDCz6ZXw02WWKIDs5/zIi3io0sDZKQ28spdbmtawkjY6Il4uOw/Ij6W6yXo9/Bm4iu8dvr/rfdg1YF5LOi4iDyU7G44Hfk/0yvonsNjVWXrVR0GemNoAvkB0DZfd8evSjWjUF5wCzyDpeQHYD8nOBfQuLqM3Knmh14zZJTwG/A34fEa8XHZC1V0RslTrRTSLr/XmGpBcjYtIyXpobJ2BL2zpdMz+U7G7qtcsSpOdWXqdLWhX4Fllt0DDg28WG1H51PX5HZJPlHwE/mRgRm9dN35BGz7aSiYj1JW1LdvPxb6aG6RdGxPkFh2Ztkn5Evx/4INBBNri6L0H2ZpK+CHyebDyg6fWLyL6cyj5OkFWMpA7gLBbXfr0BHFGBm1KfTXYbntvT9HZkYyQdXWhg1laSVgd+AhzYmxpkW89Kg6jfRHZ3i7/3xvu8OgFrQtIvI+LzRcdh7desh1RNBdpC3QccExF/TdOTgF9UoJv6w2Q34n42zRpH1uliIRXopl8lqXZ3b7IasPWAS4GLyv4jo8okfSkiftpl3rER8bOiYurKCZhVXrOGyTVlbzcj6dYut2lpOK9smnXPrylj7zFJG5Dd33SNiNhE0mbA5Ij4fsGhtVVq//UHsqTrb0XHY+0n6e6I2KrLvHsiYsuiYurKCZhZRdWNh3UwMAS4gKy9437A6xHxzaJis/aQdBPwb8BptS8iSQ9ExCbFRtZekpa4z6uVl6QDgH8ha/91c92i4UBnROxcSGANuBG+WVLBgVh/3GW6vibQX1blNCQi7qzdjDvpVTcobgcnX5VyNzADWJ0lz3GzgV51pwsnYGaLVWog1oj4cNExWO5ekbQeKcGWtA/Zl5VZWVyQhqB4IiJuKjqY7jgBM1tsQkR8WtJeEXGOpN+S3Rex1NINir8DfCDNugk4oUr3y6uQY4DTgQ0lTQeeIhtk2qwsBko6FHifpE92XRgRvy8gpoacgJktVtWBWH8NPMDiAUgPJhuWYqmTl/VtEfEksHO6rVq/qoz5VtXOBxX1ObKrFyOBj3dZFmSDq/cKboRvlkj6DPA/wKbA2WQDsX4rIk4rMq52k3RvRGyxrHnWd3molWp2PqgySUdGxJlFx9Ed14CZLXZ9ukXJzWQD8SLpPcWGlIs3JU2KiFsAJO0IvFlwTNazaoPsTgS2IbvTA2Q1BDc3fEW5VLLzQcWdlwZWr29acWpvGpDVCZjZYv9DdlPqepcAWxcQS54+D5yT2oIBvE52Ky4ribrbTV0DbFW79Cjpu8DFBYaWF3c+qJ5fACulv5A1rfgl8JnCIurCCZhVnqQNyYaeWKVLo80R1A1HUWIPAyeRjRA+kuxWRJ+gl3XZth4xDlhQN72AarRzdOeD6tmmy71e/9Lb7vXqBMwsuyyzJ0s32pwNHFVIRPm6DJhJNn7O9GWsa33becCdki4lqw3aGzin2JDar6qdDyquU9J6EfEEgKR1gc6CY1qCG+GbJZLeV8XblLgxcrWkOyC8P03eHBH3FBlPO1W980GVSdqJrDPVk2nWeODwiLihqJi6cg2Y2WJ7S3qQrAH6VcDmwJci4vxiw2q72yRtGhH3Fx2ItV9E3E1W21kFVe98UGWrAZuQJV57ATuQNa/oNVwDZpbUhl6QtDdZG6gvAzd0aUdQOpIeAiaQtYuZD4js7i2bFRqYWQ9JnQ8+Vdf5YDhwcUR8rNjIrF0k3RcRm0maBPyA7LZE34iI7QoObRHXgJkttlL6uzvZ7Sxe69Jtvax2KzoAszaraueDKqu199qDbPiJy1Kv317DCZjZYn+U9AjZJcijJY0G3io4praLiGeKjsGszSrZ+aDipks6DdgZ+JGkQUC/gmNagi9BmtWRtCowKyI6JQ0BRkTEC0XHZdYTJG0PnAy8FxgI9AfmRsSIQgPLQZU6Hxik8/fHgPsj4jFJawKbRsQ1BYe2iBMwszqSdiC7NLGodjgizi0sILMeJGkqsD/Z4KsdwCFkN6H/ZqGBmVWQL0GaJZLOIxuM9F4Wtx8IwAmYlUZEPC6pf0R0AmdJuq3omMyqyAmY2WIdwEbhamErr3mSBgL3SjqJ7HY8QwuOyaySelWDNLOCPQC8u+ggzNroYLLz/heAucDawKcKjcisotwGzCyRdAOwBXAn2XhYAETE5MKCMutB6VY8b0bEwjTdHxgUEfOKjay9qtz5wHovX4I0W+y7RQdg1mbXk3XLn5OmVwauIRslvMxOoUHng0IjsspzAmaWRMRNRcdg1maDI6KWfBERc1J3/dJz5wPrbZyAWeVJuiUiJkmaTdbrcdEislvy+DKFlcVcSVul+0EiaWuygYfLzp0PrNdxGzAzs4qQtA1wIfB8mrUmsF9E3FVcVO0naR3gRbL2X18GVgF+ERGPFxqYVZoTMDOzCpG0EjCRrIb3kYj4Z8EhtV1VOx9Y7+ZhKMzMqmUisBGwJXCApEMKjicP1wP1bd1WBq4rKBYzwG3AzMwqQ9J3gA+RJWBXALsBt1D+uz1UtvOB9V6uATMzq459gI8AL0TE4cDmwKBiQ8rF3HQzbqBSnQ+sF3MNmJlZdbwZEQslvS1pBPASsG7RQeXgS8DFkpbofFBgPGZOwMzMKmSqpJHAGcBdZAOy3llsSO0XEX+XtCEV63xgvZt7QZqZVZCk8cCIiLiv4FByIWkTsrZvg2vzIqLsbd+sF3MCZmZWcvXtnxqpDcxaVs06H0TEPkXGZdXmBMzMrOTSjeabiYjYKbdgCiDpfrIOB/dExOaS1gB+FREfLzg0qzC3ATMzK7mI+HDRMRSsqp0PrBdzAmZmVhGSBgNHA5PI7nv6V+DUiHir0MDar5KdD6x38yVIM7OKkHQRMBs4P806AFg1Ij5dXFT5qlrnA+u9nICZmVWEpH9ExObLmlcWVe98YL2bL0GamVXHPZK2j4jbASRtB9xacEzt9ONulgVQ6s4H1ru5BszMrCIkPUw2GOmzadY44GFgIVlvyM2Kis2sapyAmZlVhKR1ulseEc/kFUueKtz5wHoxJ2BmZlZq7nxgvZETMDMzK7WqdT6wvqFf0QGYmZm12T2Stq9NVKDzgfUBrgEzM7NSc+cD642cgJmZWalVtfOB9W5OwMzMzMxy5jZgZmZmZjlzAmZmlkjqL+lYSb5LiJm1lRMwM+vTJM3pMn2YpFOWc3NfAeZExNtN9jVG0iXLuW0zs0X8K8/MKk3SgIh4W1I/4IWIOK+b9Z4H9sk3QjMrI9eAmVlpSVpH0vWS7kt/x6X5Z0v6iaQbgB9J2ha4BfiKpNskTUzrHSbpYkl/BK6RNF7SA2nZYElnSbpf0j2SPlxUOc2s73ENmJn1dStLurduehRweXp+CnBuRJwj6Qjg/wGfSMs2AHaOiE5JI4APpJqwXYEfAJ9K670P2CwiXpM0vm4/xwBExKaSNiRL0Dbw/QXNrBVOwMysr3szIraoTUg6DOhIk+8DPpmenwecVPe6iyOiMz0fDpwhaS1AwGp1610bEa812O8k4GSAiHhE0jNkSd19K1YcM6sCX4I0syqpH/hwbt3z7wM3RMQk4CBgcJP16qmHYzOzCnECZmZldhuwf3p+IFk7r0ZWBV5Ozw9rcds3p20iaQOy29s8ulxRmlnlOAEzszL7InC4pPuAg4Fjm6z3f4ETJd1K6+fFXwD9Jd0P/A44LCLmr2jAZlYNvhWRmZmZWc5cA2ZmZmaWMydgZmZmZjlzAmZmZmaWMydgZmZmZjlzAmZmZmaWMydgZmZmZjlzAmZmZmaWMydgZmZmZjn7/yjBOOFXrSteAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_central'))].groupby('ESPC')['HOUR_EFIC'].min())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média - STI Central\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_central'))].groupby('ESPC')['ESPC'].count())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por tipo de problema - STI Central\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Stfv8VxP70e5zpwkTyD73C8m+X6a3cz2rwyPrtdtQGjkc+FSs461SIuJdsovrf5G191pG1vlkK2BGmucKsgvr75JdJM4nK6X/Uxs3dwLZhUjDX6vV+tY6ZVX7ZmbWGkk3knW0+G5O259K1uD5V3lsvymSBpBdpGwcvl+gWatc4mVmZmZWJk68zMzMzMrEVY1mZmZmZeISLzMzM7MyceJlZmZmViYV8ZT4rbbaKgYMGJB3GGZmZlYCEcGcOXPo0qULtbUf3HP6hRde4LXXXmOvvfZqcrnFixfz2mvZAzv69+/PlltuCcAbb7zB/PnZXYK22morPvzh9t4DvH1mzZr1akQ0+YSWiki8BgwYQF1dXd5hmJmZWQlcccUV1NXVsWzZMv7yl+yew3V1dVx55ZX88Y9/bDIHmD17NieccALPPvssixYt4tBDD2XGjBkA7LTTTsyePZuamhqGDRvGhAkTGDRoUNn2R1Ljx4et5qpGMzMzy82CBQu48847OeOMM1aPq6+v5xvf+AaXX978zfYnTpzIqFGj6Nq1K9tvvz21tbXMnDmTmTNnUltbyw477ECXLl0YNWoUEydOLMeuFMWJl5mZmeXmq1/9KpdffjkbbfRBSnLVVVcxcuRI+vZt/nngCxcupH//Dx4RWlNTw8KFC5sdv6Fw4mVmZma5+Mtf/kKfPn3Ye++9V49btGgRt912G1/5yldaWDJrF9aYpGbHbygqoo2XmZmZVZ9p06Zxxx13cNddd/HOO++wbNkyBg8eTNeuXVc3sn/rrbeora3lmWeeWWPZmpqa1Q3oIauy7NevH0Cz4zcEFXED1aFDh4Yb15uZmVWvqVOn8qMf/Wh14/oGm2++OcuXr/1s8aeeeorPf/7zzJw5k0WLFnHIIYcwd+5cIoKddtqJ++67j2222YZhw4bxu9/9jsGDB5drV5A0KyKGNjXNJV5mZmZWEe644w7q6uq49NJLGTx4MMcddxyDBg2ic+fOXH311XTq1AnI2oiNGDGC+vp6xowZU9akqzUu8TIzMzNbj1oq8XLjejMzM7MyceJlZmZmViZOvMzMzMzKxImXmZmZWZm4V6OZmZmV1IAL78w7hBbNG/epsm3LJV5mZmZmZeLEy8zMzKxMnHiZmZmZlYkTLzMzM7MyceJlZmZmViZOvMzMzMzKxImXmZmZWZk48TIzMzMrEydeZmZmZmXixMvMzMysTJx4mZmZmZWJEy8zMzOzMnHiZWZmZlYmTrzMzMzMysSJl5mZmVmZOPEyMzMzKxMnXmZmZmZl4sTLzMzMrEyceJmZmZmVSckTL0mdJD0m6S9peHtJMyTNlfS/krqUOgYzMzOzDUE5SrzOA+YUDP8Q+ElEDASWAKeXIQYzMzOz3JU08ZJUA3wK+FUaFnAwcHuaZTzwmVLGYGZmZrahKHWJ10+BbwLvp+EPAUsjYlUaXgBs09SCks6UVCep7pVXXilxmGZmZmalV7LES9KRwMsRMatwdBOzRlPLR8S1ETE0Iob27t27JDGamZmZlVPnEq77Y8BISUcAmwBbkJWA9ZDUOZV61QCLShiDmZmZ2QajZCVeEfGtiKiJiAHAKOD+iDgReAA4Ns02GphYqhjMzMzMNiR53MfrAuB8Sc+Qtfm6PocYzMzMzMqulFWNq0XEVGBqev0ssE85tmtmZma2IfGd683MzMzKxImXmZmZWZk48TIzMzMrEydeZmZmZmXixMvMzMysTJx4mZmZmZWJEy8zMzOzMnHiZWZmZlYmTrzMzMzMysSJl5mZmVmZOPEyMzMzKxMnXmZmZmZl4sTLzMzMrEyceJmZmZmViRMvMzMzszJx4mVmZmZWJk68zMzMzMrEiZeZmZlZmTjxMjMzMysTJ15mZmZmZeLEy8zMzKxMnHiZmZmZlYkTLzMzM7MyceJlZmZmViZOvMzMzMzKxImXmZmZWZk48TIzMzMrEydeZmZmZmXSauIl6WOSuqXXJ0m6QtJ2pQ/NzMzMrLoUU+L1C+AtSXsC3wSeByaUNCozMzOzKlRM4rUqIgI4CrgyIq4Eupc2LDMzM7Pq07mIed6U9C3gJOBASZ2AjUsblpmZmVn1KabE63jgXeD0iHgR2Ab4n5JGZWZmZlaFWi3xSsnWFQXDL+A2XmZmZmZtVkyvxv0k/V3SckkrJdVLeqMcwZmZmZlVk2KqGq8CTgDmApsCZwBXlzIoMzMzs2pUTON6IuIZSZ0ioh64QdIjJY7LzMzMrOoUk3i9JakL8Liky4HFQLfShmVmZmZWfYqpajwZ6AR8GVgB9AeOKWVQZmZmZtWomF6Nz6eXbwNjSxuOmZmZWfVqNvGSdGtEHCfpSSAaT4+IPUoamZmZmVmVaamq8bz0/0jg0038mdl69M4777DPPvuw5557MnjwYC655BIAnnvuOfbdd18GDhzI8ccfz8qVK5tc/rLLLqO2tpadd96ZyZMnrx4/adIkdt55Z2praxk3blxZ9sXMzJrWbOIVEYsL5nkpIp5P1Y4vAypHcGYdSdeuXbn//vv5xz/+weOPP86kSZOYPn06F1xwAV/72teYO3cuPXv25Prrr19r2dmzZ3PLLbfw1FNPMWnSJL70pS9RX19PfX0955xzDnfffTezZ8/m5ptvZvbs2TnsnZmZQXGN628D3i8Yrk/jzGw9ksTmm28OwHvvvcd7772HJO6//36OPfZYAEaPHs2f/vSntZadOHEio0aNomvXrmy//fbU1tYyc+ZMZs6cSW1tLTvssANdunRh1KhRTJw4saz7ZWZmHygm8eocEavrNtLrLqULyazjqq+vZ8iQIfTp04fhw4ez44470qNHDzp3zppj1tTUsHDhwrWWW7hwIf3791893DBfc+PNzCwfxSRer0ga2TAg6Sjg1dKFZNZxderUiccff5wFCxYwc+ZM5syZs9Y80to1/RFr9X9BUrPjzcwsH8XcQPUs4LeSriJr2zUfOKWkUZl1cD169OATn/gE06dPZ+nSpaxatYrOnTuzYMEC+vXrt9b8NTU1zJ8/f/Vw4XzNjTczs/JrtcQrIv4dEfsBg4BBEbF/RDxT+tDMOpZXXnmFpUuXAvD2229z7733suuuu3LQQQdx++23AzB+/HiOOuqotZYdOXIkt9xyC++++y7PPfccc+fOZZ999mHYsGHMnTuX5557jpUrV3LLLbcwcuTItZY3M7PyaLXES1JXsjvVDwA6N1RTRMSlJY3MrINZvHgxo0ePpr6+nvfff5/jjjuOI488kkGDBjFq1Ci++93vstdee3H66acDcMcdd1BXV8ell17K4MGDOe644xg0aBCdO3fm6quvplOnTgBcddVVjBgxgvr6esaMGcPgwYPz3E0zsw5NTbUBWWMGaRLwBjCLrEcjABHx49KG9oGhQ4dGXV1duTZnZmZm69GAC+/MO4QWzRv3qfW6PkmzImJoU9OKaeNVExGHtWOjmwAPAV3Tdm6PiEskbQ/cAvQCHgVOLuw1aWZmZlatiunV+Iik3dux7neBgyNiT2AIcJik/YAfAj+JiIHAEuD0dqzbzMzMrOIUk3gdAMyS9LSkJyQ9KemJ1haKzPI0uHH6C+Bg4PY0fjzwmXbEbWZmZlZxiqlqPLy9K5fUiaxtWC1wNfBvYGlErEqzLAC2aWbZM4EzAbbddtv2hmC2Qelo7RzMzGxNxdxO4nmgP1m14fPAW8Usl5atj4ghQA2wD7BrU7M1s+y1ETE0Iob27t27mM2ZmZmZbdCaTaAk7ZL+XwJcAHwrTdoY+E1bNhIRS4GpwH5AD0kNJW01wKK2hWxmZmZWmZpMvCQdzAeN3o8GRgIrACJiEdC9tRVL6i2pR3q9KXAoMAd4ADg2zTYa8BN7zczMrENoro3XJqREC1gZESEpACR1K3LdfYHxqZ3XRsCtEfEXSbOBWyR9H3gMuL794ZuZmZlVjiYTr4i4S9LLafBWSb8kqyL8AjAGuK61FUfEE8BeTYx/lqy9l5mZmVmH0myvxoioS/9/JGk4sAzYGbg4Iu4pU3xmZmZmVaPF20mkasLJEXEo4GTLzMzMbB20eFuIiKgH3pK0ZZniMTMzM6taxdxA9R3gSUn38EGDeyLi3JJFZWZmZlaFikm87kx/ZmZmZrYOimnjNTwiTipTPGZmZmZVq5g2Xr0ldSlTPGZmZmZVq5iqxnnANEl3sGYbrytKFZSZmZlZNSom8VqU/jaiiEcFmZmZmVnTWk28ImIsgKTu2WAsL3lUZmZmZlWoxTZeAJJ2k/QY8E/gKUmzJA0ufWhmZmZm1aXVxAu4Fjg/IraLiO2Ar1PEsxrNzMzMbE3FJF7dIuKBhoGImAp0K1lEZmZmZlWqmMb1z0q6CLgpDZ8EPFe6kMzMzMyqUzElXmOA3sAf0t9WwGmlDMrMzMysGhXTq3EJ4OcympmZma2jYno13iOpR8FwT0mTSxuWmZmZWfUppqpxq4hY2jCQSsD6lC4kMzMzs+pUTOL1vqRtGwYkbQdE6UIyMzMzq07F9Gr8DvCwpAfT8IHAmaULyczMzKw6FdO4fpKkjwD7pVFfi4hXSxuWmZmZWfUppsQLYH+ykq4GfylBLGZmZmZVrZhejeOA84DZ6e88SZeVOjAzMzOzalNMidcRwJCIeB9A0njgMeBbpQzMzMzMrNoU06sRoEfB6y1LEYiZmZlZtSumxOsy4DFJDwAia+vl0i4zMzOzNiqmV+PNkqYCw8gSrwsi4sVSB2ZmZmZWbYrq1RgRi4E7ShyLmZmZWVUrto2XmZmZma0jJ15mZmZmZVJU4iXpAEmnpde9JW1f2rDMzMzMqk8xN1C9BLiAD3oybgz8ppRBmZmZmVWjYkq8jgZGAisAImIR0L2UQZmZmZlVo2ISr5UREUAASOpW2pDMzMzMqlMxidetkn4J9JD0BeBe4LrShmVmZmZWfYq5geqPJA0HlgE7AxdHxD0lj8zMzMysyhR7A9V7ACdbZmZmZuug2cRL0pukdl1NiYgtShKRmZmZWZVqNvGKiO4Aki4FXgRuIntW44m4V6OZmZlZmxXTuH5ERPw8It6MiGUR8QvgmFIHZmZmZlZtikm86iWdKKmTpI0knQjUlzqwPMyfP5+DDjqIXXfdlcGDB3PllVcC8PrrrzN8+HAGDhzI8OHDWbJkSZPLjx8/noEDBzJw4EDGjx+/evysWbPYfffdqa2t5dxzzyW7O4eZmZl1NMUkXp8HjgNeSn+fS+OqTufOnfnxj3/MnDlzmD59OldffTWzZ89m3LhxHHLIIcydO5dDDjmEcePGrbXs66+/ztixY5kxYwYzZ85k7NixqxO0s88+m2uvvZa5c+cyd+5cJk2aVO5dMzMzsw1Aq4lXRMyLiKMiYquI6B0Rn4mIeWWIrez69u3LRz7yEQC6d+/OrrvuysKFC5k4cSKjR48GYPTo0fzpT39aa9nJkyczfPhwevXqRc+ePRk+fDiTJk1i8eLFLFu2jI9+9KNI4pRTTmlyeTMzM6t+RT0kuyOaN28ejz32GPvuuy8vvfQSffv2BbLk7OWXX15r/oULF9K/f//VwzU1NSxcuJCFCxdSU1Oz1ngzMzPreJx4NWH58uUcc8wx/PSnP2WLLYq7a0ZT7bYkNTvezMzMOh4nXo289957HHPMMZx44ol89rOfBWDrrbdm8eLFACxevJg+ffqstVxNTQ3z589fPbxgwQL69etHTU0NCxYsWGu8mZmZdTytJl6StpZ0vaS70/AgSaeXPrTyiwhOP/10dt11V84///zV40eOHLm6l+L48eM56qij1lp2xIgRTJkyhSVLlrBkyRKmTJnCiBEj6Nu3L927d2f69OlEBBMmTGhyeTMzM6t+xZR43QhMBhqKaf4P+GqpAsrTtGnTuOmmm7j//vsZMmQIQ4YM4a677uLCCy/knnvuYeDAgdxzzz1ceOGFANTV1XHGGWcA0KtXLy666CKGDRvGsGHDuPjii+nVqxcAv/jFLzjjjDOora1lxx135PDDD89tH83MzCw/au2eUpL+HhHDJD0WEXulcY9HxJCyRAgMHTo06urqyrU5s5IZcOGdeYfQonnjPpV3CGZWhTrad5+kWRExtKlpxZR4rZD0IdJzGyXtB7xRxEb7S3pA0hxJT0k6L43vJekeSZjaeHkAAB0hSURBVHPT/55t2BczMzOzilVM4nU+cAewo6RpwATgK0Ustwr4ekTsCuwHnCNpEHAhcF9EDATuS8NmZmZmVa/Zh2Q3iIhHJX0c2JnsIdlPR8R7RSy3GFicXr8paQ6wDXAU8Ik023hgKnBBe4I3MzMzqyTNJl6SPtvMpJ3S/an+UOxGJA0A9gJmAFunpIyIWCxp7XszmJmZmVWhlkq8Pp3+9wH2B+5PwweRlVIVlXhJ2hz4PfDViFhW7M1DJZ0JnAmw7bbbFrVMsTpaIz8zMzPbMDTbxisiTouI08ga1Q+KiGMi4hhgcLErl7QxWdL124ISspck9U3T+wJrP38n2/61ETE0Iob27t272E2amZmZbbCKaVw/oKFqMHkJ2Km1hZQVbV0PzImIKwom3QGMTq9HAxOLjNXMzMysorXauB6YKmkycDNZ6dco4IEilvsYcDLwpKTH07hvA+OAW9Pd718APtfmqM3MzMwqUDG9Gr+cGtr/Rxp1bUT8sYjlHibrBdmUQ4oP0czMzKw6FFPi1dCDsehejGZmZma2tmIekr2fpL9LWi5ppaR6ScvKEZyZmZlZNSmmcf1VwAnAXGBT4AzgZ6UMyszMzKwaFVvV+IykThFRD9wg6ZESx2VmZmZWdYpJvN6S1AV4XNLlZI8B6lbasMzMzMyqTzFVjScDnYAvAyuA/sAxpQzKzMzMrBoVczuJ59PLt4GxpQ3HzMzMrHq19JDsJ8lumNqkiNijJBGZmZmZVamWSryOTP/PSf9vSv9PBN4qWURmZmZmVarZxKuhilHSxyLiYwWTLpQ0Dbi01MGZmZmZVZNiGtd3k3RAw4Ck/XGvRjMzM7M2K+Z2EqcDv5a0ZRpeCowpXUhmZmZm1amYXo2zgD0lbQEoIt4ofVhmZmZm1aelXo0nRcRvJJ3faDwAEXFFiWMzMzMzqyotlXg1tOPq3sS0Zm8zYWZmZmZNa6lX4y/Ty3sjYlrhNEkfa2IRMzMzM2tBMb0af1bkODMzMzNrQUttvD4K7A/0btTOawuyZzeamZmZWRu01MarC7B5mqewndcy4NhSBmVmZmZWjVpq4/Ug8KCkGwselG1mZmZm7VTMDVS7SroWGFA4f0QcXKqgzMzMzKpRMYnXbcA1wK+A+tKGY2ZmZla9ikm8VkXEL0oeiZmZmVmVK+Z2En+W9CVJfSX1avgreWRmZmZmVaaYEq/R6f83CsYFsMP6D8fMzMysehXzkOztyxGImZmZWbUrpsQLSbsBg4BNGsZFxIRSBWVmZmZWjVpNvCRdAnyCLPG6CzgceBhw4mVmZmbWBsU0rj8WOAR4MSJOA/YEupY0KjMzM7MqVEzi9XZEvA+skrQF8DJuWG9mZmbWZsW08aqT1AO4DpgFLAdmljQqMzMzsypUTK/GL6WX10iaBGwREU+UNiwzMzOz6lNM4/oDmxoXEQ+VJiQzMzOz6lRMVWPhjVM3AfYhq3L0Q7LNzMzM2qCYqsZPFw5L6g9cXrKIzMzMzKpUMb0aG1sA7La+AzEzMzOrdsW08foZ2bMZIUvUhgD/KGVQZmZmZtWoqNtJFLxeBdwcEdNKFI+ZmZlZ1Som8boNqE2vn46Id0sYj5mZmVnVaraNl6SNJf0UmA/cAIwHnpV0YZq+V3lCNDMzM6sOLZV4/RjYDBgQEW8CpEcG/UjSL4DDgO1LH6KZmZlZdWgp8ToCGBgRDQ3riYhlks4GXgUOL3VwZmZmZtWkpdtJvF+YdDWIiHrglYiYXrqwzMzMzKpPS4nXbEmnNB4p6SRgTulCMjMzM6tOLVU1ngP8QdIYskcEBTAM2BQ4ugyxmZmZmVWVZhOviFgI7CvpYGAwIODuiLivXMGZmZmZVZNintV4P3B/GWIxMzMzq2rteVajmZmZmbWDEy8zMzOzMnHiZWZmZlYmJUu8JP1a0suS/lkwrpekeyTNTf97lmr7ZmZmZhuaUpZ43Uj2WKFCFwL3RcRA4L40bGZmZtYhlCzxioiHgNcbjT6K7GHbpP+fKdX2zczMzDY05W7jtXVELAZI//uUeftmZmZmudlgG9dLOlNSnaS6V155Je9wzMzMzNZZuROvlyT1BUj/X25uxoi4NiKGRsTQ3r17ly1AMzMzs1Ipd+J1BzA6vR4NTCzz9s3MzMxyU8rbSdwM/A3YWdICSacD44DhkuYCw9OwmZmZWYfQ6rMa2ysiTmhm0iGl2qaZmZnZhmyDbVxvZmZmVm2ceJmZmZmViRMvMzMzszJx4mVmZmZWJk68zMzMzMrEiZeZmZlZmTjxMjMzW4+WLl3Kscceyy677MKuu+7K3/72tzWmRwTnnnsutbW17LHHHjz66KOrp40fP56BAwcycOBAxo8fX+7QrQxKdh8vMzOzjui8887jsMMO4/bbb2flypW89dZba0y/++67mTt3LnPnzmXGjBmcffbZzJgxg9dff52xY8dSV1eHJPbee29GjhxJz549c9oTKwWXeJmZma0ny5Yt46GHHuL0008HoEuXLvTo0WONeSZOnMgpp5yCJPbbbz+WLl3K4sWLmTx5MsOHD6dXr1707NmT4cOHM2nSpDx2w0rIiZeZmdl68uyzz9K7d29OO+009tprL8444wxWrFixxjwLFy6kf//+q4drampYuHBhs+OtujjxMjMzW09WrVrFo48+ytlnn81jjz1Gt27dGDduzccSR8Ray0lqdrxVFydeZmZm60lNTQ01NTXsu+++ABx77LFrNJ5vmGf+/PmrhxcsWEC/fv2aHW/VxYmXmVWlAQMGsPvuuzNkyBCGDh261nT3LLNS+PCHP0z//v15+umnAbjvvvsYNGjQGvOMHDmSCRMmEBFMnz6dLbfckr59+zJixAimTJnCkiVLWLJkCVOmTGHEiBF57IaVkHs1mlnVeuCBB9hqq62anOaeZVYqP/vZzzjxxBNZuXIlO+ywAzfccAPXXHMNAGeddRZHHHEEd911F7W1tWy22WbccMMNAPTq1YuLLrqIYcOGAXDxxRfTq1ev3PbDSsOJl5l1SM31LJs6derqnmXA6p5lJ5xwQs4RW6UYMmQIdXV1a4w766yzVr+WxNVXX93ksmPGjGHMmDEljc/y5apGM6tKkvjkJz/J3nvvzbXXXrvWdPcsM7M8uMTLzKrStGnT6NevHy+//DLDhw9nl1124cADD1w93T3LzCwPLvEys6rU0BusT58+HH300cycOXON6e5ZZmZ5cImXmVWdFStW8P7779O9e3dWrFjBlClTuPjii9eYZ+TIkVx11VWMGjWKGTNmrNGz7Nvf/jZLliwBYMqUKVx22WV57IZtQAZceGfeIbRo3rhP5R2CFcmJl5lVnZdeeomjjz4ayG5o+fnPf57DDjvMPcvMLHdOvMys6uywww784x//WGu8e5aZWd7cxsvMzMysTJx4mZmZmZWJEy8zMzOzMnEbLzOrGO5ZZmaVziVeZmZmZmXixMvMzMysTJx4mZmZmZWJEy8zMzOzMnHiZWZmZlYmTrzMzMzMysSJl5mZmVmZOPEyMzMzKxMnXmZmZmZl4sTLzMzMrEyceFlJjBkzhj59+rDbbrs1OT0iOPfcc6mtrWWPPfbg0UcfXT1t/PjxDBw4kIEDBzJ+/PhyhWxmiT+/ZqXjxMtK4tRTT2XSpEnNTr/77ruZO3cuc+fO5dprr+Xss88G4PXXX2fs2LHMmDGDmTNnMnbsWJYsWVKusM0Mf37NSsmJl5XEgQceSK9evZqdPnHiRE455RQksd9++7F06VIWL17M5MmTGT58OL169aJnz54MHz68xR8AM1v//Pk1Kx0nXpaLhQsX0r9//9XDNTU1LFy4sNnxZrbh8OfXrP2ceFkuImKtcZKaHW9mGw5/fs3az4mX5aKmpob58+evHl6wYAH9+vVrdryZbTj8+TVrPydelouRI0cyYcIEIoLp06ez5ZZb0rdvX0aMGMGUKVNYsmQJS5YsYcqUKYwYMSLvcM2sgD+/Zu3XOe8ArDqdcMIJTJ06lVdffZWamhrGjh3Le++9B8BZZ53FEUccwV133UVtbS2bbbYZN9xwAwC9evXioosuYtiwYQBcfPHFLTbyNbP1z59fs9Jx4mUlcfPNN7c4XRJXX311k9PGjBnDmDFjShGWmRXBn1+z0nFVo5mZmVmZOPEyMzMzKxMnXmZmZmZl4sTLzMzMrEzcuN7aZMCFd+YdQovmjftU3iGYbbA25M+vP7vWUbjEy8zMzKxMckm8JB0m6WlJz0i6MI8YzMzMzMqt7ImXpE7A1cDhwCDgBEmDyh2HmZmZWbnlUeK1D/BMRDwbESuBW4CjcojDzMzMrKzU1NPkS7pB6VjgsIg4Iw2fDOwbEV9uNN+ZwJlpcGfg6bIG2jZbAa/mHUSF8rFbNz5+68bHb934+LWfj9262dCP33YR0bupCXn0alQT49bK/iLiWuDa0oez7iTVRcTQvOOoRD5268bHb934+K0bH7/287FbN5V8/PKoalwA9C8YrgEW5RCHmZmZWVnlkXj9HRgoaXtJXYBRwB05xGFmZmZWVmWvaoyIVZK+DEwGOgG/joinyh3HelYRVaIbKB+7dePjt258/NaNj1/7+ditm4o9fmVvXG9mZmbWUfnO9WZmZmZl4sTLzMzMrEyceJmZmZmViRMvMzMriqStJV0v6e40PEjS6XnHZVZJnHi1k6QDJJ2WXveWtH3eMVnHIGnvJsZ9Oo9YrMO5kaxHer80/H/AV3OLpkJJ2kJSr4a/vOOpFJI2k3SRpOvS8EBJR+YdV1s58WoHSZcAFwDfSqM2Bn6TX0SVQdKTkp5o7i/v+CrIdZJ2bxiQdALw3RzjqSiS9pP0d0nLJa2UVC9pWd5xVYitIuJW4H3Ibg8E1OcbUuWQ9EVJLwFPALPSX12+UVWUG4B3gY+m4QXA9/MLp33yeGRQNTga2At4FCAiFknqnm9IFaHhyuSc9P+m9P9E4K3yh1OxjgVul3QicABwCvDJfEOqKFeR3bj5NmAo2fGrzTWiyrFC0odIj3mTtB/wRr4hVZT/BAZHxIb8jMEN2Y4RcXy62CQi3pbU1GMIN2hOvNpnZUSEpIYvn255B1QJIuJ5AEkfi4iPFUy6UNI04NJ8IqssEfGspFHAn4D5wCcj4u2cw6ooEfGMpE4RUQ/cIOmRvGOqEOeTPWlkx/SZ7U12IWDF+Te+yFwXKyVtygeJ/45kJWAVxYlX+9wq6ZdAD0lfAMYAv8o5pkrSTdIBEfEwZIkY4OS1FZKeZM0Hyvcie/rDDElExB75RFZx3kqPK3tc0uXAYnz+FSUiHpX0cWBnQMDTEfFezmFVkm8Bj0iaQUHCEBHn5hdSRbkEmAT0l/Rb4GPAqblG1A6+c307SRpOVr0jYHJE3JNzSBUjNQ7/NbAlWSLxBnBaRDyWa2AbOEnbtTS9oUTRWpaO40tAF+BrZOfhzyPimVwD24BJ+mxL0yPiD+WKpZJJmgk8DDxJaicHEBHjcwuqQqQqxRqyEsP9yH57p1dita0Tr3aQ9MOIuKC1cdY0SV3Jqid2BHoCS4GICFc1toGkPsAmDcMR8UKO4VSM1DTg7Yh4Pw13ArpGhKuAmiHphvSyD7A/cH8aPgiYGhEtJmaWkfRIROyfdxyVStKsiFirV3elca/G9hnexLjDyx5F5ZoIfBp4h6xXynJgRa4RVRBJIyXNBZ4DHgTmAXfnGlRluQ/YrGB4U+DenGKpCBFxWkScRlZCPSgijomIY4DBOYdWaR6QdKakvr6dRLtMlzQs7yDWldt4tYGks4EvATs0uv1Bd2BaPlFVpJqIOCzvICrYf5EVtd8bEXtJOgg4IeeYKskmEbG8YSAilkvarKUFbLUBEbG4YPglYKe8gqlAn0//v1UwLoAdcoilEh0EfFHS82QX6yKrLamo9q1OvNrmd2QlC5cBFxaMfzMiXs8npIr0iKTdI+LJvAOpUO9FxGuSNpK0UUQ8IOmHeQdVQVZI+khEPAqr2xy6V2hxpkqaDNxMljCMAh7IN6TKERG+0fa6qYqaJbfxaqfULmRrCpJXt7FpWUGvvM7AQOBZsp49FXnVkhdJ9wKfAcYBHwJeBoa57UhxUlXFLcCiNKovcHxEzMovqsoh6WjgwDT4UET8Mc94Ko2k3YBBrNk+c0J+EVWeSm/f6sSrHSR9GfgeWTF7Q88UJw6tcK+89aOhcThZG80TyXrl/TYiXss1sAoiaWM+uCXCv3xLhOKlz/HAiLg3VdF2iog3846rEqSnnnyCLPG6i6wE5+GI8L3QiiBpJPBjskdWvQxsB8yJiIpqa+jEqx0kPQPs6x86y4t//NaNSx3aJ9238EygV0TsKGkgcE1EHJJzaBUhlfrvCTwWEXtK2hr4VUT4WatFkPQP4GAatW+NiDNzDq1N3Kuxfebjx2RYTtKP3+3AL9OobcjuYm9FSKUOP0t/BwGXAyNzDapynEN208plABExl+wWE1acd9JtTFZJ2oKs1MYN64v3XirwWN2+FRiSd1Bt5cb17fMsWSPTO1nz7sNX5BeSdSDnAPsAMyD78UttHqw4x/JBqcNpDaUOOcdUKd6NiJUNj8eT1Jk1n6ZgzUg3AH1CUg/gOrIHZC8HZuYaWGVZKmlz4CHgt5JeBlblHFObOfFqnxfSX5f0Z1ZO/vFbN29HxPuSXOrQdg9K+jawaXp6x5eAP+ccU0VIz/cdEhFLgWskTQK2iIgnWlvWVjuKrH3r1/igfWvF3XjbiVc7RMRYAEnds8EP7glkVgb+8Vs3dS51aLcLgdPJHnnzRbIG4i4tLN50ScMi4u8RMS/vYCrQ8cBfUxV3xT5myY3r2yE1zL2J7CHFAK8Cp0TEU/lFZR2FpI3IfvxWPyuUrIGuP8xtJGkALnUoSrqFzviIOCnvWCqVpNlkN5yt6BuA5kXSpcABwACgDvgrWSL2eJ5xtZUTr3aQ9AjwndSwD0mfAH7g+yhZqfnHr/0kfaSl6Q03VLXmpZunfjoiVuYdSyVq7pY6vpVO20jaFPgC8J/ANhHRKeeQ2sRVje3TrSHpAoiIqeneSmYlFRH1knpL6uIfvzb7ccHrwitOpeGDyxtORZoHTJN0BwXPV3XHouI4wVo3kr5L1qt2c+AxssTrr7kG1Q5OvNrnWUkXkVU3ApxE9sBis3KYh3/82iwiDoLVV8tfIquyCLIv7l/kGFolWZT+NiJ7Rq1ZOX2WrBfjncCDwPSIeCffkNrOVY1tIOmmiDhZ0vlkdcwHkF0tPwiMjYglecZnHUO6D9VaGjp9WMsk3Up2H6rfplEnAD0i4rj8ojKzYqRObQekv+OAlyLigHyjahuXeLXN3qmOfjTZjRcbqihIr81KrqBX7RbZoO9Y30Y7R8SeBcMPpDtim9kGLHVs+w/g48BQspuZu6qxyl0DTCK7509dwfiGBMz3ArKSkzQUuIFU1SPpDWCMH/JctMck7RcR0wEk7QtMyzkmM2vdD8lqmP4f8PdKfcaqqxrbQdIvIuLsvOOwjknSE8A5EfHXNHwA8HN3SS+OpDlkD8h+IY3aFphD9sB7d+0320BJ+mpE/LTRuPMi4sq8YmoPJ15mFUbStIj4WGvjrGnNdelv4J5nzZO0E1lHhK0jYjdJewAjI+L7OYdmHYCkRyPiI43GPRYRe+UVU3s48TKrEAX3oToZ2Ay4mayK+3hgSUR8J6/YrGOQ9CDwDeCXDT92kv4ZEbvlG5lVM0knAJ8na9/1UMGk7kB9RByaS2Dt5DZeZpXjx42GC3s3+grKymGziJjZ8JzQpOIeUmwV51FgMbAVa34PvglU3FMnnHiZVYiG+1CZ5ehVSTuSEn1Jx5L9IJqV0s0R8RFJ/46IB/MOZl058TKrMJK2JCvtOjCNehC4NCLeyC8q6yDOAa4FdpG0kOzG0X58lZVaF0mjgY9K+mzjiRHxhxxiaje38TKrMJJ+D/wTGJ9GnQzsGRFrfSGZlUJ6RNpGvoeclUPquX0i2Q1T72g0OSJiTPmjaj8nXmYVRtLjETGktXFm60t6Wkez/LgqKwdJp0fE9XnHsa5c1WhWed6WdEBEPAwg6WPA2znHZNWt4bmMOwPD+KDU4dOs2cvMrJRuknQuazazuKbSbqTqEi+zCiNpCFk145Zp1BJgdERUXO8eqyySpgDHNFQxpufm3RYRh+UbmXUEkn4FbMyazSzqI+KM/KJqO5d4mVWeOcDlwI5AD+AN4DNUYLdqqzjbAisLhlcCA/IJxTqgYY2es3p/JT5n1YmXWeWZCCwlu7fNwpxjsY7lJmCmpD+S3VLiaD4ofTArtXpJO0bEvwEk7QDU5xxTm7mq0azC+E7hlqf0BIX/SIMPRcRjecZjHYekg4EbgWfTqAHAaRHxQF4xtYdLvMwqzyOSdo+IJ/MOxDqeiHiUrLTVrNw+BOxGlnAdBexP1tSiomyUdwBm1mYHALMkPS3pCUlPSnL7LjOrdhdFxDJgC2A4cA3ZQ9sriku8zCrP4XkHYGaWg4b2XJ8iu43EREnfyzGednEbLzMzM9vgSfoLWYeiQ4G9ye5fOLNRT8cNnqsazcysKJL2k/R3ScslrZRUL2lZ3nFZh3EcMBk4LCKWAr2Ab+QbUtu5xMvMzIoiqQ4YBdwGDAVOAWoj4ju5BmZWQdzGy8zMihYRz0jqFBH1wA2SHsk7JrNK4sTLzMyK9ZakLsDjki4HFgPdco7JrKK4jZeZmRXrZLLfjS8DK4D+wDG5RmRWYdzGy8zMiiKpG/B2RLyfhjsBXSPirXwjM6scLvEyM7Ni3QdsVjC8KXBvTrGYVSQnXmZmVqxNImJ5w0B6vVkL85tZI068zMysWCvSQ7IBkNRwE0szK5J7NZqZWbG+CtwmaVEa7gscn2M8ZhXHjevNzKxokjYGdgYE/Csi3ss5JLOK4sTLzMyKJmk3YBCwScO4iJiQX0RmlcWJl5mZFUXSJcAnyBKvu4DDgYcj4tg84zKrJG5cb2ZmxToWOAR4MSJOA/YEuuYbklllceJlZmbFarh56ipJWwAvAzvkHJNZRXGvRjMzK1adpB7AdcAsYDkwM9+QzCqL23iZmVmbSRoAbBERT+QcillFceJlZmYtKrxpalMi4tFyxWJW6Zx4mZlZiyQ90MLkiIiDyxaMWYVz4mVmZmZWJm5cb2ZmRZG0CfAl4AAggL8C10TEO7kGZlZBXOJlZmZFkXQr8CbwmzTqBKBnRHwuv6jMKosTLzMzK4qkf0TEnq2NM7Pm+QaqZmZWrMck7dcwIGlfYFqO8ZhVHJd4mZlZUSTNAXYGXkijtgXmAO+T9W7cI6/YzCqFEy8zMyuKpO1amh4Rz5crFrNK5cTLzMzMrEzcxsvMzMysTJx4mVmHJqmTpPMk+b6GZlZyTrzMrOJIWt5o+FRJV7VzdecDyyNiVTPb6ifp9nau28xsDb7CM7MOR1LniFglaSPgxYi4qYX5FgHHljdCM6tWLvEys6oiaTtJ90l6Iv3fNo2/UdIV6YHPP5S0D/AwcL6kRyTtnOY7VdJtkv4MTJE0QNI/07RNJN0g6UlJj0k6KK/9NLPK5BIvM6tEm0p6vGC4F3BHen0VMCEixksaA/w/4DNp2k7AoRFRL2kL4MBU8jUC+AFwTJrvo8AeEfG6pAEF2zkHICJ2l7QLWWK2k59VaGbFcuJlZpXo7YgY0jAg6VRgaBr8KPDZ9Pom4PKC5W6LiPr0ujtwnaRtAAEfKpjvnoh4vYntHgD8DCAi/iXpebJk7ol12x0z6yhc1Whm1a7wZoUrCl5/H3ggIg4ATgI2aWa+QlrPsZlZB+PEy8yqzSPAqPT6RLJ2XE3pCbySXp9a5LofSutE0k5kj8x5ul1RmlmH5MTLzKrNucBpkp4ATgbOa2a+/wEukzSN4r8Lfw50kvQk8L/AqRHx7roGbGYdhx8ZZGb/vx07pgEAAAAQ1L+1LbwghROAieMFADARXgAAE+EFADARXgAAE+EFADARXgAAE+EFADARXgAAkwC/Nh0jFoYY/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_cchl'))].groupby('ESPC')['ESPC'].count())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por tipo de problema - STI CCHL\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_ct'))].groupby('ESPC')['ESPC'].count())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por tipo de problema - STI CT\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_cce'))].groupby('ESPC')['ESPC'].count())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por tipo de problema - STI CCE\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'sti_cca'))].groupby('ESPC')['ESPC'].count())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por tipo de problema - STI CCA\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quantidade de ocorrências')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['STI'] == 'biblioteca'))].groupby('ESPC')['ESPC'].count())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por tipo de problema - STI BIBLIOTECA\")\n",
    "plt.xlabel(\"Horário\")\n",
    "plt.ylabel(\"Quantidade de ocorrências\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Número de ocorrências não encerradas por tipo de equipamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sti_central    3\n",
       "Name: STI, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HOUR_EFIC'] < 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'hd')]['STI'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              13\n",
       "campus          2\n",
       "sti_cca         3\n",
       "sti_cce         2\n",
       "sti_cchl        4\n",
       "sti_central    23\n",
       "sti_ct          7\n",
       "Name: STI, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HOUR_EFIC'] < 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'fonte')]['STI'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: STI, dtype: int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HOUR_EFIC'] < 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'bateria')]['STI'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'hd')]['STI'].value_counts().sort_index()\n",
    "fonte = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'fonte')]['STI'].value_counts().sort_index()\n",
    "bateria = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'bateria')]['STI'].value_counts().sort_index()\n",
    "ram = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'ram')]['STI'].value_counts().sort_index()\n",
    "placaR = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de rede')]['STI'].value_counts().sort_index()\n",
    "placaV = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de video')]['STI'].value_counts().sort_index()\n",
    "placaM = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa')]['STI'].value_counts().sort_index()\n",
    "cabo = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'cabo')]['STI'].value_counts().sort_index()\n",
    "nobreak = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'nobreak')]['STI'].value_counts().sort_index()\n",
    "estabilizador = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'estabilizador')]['STI'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              12\n",
       "biblioteca      1\n",
       "sti_cca         7\n",
       "sti_cce         2\n",
       "sti_cchl        1\n",
       "sti_central    27\n",
       "sti_ct          1\n",
       "Name: STI, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Especificação')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf = round(df[((df['OPEN_YEAR_1'] >= 2015) & (df['HAS_VALUE'] == 'P'))].groupby('EQUIP')['HOUR_EFIC'].min())\n",
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "autolabel(ax.patches)\n",
    "plt.title(\"Volume de cadastro de ocorrências por horário em média\")\n",
    "plt.ylabel(\"Tempo gasto\")\n",
    "plt.xlabel(\"Especificação\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] != 'P')].groupby('STI')['HOUR_EFIC'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "plt.title(\"Tempo médio gasto para resolver problemas não precificáveis\")\n",
    "plt.xlabel(\"Tempo\")\n",
    "plt.ylabel(\"Setor\")\n",
    "\n",
    "autolabel(ax.patches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf = df[df['HOUR_EFIC'] > 0].groupby('STI')['HOUR_EFIC'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "ax = ndf.plot.bar()\n",
    "plt.title(\"Tempo médio gasto para resolver problemas Gerais\")\n",
    "plt.xlabel(\"Tempo\")\n",
    "plt.ylabel(\"Setor\")\n",
    "\n",
    "autolabel(ax.patches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                2617\n",
       "ccn              2175\n",
       "ccs              1977\n",
       "cca              1487\n",
       "cchl             1237\n",
       "prad             1037\n",
       "cce               862\n",
       "ct                607\n",
       "administracao     600\n",
       "campus            425\n",
       "preg              416\n",
       "praec             378\n",
       "nti               369\n",
       "prppg             290\n",
       "biblioteca        272\n",
       "prex              219\n",
       "proplan           160\n",
       "cead              109\n",
       "tropen            105\n",
       "fadex              96\n",
       "hu                 95\n",
       "juizado            63\n",
       "nintec             55\n",
       "parfor             49\n",
       "dce                36\n",
       "ntf                27\n",
       "noe                11\n",
       "Name: DEPARTMENT, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DEPARTMENT LOCALE\n",
    "df['DEPARTMENT'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantidade Média, minima e máxima de cada produtor precificável agrupada por ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fonte             325\n",
       "nobreak           167\n",
       "placa             100\n",
       "ram                59\n",
       "hd                 51\n",
       "software           37\n",
       "placa de rede      13\n",
       "placa de video      3\n",
       "Name: ESPC, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015)]['ESPC'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### software não deveria esta na lista de problemas precificáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11052    problema na conexao vga da placa de video onbo...\n",
       "11090    nao ligaformatar e instalar programas solucao ...\n",
       "11340    problema na placa de video local nes posta out...\n",
       "11342    computador reiniciando e aparecendo uma tela a...\n",
       "11662    nao inicia computador formatado e instalado pl...\n",
       "11708    placa de rede danificada e fazer o backup para...\n",
       "12476    ja e a terceira vez que mando a cpu para conse...\n",
       "12639    desliga sozinho computador nao apresenta erros...\n",
       "12669    nao esta dando video e nem saindo somlocal pre...\n",
       "12727    nao ligachamado hp 4653907913 hd trocado pela ...\n",
       "12739    executando diagnostico epsa da dell apresentou...\n",
       "12874    verificar bateriainstalar programa libreoffice...\n",
       "12927    o notebook nao esta iniciando corretamente ver...\n",
       "12931    tela azul na instalacao do windows  maquina fo...\n",
       "13076    computador necessita instalacao de placa wirel...\n",
       "13179    lento e incompativel para a instalacao do cert...\n",
       "13509    computador nao detecta rede  dessa forma foi i...\n",
       "13554    o computador nao liga obs o solicitante requer...\n",
       "13686    libreoffice problemas abre arquivos e ficam de...\n",
       "13878    computador lento e a entrada vga nao esta func...\n",
       "14175    trocar hd e instalar o windows fazer backup  s...\n",
       "14247    colocar placa wifi  foi colocada placa wifi ma...\n",
       "14557    hd danificado solucao substituicao do hd   sub...\n",
       "14570    local nes ctproblema hd defeito  hd substituid...\n",
       "14662    trocar bateria e verificar se e necessario for...\n",
       "14921    nao liga sem backup  bateria trocada manutenca...\n",
       "15010    lab de aguas cca deasobs instalar placa de red...\n",
       "15205    nao inicializa cooler da placa nao gira local ...\n",
       "15237    nao liga nao inicializa o windows obs backup o...\n",
       "15392    substituicao de hd maquina pouca memoria e apa...\n",
       "15427    nao inicia  formatacao e troca de bateria manu...\n",
       "15512    nao liga aberto por wesley rafael local parfor...\n",
       "15562    nao ligawindows corrompidolocal unepesaberto p...\n",
       "15635    problemas no adaptador de video vga so e possi...\n",
       "15701    nao liga  foi realizada a substituicao do hd f...\n",
       "15711    nao ligatrocar bateriaaberto por gerisson troc...\n",
       "15751    computador nao liga formatar fazer backup senh...\n",
       "Name: DESCRIB, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['HOUR_EFIC'] > 0) \n",
    "   & (df['HAS_VALUE'] == 'P') \n",
    "   & (df['OPEN_YEAR_1'] >= 2015) \n",
    "   & (df['ESPC'] == 'software')\n",
    "  ]['DESCRIB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pode-se verificar que as 24 ocorrencias de software **precificáveis** são de fato precificáveis porem a `class ESPC` **não** as classificou corretamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'hd')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "fonte = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'fonte')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "bateria = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'bateria')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "ram = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'ram')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "placaR = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de rede')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "placaV = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de video')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "placaM = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "cabo = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'cabo')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "nobreak = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'nobreak')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "estabilizador = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'estabilizador')]['OPEN_YEAR_1'].value_counts().sort_index()\n",
    "# print(hd)\n",
    "# print(fonte)\n",
    "# print(bateria)\n",
    "# print(ram)\n",
    "# print(placaR)\n",
    "# print(placaV)\n",
    "# print(placaM)\n",
    "# print(cabo)\n",
    "# print(nobreak)\n",
    "# print(estabilizador)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Produtos como cabo, estabilizadores e placas de video não apresentam valores expressivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (10,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1584x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (22,10)\n",
    "barWidth = 0.1  # the width of the bars\n",
    "\n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(5)\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    "r4 = [x + barWidth for x in r3]\n",
    "#r5 = [x + barWidth for x in r4]\n",
    "r6 = [x + barWidth for x in r4]\n",
    "#r7 = [x + barWidth for x in r6]\n",
    "r8 = [x + barWidth for x in r6]\n",
    "#r9 = [x + barWidth for x in r8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(r1, hd.values.tolist(), width=barWidth, edgecolor='white', label='hd')\n",
    "rects2 = ax.bar(r2, fonte.values.tolist(), width=barWidth, edgecolor='white', label='fonte')\n",
    "rects3 = ax.bar(r3, ram.values.tolist(), width=barWidth, edgecolor='white', label='ram')\n",
    "rects4 = ax.bar(r4, placaR.values.tolist(), width=barWidth, edgecolor='white', label='placa de rede')\n",
    "#rects5 = ax.bar(r5, placaV.values.tolist(), width=barWidth, edgecolor='white', label='placa de video')\n",
    "rects6 = ax.bar(r6, placaM.values.tolist(), width=barWidth, edgecolor='white', label='placa mãe')\n",
    "#rects7 = ax.bar(r7, cabo.values.tolist(), width=barWidth, edgecolor='white', label='cabos em geral')\n",
    "rects8 = ax.bar(r8, nobreak.values.tolist(), width=barWidth, edgecolor='white', label='nobreak')\n",
    "#rects9 = ax.bar(r9, estabilizador.values.tolist(), width=barWidth, edgecolor='white', label='estabilizador')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "autolabel(rects3)\n",
    "autolabel(rects4)\n",
    "#autolabel(rects5)\n",
    "autolabel(rects6)\n",
    "#autolabel(rects7)\n",
    "autolabel(rects8)\n",
    "#autolabel(rects9)\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Quantidade')\n",
    "ax.set_xlabel('Anos')\n",
    "ax.set_title('Quantidade de problemas precificavéis por ano')\n",
    "ax.set_xticklabels(['x','2015', '2016', '2017', '2018', '2019'])\n",
    "ax.legend()\n",
    "plt.savefig('foo.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     0.4\n",
      "2     0.4\n",
      "3     2.2\n",
      "4     1.4\n",
      "5     1.2\n",
      "6     1.6\n",
      "7     0.6\n",
      "8     0.2\n",
      "9     0.6\n",
      "10    0.6\n",
      "11    0.6\n",
      "12    0.4\n",
      "Name: OPEN_MONTH_1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "hd = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'hd')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "fonte = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'fonte')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "bateria = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'bateria')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "ram = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'ram')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "placaR = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de rede')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "placaV = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de video')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "placaM = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "cabo = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'cabo')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "nobreak = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'nobreak')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "estabilizador = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'estabilizador')]['OPEN_MONTH_1'].value_counts().sort_index()/5\n",
    "print(hd)\n",
    "# print(fonte)\n",
    "# print(bateria)\n",
    "# print(ram)\n",
    "# print(placaR)\n",
    "# print(placaV)\n",
    "# print(placaM)\n",
    "# print(cabo)\n",
    "# print(nobreak)\n",
    "# print(estabilizador)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6addc14510>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fPq2MjAxJUmVlpYqLi5Wamqrx48fL5XLprRA8iwYAMDTs26Mj1Ou9rq4u4n0AIuns/zkrl8sVkm3myjFfUPN7hyPeBwAAYlE4R7+91bKsqy3LSrEsy2lZ1uZwLQsD87Vbyhzzt4/b6XTK5/P1qLN27Vq98MILcjqdKiws1NNPP31+Xp9PmZmZPeY93uWPTOAAgH6xb4+OUK/3z84LJBp/m3/A7/1gt5m0y67U6Y9aIxM4AAAxjmfqJQmrjyFIzGdusdq6dauWLVsmr9er7du364477lB3d7esPmbm5iwAiD727dER8vXOLc9IdCHeZthbAQBwHs/USxLOMUYtH3QH33u93uBtDZ/YvHmzqqqqJEnXX3+9Ojo61NraKqfTqZaWlh7z3mRPiUzgAIB+sW+PjlCv98/OCySaFEeKWuou/L0f7DbT9mGrxo68IjKBAwAQ47hSL0nkfcGmxve71dTUpHPnzqmiokJut7tHnXHjxmnXrl2SpEOHDqmjo0NXXXWV3G63Kioq1NnZqaamJjU2NmrKiBHR6AYA4FPYt0dHqNf7rFmzotENIGI+N/5zamxsDMk2c+K0T9ekT45GNwAAiDlcqZck7MOMNhWOUEFBgQKBgDwej3JyclRSUqLc3Fy53W5t3LhR3/72t/XjH/9Yxhht2bJFxhjl5OSoqKhI2dnZstvtKisrk+3+B6LdJQBIeuzboyPk652Rb5HgjM1o06ZNIdlmiub+d0a+BQDgYyT1kkjhxBQV/vJIj7J169YFX2dnZ2vPnj19zrt69WqtXr06+P4QJ34AEBPYt0dHKNc7kAwKCwtVWFjYo+xStpmyFbvDFyQAAHGG228BAAAAAACAOENSDwAAAAAAAIgzJPUAAAAAAACAOENSL8l0BjqjHQIAIMTYt0dHKNY7nx2SRXcn33UAAEKNgTKSTKotVVPKpwypjbeWvhWiaAAAocC+PTqGut7b97cr9aVUBQIBLV++XI888kivOv/+7/+utWvXyhijadOm6Ve/+pUkqby8XOvXr5ckPfroo5IyLzmOwaiqqtKqVatCEuvSsEaKWDUsNVWHJmcNqY2sw4dCFM2F8X2PvERa56HsC/t2AAMhqQcAABBhVreld55/R4dfOyyn06m8vDy53W5lZ2cH6zQ2NmrDhg3as2eP0tLS9N5770mSTp48qdLSUtXX18sYo5kzZ+qeG36skamjwxJrIBDQypUr9fLLL4ckVvc3LaV9zoQlVmCo+L5HXiKt81D3hX07gIFw+y0AAECEnf0/Z5X6d6maMGGChg8fruLiYlVWVvao86//+q9auXKl0tLSJEnp6emSpOrqauXn58vhcCgtLU35+flqaHk9bLHW1dXJ5XKFLNaqt7vCFiswVHzfIy+R1nmo+8K+HcBASOoBAABEmL/NrxRHSvC90+mUz+frUefIkSM6cuSIvvKVr2jOnDmqqqqSJPl8PmVmZvaY99SHrWGLta/lDSVWX3t32GIFhorve+Ql0joPdV/YtwMYCLffAgAARJrVu8iYnrctdXV1qbGxUbW1tfJ6vfrqV7+qAwcOyLJ6zxzOG576XF6MxgoMFd/3yEukdR5PfYmnWAH0jyv1AAAAIizFkSL/SX/wvdfrVUZGRo86TqdTX//615WSkqLx48dr0qRJamxslNPpVEtLS495x152Zdhi7Wt5Q4k1YzSHn4hdfN8jL5HWeaj7wr4dwEDY8gAAACLsc+M/p87jnWpqatK5c+dUUVEht9vdo843vvENvfLKK5Kk1tZWHTlyRBMmTFBBQYFqamrU1tamtrY21dTUKMuZG7ZY8/Ly1NjYGLJYC1zcKILYxfc98hJpnYe6L+zbAQyELQ8AACDCjM0o4/YMFRQUKBAIyOPxKCcnRyUlJcrNzZXb7Q6eNGVnZ8tms+lHP/qRrrjiCknSmjVrlJeXJ0kqKSnRR3vHhC1Wu92uTZs2hSxWx1/vC1uswFDxfY+8RFrnoe4L+3YAAyGpBwAAEAWjp43WW0++1aNs3bp1wdfGGD355JN68skne83r8Xjk8XiC78v27g5foJIKCwtVWFjYo+xSY9VaTvwQ2/i+R14irfNQ9oV9O4CBcPstAAAAAAAAEGdI6gEAAAAAAABxhqQeAAAAAAAAEGdI6gEAAERJd2dntEMYtA5/INohABHD9z3yEmmds28HECkMlBEBVVVVWrVqlQKBgJYvX65HHnmkz3ovvviivvnNb+r1119Xbu754cs3bNigzZs3y2az6ac//akKIhl4nIuV9V71dpdWTZoUkjjGDSEOAEDsGZaaqkOTs4bURtbhQyGK5sJGpNh0zSO/G1IbzY/fHKJogPDi+x55sbDOQ3rcvmpoA0ck07492c6XEuk8taOjQ5I0YsSIIfXlJpdH2Zl5Q+hN8iKpF2aBQEArV67Uyy+/LKfTqby8PLndbmVnZ/eo197erp/+9KeaPXt2sKyhoUEVFRU6ePCg3nnnHd100006cpsl2zAT6W7EnVhZ74FuSyu3n9XL9TtCEkelPUU2w+cPAAAAhBLH7dGRbOs9kc5T9+/fr6ysLFmWpbfeektz5sy55L7Mmv4VlXyrXMOG2S66L8mO22/DrK6uTi6XSxMmTNDw4cNVXFysysrKXvXWrFmjhx56SCNGjAiWVVZWqri4WKmpqRo/frxcLpfqfFwePRixst7rfAG5HMNCFsdbH/9PCAAAAIDQ4bg9OpJtvSfSeeqbb76pyZMnKysrS2+88caQ+nLlmC+o+b3Dl9SXZEdSL8x8Pp8yMzOD751Op3w+X486+/btU0tLixYtWjTwvO1WeANOELGy3n3tljLH/G0zG2ocx7v8lxQHAAAAgP5x3B4dybbeE+k89ZN/P5l3KH1Ju+xKnf6o9ZL6kuy4/TbMLKv3RmY+dTlwd3e37r//fm3ZsmVw84Y0usQVK+u9j6b4/AEAAIAYw3F7dCTbek+k89RPx/PJvEPpS+x/erGJpF6YOZ1OtbS0BN97vV5lZGQE37e3t+vAgQOaP3++JOndd9+V2+3Wtm3b+p7XyRd9MGJlvTvHGLV80B2yOG6yp1xSHAAAAAD6x3F7dCTbek+k89T58+cH48nIyFBtbe0l96Xtw1aNHXnFJfUl2XH7bZjl5eWpsbFRTU1NOnfunCoqKuR2u4PTx44dq9bWVjU3N6u5uVlz5szRtm3blJubK7fbrYqKCnV2dqqpqUmNjY2a9QUeHDkYsbLe875gU+P73SGLY8qnnkMAAAAAIDQ4bo+OZFvviXSeOnXqVB06dEgNDQ2aPn36kPpy4rRP16RPvvQVm8RI6oWZ3W7Xpk2bVFBQoKysLBUVFSknJ0clJSXatm3bBefNyclRUVGRsrOz9bWvfU1lZWWMfDtIsbLe7cOMNhWOCF0cMTySEwAAABCvOG6PjmRb74l0njp16lQFAgF1d3fr2muvHVJfiub+d0a+vUTcfhsBhYWFKiws7FG2bt26PuvW1tb2eL969WqtXr36bwV7Qx1d4oqV9V44MUWFvzwSkjgO3f/ApQcCAAAAoF8ct0dHsq33RDxPHWobZSt2DyJi9IUr9QAAAAAAAIA4Q1IPAAAAAAAAiDMk9QAAAAAAAIA4Q1IvQjr8gWiHkJRiZb13BjqjHQIAAACAAXDcHh3Jtt4T6Ty1u6MjBJHgUjFQRoSMSLHpmkd+N6Q2mh+/OUTRJI9YWe+ptlRNKZ8ypDbeWvrWkOMAAAAA0D+O26Mj2dZ7op2nHpqcdcnzZx0+NKTlJzuu1AMAAABiUFVVlSZNmiSXy6XHH3+81/Qnn3xS2dnZmjp1qv7+7/9ef/3rX4PTysvLNXHiRE2cOFHl5eWRDBsAAEQIST0AAAAgxgQCAa1cuVI7duxQQ0ODtm7dqoaGhh51rrvuOtXX12v//v265ZZb9NBDD0mSTp48qdLSUu3du1d1dXUqLS1V21krGt0AAABhRFIPAAAAiDF1dXVyuVyaMGGChg8fruLiYlVWVvaoc+ONN2rkyJGSpDlz5sjr9UqSqqurlZ+fL4fDobS0NOXn56vq7a6I9wEAAIQXST0AAAAgxvh8PmVmZgbfO51O+Xy+futv3rxZCxcu7H/e9u7wBQsAAKKCgTIAAACAGGNZvW+XNcb0WfeFF15QfX29Xn311f7nDW14AAAgBnClHgAAABBjnE6nWlpagu+9Xq8yMjJ61du5c6d+8IMfaNu2bUpNTe1/3tEc9gMAkGj4dQcAAABiTF5enhobG9XU1KRz586poqJCbre7R519+/bpO9/5jrZt26b09PRgeUFBgWpqatTW1qa2tjbV1NSowMUNOgAAJBp+3QEAAIAYY7fbtWnTJhUUFCgQCMjj8SgnJ0clJSXKzc2V2+3Wgw8+qDNnzuib3/ymJGncuHHatm2bHA6H1vz/7d17kCVlecfx7y+Agloar0kUdJUQjBogZAFTipdEEkSiEjHuBi/USqGJmIi1Xgm4hSImgkoiaGkKCFZkTQgoIgQ1hhIjAUFWBBUR8ILGBDReMCsCPvmj38HeMzuwt3N6z5nvp2pqTr/dZ84zz/T05en37T76aPbaay8AjjnmGB70jVcN+etIkqQxsKgnSZIkbYUOOOAADjjggHXajj322Ltef/KTn1zwvStWrGDFihW/aFhlUU+SpFnj8FtJkiRJkiRpyljUkyRJkiRJkqaMRT1JkiRJkiRpyljUkyRJkrZSP739zqFDkCRJWykflCFJkiRtpbbfbhuWvP5jm/Uzvv62Z22haCRJ0tbEnnqSJEmSJEnSlLGoJ0mSJEmSJE0Zi3qSJEmSJEnSlLGoJ0mSJEmSJE0Zi3qSJEmSJEnSlLGoJ0mSJEmSJE0Zi3qSJEmSJEnSlLGoJ0mSJEmSJE2ZsRb1kuyf5NokX0vy+nF+liRJkiRJkrRYjK2ol2Qb4GTgmcDjgOVJHjeuz5MkSZIkSZIWi3H21Nsb+FpV3VBVPwNWA88Z4+dJkiRJkiRJi0Kqajw/ODkY2L+qDmvTLwL2qaojRpY7HDi8Te4KXDuWgKbDQ4Bbhg5iETLvk2fOh2Heh2Heh2Heh2HeJ8+cD8O8D8O8D8O8T545H8bWmvdHVdVD1zdj2zF+aNbTNq+CWFXvA943xjimRpLLq2rp0HEsNuZ98sz5MMz7MMz7MMz7MMz75JnzYZj3YZj3YZj3yTPnw5jGvI9z+O1NwE696R2B74zx8yRJkiRJkqRFYZxFvc8BuyR5dJJ7AcuAc8f4eZIkSZIkSdKiMLbht1V1R5IjgAuBbYBTq+qacX3ejHAY8jDM++SZ82GY92GY92GY92GY98kz58Mw78Mw78Mw75NnzocxdXkf24MyJEmSJEmSJI3HOIffSpIkSZIkSRoDi3qSJEmSJEnSlLGoN2FJbu29PjLJT5M8oNf2tCSV5I96becledqEQ50JSe5Msqb3taTl+Icj7c9IclGSPxx5/6uSnDJU/NOmrbsf6E1vm+TmJOeNLPeRJJeMtK1K8u329/hSkuWTintWJDmo/Q0e26aXJFnbcvqFJJ9NsmubN/d/cGWSryQ5Ydjop9cWWO9XTirWWZLk1rYej+b59CQHt9cXJbm8N29pkosmHOrM2YjcLx0mwtkxul1vbbu0Y8Prk1yR5N+TPKU3/7lJrmrb9i8mee4w0U+3JEcluablck2Sfdp6fW3v+PGsttzcdP+48y+G/h2mTZId277yurZ+n5TkXkkOTfLukWUvatv0S1u+v9n2vXcd8w/zW0yXtn05sTe9Msmq3vThbVvylSSXJXlya1+V5PiRn7VHki9PLPgp1ttWXNOO0V+d5JfavIXOVQ8aaVuT5OdJnjn076Nhje1BGdogy+meEnwQcHqv/SbgKOCjA8Q0a9ZW1R79hraTv7iqDhxp35nuKc0X9pqXAa8Zc4yz5CfAE5LsUFVrgf2Ab/cXSPLLwJ7ArUkeXVU39ma/s6pOSLILcEWSs6rq9olFP/2WA5+hW29Xtbbr5/4HkrwMeCPwkjbv4qo6MMkOwJVJzqmq/5hwzLNgc9d7jdfDkjyzqi4YOhBpE6yzXU+yPfAxYGVVnQuQ5AnAUuDTSXYHTgD2q6obkzwa+ESSG6rqqmF+hemT5HeBA4E9q+q2JA8B7tVmH1JVl4+85bj2vltHjzu1YZIEOBt4T1U9J8k2dDesPw5Y8GGLVbVPe/+hwNKqOmIC4c6S24A/TnJ8Vd3Sn5HkQOBlwJOr6pYkewIfTrI3cCZwAfCG3luWAR+cUNzTbm3v+PxhdHl7APCmNn/euWpzztyLJIcDh7DuuasWIXvqDaQVkO4H/BXdAVvfF4AfJtlv4oEtbmcBBya5N9xV/Hs43cG0NtwFwLPa6+V0O/2+59EVrFfT7fznqarrgP8DHjimGGdOkvsBTwJeygJ5Be4P/O9oYytErQEeMbYAZ99mr/cam7fT7WulqbLAdv0Q4JK5gh5AVV1dVae3yZXAW+cuHLTvx+MFyo31a8AtVXUbQFXdUlXfGTimWfd7wE+r6jSAqroTOBJYAdxnyMBm3B10xdMj1zPvdcBr5op9VfV54B+AV1TVtcAPkuzTW/5P6I5ztBGq6n+Aw4EjWnH7HiX5DeAY4EVV9fNxxqetn0W94cyd9F0M7Noq9H1vwZOQLWGHXvfkc3rt+450Xd65qr4HXAbs35ZZBnyofET0xloNLGu9CXYDLh2ZP7fun8n8gjYA7UrgdW0npw3zXOBfq+qrwPdbDgF2buv49cCrgXeMvjHJA4FdgE9PLNrZs9nrvcbmEuC2JE8fOhBpI61vu/544PN3857HA1eMtF3e2rXhPg7slOSrSU5J8tTevH/sHT++fagAZ9C8dbeqfgR8E0eXjdvJwCHp3RKquaftyZm0Cw5Jngh8r12Y10aqqhvoajNzNYF556pzyybZjq5n38qq+uYA4WorY1FvOMuA1a2yfjbw/P7MqroYIMm+A8Q2S9ZW1R7t66Be+8W99j2q6vrWftfOqX0f7W2je9CG9yyhK1yc35+X5FeAXwc+005S7mjDhuYcmeRauoLIqokEPDuW84uro6v5ReHo+raO7wy8iu5q7Jx9k1wFfBc4r6q+O7FoZ8xmrvfadAtddBlt90LZlrehudemW2i7fpck5yS5OsnZc03M/xusr013o6puBX6HrvfMzcCH2vBO6Ibfzh0/2gNyy1loPQ0Lj9xwvd4CWvH0DGBD7gPZ/zutBg5u94LzvGnz9XvpLXSuCvBm4JqqslekAIt6g0iyG12vmE8k+TrdRnB9PTeOo7u3nibnw8Dvt6vhO7Ru5tp459Ld02d05/4CugOzG9u6v4R1hyK+s6p2bcud0Xo96R4keTDdsJW/b3l9DV0OR7vwnws8pTd9cVXtBvwW8GdJvA/Q5tnU9V6b7nvMP9l7ELDOfYGq6lPA9sATJxTXYrBBudemuZvt+jV09+cEoF2wPJQu97T5ow8o2RP40ngjnj1VdWdVXVRVbwKOoLuNgsZn3rqb5P7ATsCVuL0Zt3fRDfW/b6/tS3TF7b67tidV9S3g68BT6f4//mnsUc6oJI8B7gTudpRSuodnPo9umyQBFvWGshxYVVVL2tfDgUckeVR/oar6ON0ObPchglyM2pXZi4BT8WrT5jgVOLaqvjjSvhzYf27dpztQmFfcqKqz6br3v2R0ntbrYOCMqnpUy+1OwI3AjiPLPRm4fvTNrffY8XT3TtGm26z1XpvkOuDhSX4ToO1Hd6e7R+So44DXTjC2WbcxudfGW2i7/lXgSUme3Vu2f7+xE4A3tPsCz90f+I3AiWiDJdm1PbRrzh7AN4aKZ5H4N+A+SV4M0B6UcSLdwwQvpVvvf7XNWwrcG/jWMKHOnqr6Pl1R7qW95r8B/rpdZKBd/D0UOKW3zJnAO+lGhtw0mWhnS5KHAu8F3n13t31qt8s5DXhxVf14UvFp6+f9CSYoybZ0TxhaBow+evqc1j56H6bjgI+MP7pFZ98k/ROPt1TVWe31mXRDoj3p3kRtp35Sv62dWDwS+M/ecjcm+dHITXbnHAt8MMn7vQHsPVoOvG2k7V/oTuR2but6gJ8Bhy3wM94LrPTJrJtuC6332gBz+9P2VMoXAqe1nr23A4dV1Q9H31NV5ye5edKxzpqNzP3Hksw9wfySqnr+6M/Tghbarv8p3VNZ35HkXcB/Az+mG2JOVa1J8jrgo+2+S7cDr60qi60b537A37Unl98BfI1uKO5ZdPfUW9uWu6WqnjFQjDOlqirJQcApSY6m63xyPvDGtr35S+D8NtTzVmC5x4db3In0eoBV1blJHgF8NknRbWteWFX/1XvPP9Md+7xyopFOvx3a8fl2dNuYD7Dufa/nnavSjfR7GPCekedpHF9VHxpzvNqKxWcATE6S3YH3V9XeQ8ciSdK0cn86HHMvSZK09XD47YQkeTldDzBv1C1J0iZyfzoccy9JkrR1saeeJEmSJEmSNGXsqSdJkiRJkiRNGYt6kiRJkiRJ0pSxqCdJkqR5kvx5kp2HjkOSJEnrZ1FPkiRpEUlSST7Qm942yc1Jzuu1vQL4AXB0kvsOEackSZLu3rZDByBJkqSJ+gnwhCQ7VNVaYD/g2/0Fqurk9vKDkw5OkiRJG8aeepIkSYvPBcCz2uvlwJlzM5LcN8mpST6X5Mokz2ntj09yWZI1Sa5KsssAcUuSJKmxqCdJkrT4rAaWJdke2A24tDfvKOBTVbUX8HTg7W0I7suBk6pqD2ApcNOEY5YkSVKPw28lSZIWmaq6KskSul5654/M/gPg2UlWtuntgUcClwBHJdkROLuqrptQuJIkSVoPi3qSJEmL07nACcDTgAf32gM8r6quHVn+y0kupRu2e2GSw6rqUxOJVJIkSfM4/FaSJGlxOhU4tqq+ONJ+IfDKJAFI8tvt+2OAG6rqb+kKgrtNMlhJkiSty6KeJEnSIlRVN1XVSeuZ9WZgO+CqJFe3abh7HNwAAABvSURBVIAXAFcnWQM8FjhjMpFKkiRpfVJVQ8cgSZIkSZIkaSPYU0+SJEmSJEmaMhb1JEmSJEmSpCljUU+SJEmSJEmaMhb1JEmSJEmSpCljUU+SJEmSJEmaMhb1JEmSJEmSpCljUU+SJEmSJEmaMv8PTecACLmuiHkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1584x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (22,10)\n",
    "barWidth = 0.18  # the width of the bars\n",
    "\n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(12)\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    "# r4 = [x + barWidth for x in r3]\n",
    "#r5 = [x + barWidth for x in r4]\n",
    "r6 = [x + barWidth for x in r3]\n",
    "#r7 = [x + barWidth for x in r6]\n",
    "r8 = [x + barWidth for x in r6]\n",
    "#r9 = [x + barWidth for x in r8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(r1, hd.values.tolist(), width=barWidth, edgecolor='white', label='hd')\n",
    "rects2 = ax.bar(r2, fonte.values.tolist(), width=barWidth, edgecolor='white', label='fonte')\n",
    "rects3 = ax.bar(r3, ram.values.tolist(), width=barWidth, edgecolor='white', label='ram')\n",
    "# rects4 = ax.bar(r4, placaR.values.tolist(), width=barWidth, edgecolor='white', label='placa de rede')\n",
    "#rects5 = ax.bar(r5, placaV.values.tolist(), width=barWidth, edgecolor='white', label='placa de video')\n",
    "rects6 = ax.bar(r6, placaM.values.tolist(), width=barWidth, edgecolor='white', label='placa mãe')\n",
    "#rects7 = ax.bar(r7, cabo.values.tolist(), width=barWidth, edgecolor='white', label='cabos em geral')\n",
    "rects8 = ax.bar(r8, nobreak.values.tolist(), width=barWidth, edgecolor='white', label='nobreak')\n",
    "#rects9 = ax.bar(r9, estabilizador.values.tolist(), width=barWidth, edgecolor='white', label='estabilizador')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "autolabel(rects3)\n",
    "#autolabel(rects4)\n",
    "#autolabel(rects5)\n",
    "autolabel(rects6)\n",
    "#autolabel(rects7)\n",
    "autolabel(rects8)\n",
    "#autolabel(rects9)\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Quantidade')\n",
    "ax.set_xlabel('Mês')\n",
    "ax.set_title('Quantidade de problemas precificavéis por meses')\n",
    "ax.set_xticks((0,1,2,3,4,5,6,7,8,9,10,11,12))\n",
    "ax.set_xticklabels(['JAN', 'FEV', 'MAR', 'ABR', 'MAI', 'JUN', 'JUL', 'AGO', 'SET', 'OUT', 'NOV', 'DEZ'])\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definição dos mínimos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'hd')]['OPEN_YEAR_1'].value_counts().min())\n",
    "fonte_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'fonte')]['OPEN_YEAR_1'].value_counts().min())\n",
    "# bateria_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'bateria')]['OPEN_YEAR_1'].value_counts().min())\n",
    "ram_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'ram')]['OPEN_YEAR_1'].value_counts().min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "placaR_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de rede')]['OPEN_YEAR_1'].value_counts().min())\n",
    "placaV_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de video')]['OPEN_YEAR_1'].value_counts().min())\n",
    "placaM_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa')]['OPEN_YEAR_1'].value_counts().min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "fonte_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'fonte')]['OPEN_YEAR_1'].value_counts().min())\n",
    "# bateria_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'bateria')]['OPEN_YEAR_1'].value_counts().min())\n",
    "ram_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'ram')]['OPEN_YEAR_1'].value_counts().min())\n",
    "placaR_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de rede')]['OPEN_YEAR_1'].value_counts().min())\n",
    "placaV_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de video')]['OPEN_YEAR_1'].value_counts().min())\n",
    "placaM_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa')]['OPEN_YEAR_1'].value_counts().min())\n",
    "# cabo_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'cabo')]['OPEN_YEAR_1'].value_counts().min())\n",
    "nobreak_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'nobreak')]['OPEN_YEAR_1'].value_counts().min())\n",
    "estabilizador_min = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'estabilizador')]['OPEN_YEAR_1'].value_counts().min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definição das médias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'hd')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "fonte_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'fonte')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "# bateria_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'bateria')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "ram_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'ram')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "placaR_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de rede')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "placaV_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de video')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "placaM_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "# cabo_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'cabo')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "nobreak_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'nobreak')]['OPEN_YEAR_1'].value_counts().mean())\n",
    "estabilizador_mid = round(df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'estabilizador')]['OPEN_YEAR_1'].value_counts().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definição dos máximos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'hd')]['OPEN_YEAR_1'].value_counts().max()\n",
    "fonte_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'fonte')]['OPEN_YEAR_1'].value_counts().max()\n",
    "# bateria_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'bateria')]['OPEN_YEAR_1'].value_counts().max()\n",
    "ram_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'ram')]['OPEN_YEAR_1'].value_counts().max()\n",
    "placaR_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de rede')]['OPEN_YEAR_1'].value_counts().max()\n",
    "placaV_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa de video')]['OPEN_YEAR_1'].value_counts().max()\n",
    "placaM_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'placa')]['OPEN_YEAR_1'].value_counts().max()\n",
    "# cabo_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['ESPC'] == 'cabo')]['OPEN_YEAR_1'].value_counts().max()\n",
    "nobreak_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'nobreak')]['OPEN_YEAR_1'].value_counts().max()\n",
    "estabilizador_max = df[(df['HOUR_EFIC'] > 0) & (df['HAS_VALUE'] == 'P') & (df['OPEN_YEAR_1'] >= 2015) & (df['EQUIP'] == 'estabilizador')]['OPEN_YEAR_1'].value_counts().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "32\n",
      "1\n",
      "5\n",
      "1\n",
      "11\n",
      "-------------\n",
      "10\n",
      "65\n",
      "12\n",
      "33\n",
      "3\n",
      "20\n",
      "-------------\n",
      "18\n",
      "86\n",
      "20\n",
      "64\n",
      "7\n",
      "33\n"
     ]
    }
   ],
   "source": [
    "print(hd_min)\n",
    "print(fonte_min)\n",
    "print(ram_min)\n",
    "print(nobreak_min)\n",
    "print(placaR_min)\n",
    "print(placaM_min)\n",
    "print(\"-------------\")\n",
    "print(hd_mid)\n",
    "print(fonte_mid)\n",
    "print(ram_mid)\n",
    "print(nobreak_mid)\n",
    "print(placaR_mid)\n",
    "print(placaM_mid)\n",
    "\n",
    "print(\"-------------\")\n",
    "print(hd_max)\n",
    "print(fonte_max)\n",
    "print(ram_max)\n",
    "print(nobreak_max)\n",
    "print(placaR_max)\n",
    "print(placaM_max)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otimização Multiobjetivo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Minimixar f(x1,x2,x3,x4,x5,x6) = - x1 - 0*x2 - 0*x3 - 0*x4 - 0*x3 - 0*x6;\n",
    "- Minimixar f(x1,x2,x3,x4,x5,x6) = - 0*x1 - x2 - 0*x3 - 0*x4 - 0*x3 - 0*x6; \n",
    "- Minimixar f(x1,x2,x3,x4,x5,x6) = - 0*x1 - 0*x2 - x3 - 0*x4 - 0*x3 - 0*x6; \n",
    "- Minimixar f(x1,x2,x3,x4,x5,x6) = - 0*x1 - 0*x2 - 0*x3 - x4 - 0*x3 - 0*x6; \n",
    "- Minimixar f(x1,x2,x3,x4,x5,x6) = - 0*x1 - 0*x2 - 0*x3 - 0*x4 - x3 - 0*x6; \n",
    "- Minimixar f(x1,x2,x3,x4,x5,x6) = - 0*x1 - 0*x2 - 0*x3 - 0*x4 - 0*x3 - x6; "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- g(x1,x2,x3,x4,x3,x6) = - x1*valueX1 - x2*valueX2 - x3*valueX3 - x4*valueX4 - x5*valueX5 - x6*valueX6 >= Orcamento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- f(x1,x2,x3,x4,x5,x6) → x1 <= min.x2ear(x1) or x1 <= Minimixar.year(x1)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x2 <= min.x2ear(x2) or x2 <= Minimixar.year(x2)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x3 <= min.x2ear(x3) or x3 <= Minimixar.year(x3)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x4 <= min.x2ear(x4) or x4 <= Minimixar.year(x4)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x3 <= min.x2ear(x3) or x3 <= Minimixar.year(x3)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x6 <= min.x2ear(x6) or x6 <= Minimixar.year(x6)\n",
    "\n",
    "- f(x1,x2,x3,x4,x5,x6) → x1 >= 0 or x1 >= min.year(x1)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x2 >= 0 or x2 <= min.year(x2)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x3 >= 0 or x3 <= min.year(x3)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x4 >= 0 or x4 >= min.year(x4)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x3 >= 0 or x3 >= min.year(x3)\n",
    "- f(x1,x2,x3,x4,x5,x6) → x6 >= 0 or x6 >= min.year(x6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* x1 = hd;\n",
    "* x2 = fonte;\n",
    "* x3 = ram;\n",
    "* x4 = nb; \n",
    "* x5 = placaR;\n",
    "* x6 = placaM;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "preco1 = 90.15   #valor do hd na loja Americanas\n",
    "preco2 = 199.49   #valor da fonte na loja Americanas\n",
    "preco3 = 139.15  #valor da memoria ram na loja Shopee\n",
    "preco4 = 823.41  #valor do Nobreak na loja KaBuM\n",
    "preco5 = 38.71   #valor da placa de rede na loja Americanas\n",
    "preco6 = 249.90  #valor da placa mãe no Mercado Livre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install pulp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install -c conda-forge autograd\n",
    "# first, import PuLP\n",
    "import pulp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal\n",
      "\n",
      "Soma total = 127\n",
      "\n",
      "Quantidade de HD = 18\n",
      "Quantidade de Fonte de Computador = 66\n",
      "Quantidade de Memoria Ram = 20\n",
      "Quantidade de Nobreak = 5\n",
      "Quantidade de Placa de Rede = 7\n",
      "Quantidade de Placa Mãe = 11\n",
      "\n",
      "Total gasto R$: 24750\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# then, conduct initial declaration of problem\n",
    "linearProblem = pulp.LpProblem(\"Maximizing for first objective\",pulp.LpMaximize)\n",
    "\n",
    "# delcare optimization variables, using PuLP\n",
    "# x1 = pulp.LpVariable(\"x1\", lowBound=hd_min, upBound=hd_max)  \n",
    "# x2 = pulp.LpVariable(\"x2\", lowBound=fonte_min, upBound=fonte_max)\n",
    "# x3 = pulp.LpVariable(\"x3\", lowBound=ram_min, upBound=ram_max)\n",
    "# x4 = pulp.LpVariable(\"x4\", lowBound=nobreak_min, upBound=nobreak_max)\n",
    "# x5 = pulp.LpVariable(\"x5\", lowBound=placaR_min, upBound=placaR_max)\n",
    "# x6 = pulp.LpVariable(\"x6\", lowBound=placaM_min, upBound=placaM_max)\n",
    "\n",
    "x1 = pulp.LpVariable(\"x1\", lowBound=hd_min, upBound=hd_max)\n",
    "x2 = pulp.LpVariable(\"x2\", lowBound=fonte_min, upBound=fonte_max)\n",
    "x3 = pulp.LpVariable(\"x3\", lowBound=ram_min, upBound=ram_max)\n",
    "x4 = pulp.LpVariable(\"x4\", lowBound=nobreak_min, upBound=nobreak_max)\n",
    "x5 = pulp.LpVariable(\"x5\", lowBound=placaR_min, upBound=placaR_max)\n",
    "x6 = pulp.LpVariable(\"x6\", lowBound=placaM_min, upBound=placaM_max)\n",
    "\n",
    "# add (first) objective function to the linear problem statement\n",
    "linearProblem += x1 + x2 + x3 + x4 + x5 + x6\n",
    "\n",
    "# add the constraints to the problem\n",
    "linearProblem += x1*preco1 + x2*preco2 + x3*preco3 + x4*preco4 + x5*preco5 + x6*preco6 <= 24750\n",
    "\n",
    "# solve with default solver, maximizing the first objective\n",
    "solution = linearProblem.solve()\n",
    "\n",
    "# output information if optimum was found, what the maximal objective value is and what the optimal point is\n",
    "print(str(pulp.LpStatus[solution])+\n",
    "      \"\\n\\nSoma total = \"+str(round(pulp.value(linearProblem.objective)))+\n",
    "      \"\\n\\nQuantidade de HD = \"+str(round(pulp.value(x1)))+\n",
    "      \"\\nQuantidade de Fonte de Computador = \"+str(round(pulp.value(x2)))+\n",
    "      \"\\nQuantidade de Memoria Ram = \"+str(round(pulp.value(x3)))+\n",
    "      \"\\nQuantidade de Nobreak = \"+str(round(pulp.value(x4)))+\n",
    "      \"\\nQuantidade de Placa de Rede = \"+str(round(pulp.value(x5)))+\n",
    "      \"\\nQuantidade de Placa Mãe = \"+str(round(pulp.value(x6))))\n",
    "valor = pulp.value(x1)*preco1 + pulp.value(x2)*preco2 + pulp.value(x3)*preco3 + pulp.value(x4)*preco4 + pulp.value(x5)*preco5 + pulp.value(x6)*preco6\n",
    "print(\"\\nTotal gasto R$: \"+str(round(valor)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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