executivo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salários do Executivo Federal Brasileiro\n",
    "\n",
    "Esse notebook tem com objetivo demonstrar o acesso aos dados referentes a folha salárial dos servidores do executivo, disponibilizados pelo [Portal da Transparência do Governo Brasileiro][portal1].\n",
    "\n",
    "Para a aquisição e análise dos dados, será utilizada a linguagem de programação Python e as bibliotecas Pandas, Numpy e Matplotlib. Uma forma conveniente de instalar tudo o que é necessário para executar esse notebook é baixar o [Anaconda][anaconda1] da Continuum Analytics.\n",
    "\n",
    "[portal1]: <http://www.portaldatransparencia.gov.br> \n",
    "[anaconda1]: <https://www.continuum.io/downloads>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O arquivo mensal referente aos salários do executivo é disponibilizado no formato *.zip*, dentro desse arquivo dois são de nosso interesse:\n",
    "\n",
    " - yyyymmdd_Cadastro.csv\n",
    " - yyyymmdd_Remuneracao.csv\n",
    "\n",
    "As variáveis a seguir servem para facilitar a utilização desses arquivos e estão configuradas para os dados do mês de Novembro de 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RECORDS = \"20151130_Cadastro.csv\"\n",
    "SALARIES = \"20151130_Remuneracao.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O primeiro passo é obter o arquivo *.zip* que contém os dados referentes aos salários. Para isso, vamos fazer uma requisição do tipo **GET** para http://arquivos.portaldatransparencia.gov.br/downloads.asp?a=2015&m=11&d=C&consulta=Servidores , onde ficam os dados relativos ao mês de Novembro de 2015.\n",
    "\n",
    "Foi utilizada a biblioteca urllib para fazer o download do arquivo, e a biblioteca zipfile para extrair os arquivos referentes ao cadastro de funcionários e aos salários."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "import zipfile\n",
    "\n",
    "#downloading our dataset\n",
    "urllib.urlretrieve(\"http://arquivos.portaldatransparencia.gov.br/downloads.asp?a=2015&m=11&d=C&consulta=Servidores\", \"nov15.zip\")\n",
    "\n",
    "# extracting our files\n",
    "with zipfile.ZipFile(\"nov15.zip\", \"r\") as zip_contents:\n",
    "    #zip_contents.printdir()\n",
    "    zip_contents.extract( RECORDS )\n",
    "    zip_contents.extract( SALARIES )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de começar a trabalhar com os arquivos, vamos importar as bibliotecas que serão utilizadas, bem como inicializar configurações de ambiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Portuguese_Brazil.1252'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import locale\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "locale.setlocale(locale.LC_NUMERIC, 'Portuguese_Brazil.1252')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depois de configurado o ambiente, é necessário carregar nosso *dataset*. A biblioteca Pandas possuí o método **read_csv** que permite a leitura de um arquivo CSV estruturado, de forma rápida e flexível.\n",
    "\n",
    "Embora o Portal da Transparência indique que o cabeçalho/formato do arquivo CSV é o seguinte:\n",
    "\n",
    "    ANO; MES; ID_SERVIDOR_PORTAL; CPF; NOME; REMUNERAÇÃO BÁSICA BRUTA (R$); REMUNERAÇÃO BÁSICA BRUTA (U$); ABATE-TETO (R$); ABATE-TETO (U$); GRATIFICAÇÃO NATALINA (R$); GRATIFICAÇÃO NATALINA (U$); ABATE-TETO DA GRATIFICAÇÃO NATALINA (R$); ABATE-TETO DA GRATIFICAÇÃO NATALINA (U$); FÉRIAS (R$); FÉRIAS (U$); OUTRAS REMUNERAÇÕES EVENTUAIS (R$); OUTRAS REMUNERAÇÕES EVENTUAIS (U$); IRRF (R$); IRRF (U$); PSS/RPGS (R$); PSS/RPGS (U$); PENSÃO MILITAR (R$); PENSÃO MILITAR (U$); FUNDO DE SAÚDE (R$); FUNDO DE SAÚDE (U$); DEMAIS DEDUÇÕES (R$); DEMAIS DEDUÇÕES (U$); REMUNERAÇÃO APÓS DEDUÇÕES OBRIGATÓRIAS (R$); REMUNERAÇÃO APÓS DEDUÇÕES OBRIGATÓRIAS (U$); VERBAS INDENIZATÓRIAS REGISTRADAS EM SISTEMAS DE PESSOAL - CIVIL (R$); VERBAS INDENIZATÓRIAS REGISTRADAS EM SISTEMAS DE PESSOAL - CIVIL (U$); VERBAS INDENIZATÓRIAS REGISTRADAS EM SISTEMAS DE PESSOAL - MILITAR (R$); VERBAS INDENIZATÓRIAS REGISTRADAS EM SISTEMAS DE PESSOAL - MILITAR (U$); TOTAL DE VERBAS INDENIZATÓRIAS (R$); TOTAL DE VERBAS INDENIZATÓRIAS (U$); TOTAL DE HONORÁRIOS (JETONS)\n",
    "\n",
    "Na verdade, o arquivo dos salários é um arquivo CSV separado por tabulações (**\\t**) e codificado em *latin-1*. Passaremos esses parâmetros para a função read_csv, bem como um conversor de valores para transformar os salários do formato DDD **,** DD para o formato DDD **.** DD.\n",
    "\n",
    "Como desejamos analizar apenas a remuneração básica bruta dos servidores, não levando em consideração descontos, outras remunerações eventuais, férias, 13º (gratificação natalina), bônus de produtividade, e etc., iremos importar apenas dois campos desse arquivo. O campo **NOME** e o campo **REMUNERAÇÃO BÁSICA BRUTA (R$)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NOME</th>\n",
       "      <th>REMUNERAÇÃO BÁSICA BRUTA (R$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANA REGINA E SOUZA CAMPELLO</td>\n",
       "      <td>8639.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANA MARIA ALMEIDA BRITO</td>\n",
       "      <td>29060.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CARLOS THOME</td>\n",
       "      <td>3536.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SERGIO GEBARA RAMOS</td>\n",
       "      <td>2173.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MARCIO REZENDE FERREIRA</td>\n",
       "      <td>4348.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DANILO MEDEIROS DE SANTANA COLLADO</td>\n",
       "      <td>4292.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RITA LIMA DE ALMEIDA</td>\n",
       "      <td>8650.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ADEMIR ALVES FONSECA</td>\n",
       "      <td>4980.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JANE DALLA CORTE</td>\n",
       "      <td>20174.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BARBARA ALVES MARTINS</td>\n",
       "      <td>7928.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 NOME  REMUNERAÇÃO BÁSICA BRUTA (R$)\n",
       "0         ANA REGINA E SOUZA CAMPELLO                        8639.50\n",
       "1             ANA MARIA ALMEIDA BRITO                       29060.91\n",
       "2                        CARLOS THOME                        3536.88\n",
       "3                 SERGIO GEBARA RAMOS                        2173.85\n",
       "4             MARCIO REZENDE FERREIRA                        4348.75\n",
       "5  DANILO MEDEIROS DE SANTANA COLLADO                        4292.58\n",
       "6                RITA LIMA DE ALMEIDA                        8650.81\n",
       "7                ADEMIR ALVES FONSECA                        4980.03\n",
       "8                    JANE DALLA CORTE                       20174.08\n",
       "9               BARBARA ALVES MARTINS                        7928.28"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing only usefull columns for us (NOME and REMUNERAÇÃO BÁSICA BRUTA)\n",
    "salaries = pd.read_csv( SALARIES , sep=\"\\t\", encoding='latin1', quoting=3, na_filter=True, \n",
    "                        keep_default_na=False, error_bad_lines=False, na_values=[\"\"],\n",
    "                        converters={ u\"REMUNERAÇÃO BÁSICA BRUTA (R$)\": lambda x: float(x.replace(',','.')) },\n",
    "                        usecols = [u'NOME',u'REMUNERAÇÃO BÁSICA BRUTA (R$)'],\n",
    "                      )\n",
    "\n",
    "\n",
    "salaries[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizaremos a mesma função para fazer a leitura do cadastro de funcionários.\n",
    "\n",
    "Dessa vez importaremos quatro campos para a nossa análise: NOME, CARGO, DATA_INGRESSO_SERVIÇO_PUBLICO e ORGSUP_EXERCICIO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NOME</th>\n",
       "      <th>DESCRICAO_CARGO</th>\n",
       "      <th>ORGSUP_EXERCICIO</th>\n",
       "      <th>DATA_DIPLOMA_INGRESSO_SERVICOPUBLICO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>THACIZO AUGUSTHO EUGENIO DOURADO MEIRA MACHADO</td>\n",
       "      <td>\u0000</td>\n",
       "      <td>MINISTERIO DA SAUDE</td>\n",
       "      <td>14/03/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CARLOS ALBERTO LIMONGI DAS CHAGAS JUNIOR</td>\n",
       "      <td>\u0000</td>\n",
       "      <td>MINIST.DO DESENV.INDUST.E COMER.EXTERIOR</td>\n",
       "      <td>21/10/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GUSTAVO GALVAO DE OLIVEIRA AVILA</td>\n",
       "      <td>\u0000</td>\n",
       "      <td>MINISTERIO DA EDUCACAO</td>\n",
       "      <td>N�o informada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GUSTAVO GALVAO DE OLIVEIRA AVILA</td>\n",
       "      <td>ANALISTA DE TI-PROCESSO E DESENVOLVIM</td>\n",
       "      <td>MINISTERIO DA EDUCACAO</td>\n",
       "      <td>N�o informada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JOSE CLAUDIO EPAMINONDAS DOS SANTOS</td>\n",
       "      <td>ANALISTA EM DESENVOLVIMENTO REGIONAL</td>\n",
       "      <td>MINISTERIO DA INTEGRACAO NACIONAL</td>\n",
       "      <td>N�o informada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JOSE PEREIRA DA SILVA</td>\n",
       "      <td>ARTIF DE ESTRUT DE OBRAS E METALURGIA</td>\n",
       "      <td>MINISTERIO DA DEFESA</td>\n",
       "      <td>01/06/1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANA REGINA E SOUZA CAMPELLO</td>\n",
       "      <td>PROFESSOR DO MAGISTERIO SUPERIOR</td>\n",
       "      <td>MINISTERIO DA EDUCACAO</td>\n",
       "      <td>17/02/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WIRIAM WONDER OLIVEIRA MACEDO</td>\n",
       "      <td>\u0000</td>\n",
       "      <td>MINISTERIO DO MEIO AMBIENTE</td>\n",
       "      <td>01/08/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SIMONE PORTO DA SILVA</td>\n",
       "      <td>AGENTE DE COMBATE AS ENDEMIAS</td>\n",
       "      <td>MINISTERIO DA SAUDE</td>\n",
       "      <td>28/06/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ANA MARIA ALMEIDA BRITO</td>\n",
       "      <td>ANALISTA DE PLANEJAMENTO E ORCAMENTO</td>\n",
       "      <td>MINISTERIO DO PLANEJ.,ORCAMENTO E GESTAO</td>\n",
       "      <td>10/05/1988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             NOME  \\\n",
       "0  THACIZO AUGUSTHO EUGENIO DOURADO MEIRA MACHADO   \n",
       "1        CARLOS ALBERTO LIMONGI DAS CHAGAS JUNIOR   \n",
       "2                GUSTAVO GALVAO DE OLIVEIRA AVILA   \n",
       "3                GUSTAVO GALVAO DE OLIVEIRA AVILA   \n",
       "4             JOSE CLAUDIO EPAMINONDAS DOS SANTOS   \n",
       "5                           JOSE PEREIRA DA SILVA   \n",
       "6                     ANA REGINA E SOUZA CAMPELLO   \n",
       "7                   WIRIAM WONDER OLIVEIRA MACEDO   \n",
       "8                           SIMONE PORTO DA SILVA   \n",
       "9                         ANA MARIA ALMEIDA BRITO   \n",
       "\n",
       "                         DESCRICAO_CARGO  \\\n",
       "0                                      \u0000   \n",
       "1                                      \u0000   \n",
       "2                                      \u0000   \n",
       "3  ANALISTA DE TI-PROCESSO E DESENVOLVIM   \n",
       "4   ANALISTA EM DESENVOLVIMENTO REGIONAL   \n",
       "5  ARTIF DE ESTRUT DE OBRAS E METALURGIA   \n",
       "6       PROFESSOR DO MAGISTERIO SUPERIOR   \n",
       "7                                      \u0000   \n",
       "8          AGENTE DE COMBATE AS ENDEMIAS   \n",
       "9   ANALISTA DE PLANEJAMENTO E ORCAMENTO   \n",
       "\n",
       "                           ORGSUP_EXERCICIO  \\\n",
       "0                       MINISTERIO DA SAUDE   \n",
       "1  MINIST.DO DESENV.INDUST.E COMER.EXTERIOR   \n",
       "2                    MINISTERIO DA EDUCACAO   \n",
       "3                    MINISTERIO DA EDUCACAO   \n",
       "4         MINISTERIO DA INTEGRACAO NACIONAL   \n",
       "5                      MINISTERIO DA DEFESA   \n",
       "6                    MINISTERIO DA EDUCACAO   \n",
       "7               MINISTERIO DO MEIO AMBIENTE   \n",
       "8                       MINISTERIO DA SAUDE   \n",
       "9  MINISTERIO DO PLANEJ.,ORCAMENTO E GESTAO   \n",
       "\n",
       "  DATA_DIPLOMA_INGRESSO_SERVICOPUBLICO  \n",
       "0                           14/03/2014  \n",
       "1                           21/10/2014  \n",
       "2                        N�o informada  \n",
       "3                        N�o informada  \n",
       "4                        N�o informada  \n",
       "5                           01/06/1984  \n",
       "6                           17/02/2010  \n",
       "7                           01/08/2015  \n",
       "8                           28/06/2006  \n",
       "9                           10/05/1988  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing only usefull columns for us (NOME, CARGO, DATA INGRESSO SERVICOPUBLICO and ORGSUP_EXERCICIO)\n",
    "records = pd.read_csv( RECORDS , sep=\"\\t+\", engine='python', encoding='latin1',\n",
    "                       usecols=[u'NOME', u'DESCRICAO_CARGO',u'DATA_DIPLOMA_INGRESSO_SERVICOPUBLICO','ORGSUP_EXERCICIO'],\n",
    "                      )\n",
    "\n",
    "records[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dois datasets carregados (*salaries* e *records*) contém informação que se complementa. Porém eles ainda não estão relacionados. É necessário \"colar\" os datasets. Usaremos a função **merge** da biblioteca pandas para formar o novo dataset e definiremos que essa união se dará pelo campo **NOME**.\n",
    "\n",
    "Como resultado, teremos uma nova tabela, agora com 5 campos (Nome, Cargo, Orgão, Data de Ingresso, Remuneração)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NOME</th>\n",
       "      <th>REMUNERAÇÃO BÁSICA BRUTA (R$)</th>\n",
       "      <th>DESCRICAO_CARGO</th>\n",
       "      <th>ORGSUP_EXERCICIO</th>\n",
       "      <th>DATA_DIPLOMA_INGRESSO_SERVICOPUBLICO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANA REGINA E SOUZA CAMPELLO</td>\n",
       "      <td>8639.50</td>\n",
       "      <td>PROFESSOR DO MAGISTERIO SUPERIOR</td>\n",
       "      <td>MINISTERIO DA EDUCACAO</td>\n",
       "      <td>17/02/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANA MARIA ALMEIDA BRITO</td>\n",
       "      <td>29060.91</td>\n",
       "      <td>ANALISTA DE PLANEJAMENTO E ORCAMENTO</td>\n",
       "      <td>MINISTERIO DO PLANEJ.,ORCAMENTO E GESTAO</td>\n",
       "      <td>10/05/1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANA MARIA ALMEIDA BRITO</td>\n",
       "      <td>29060.91</td>\n",
       "      <td>\u0000</td>\n",
       "      <td>MINISTERIO DOS TRANSPORTES</td>\n",
       "      <td>19/05/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CARLOS THOME</td>\n",
       "      <td>3536.88</td>\n",
       "      <td>ASSISTENTE EM ADMINISTRACAO</td>\n",
       "      <td>MINISTERIO DA EDUCACAO</td>\n",
       "      <td>31/12/2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SERGIO GEBARA RAMOS</td>\n",
       "      <td>2173.85</td>\n",
       "      <td>PROFESSOR MAGISTERIO SUPERIOR-SUBSTITUTO</td>\n",
       "      <td>MINISTERIO DA EDUCACAO</td>\n",
       "      <td>30/04/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MARCIO REZENDE FERREIRA</td>\n",
       "      <td>4348.75</td>\n",
       "      <td>\u0000</td>\n",
       "      <td>MINISTERIO DO TRABALHO E EMPREGO</td>\n",
       "      <td>06/05/2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MARCIO REZENDE FERREIRA</td>\n",
       "      <td>4348.75</td>\n",
       "      <td>AGENTE ADMINISTRATIVO</td>\n",
       "      <td>MINISTERIO DO TRABALHO E EMPREGO</td>\n",
       "      <td>06/05/2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DANILO MEDEIROS DE SANTANA COLLADO</td>\n",
       "      <td>4292.58</td>\n",
       "      <td>ASSISTENTE EM ADMINISTRACAO</td>\n",
       "      <td>MINISTERIO DA EDUCACAO</td>\n",
       "      <td>30/09/2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RITA LIMA DE ALMEIDA</td>\n",
       "      <td>8650.81</td>\n",
       "      <td>ANALISTA AMBIENTAL</td>\n",
       "      <td>MINISTERIO DO MEIO AMBIENTE</td>\n",
       "      <td>23/06/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ADEMIR ALVES FONSECA</td>\n",
       "      <td>4980.03</td>\n",
       "      <td>ASSISTENTE EM ADMINISTRACAO</td>\n",
       "      <td>MINISTERIO DA EDUCACAO</td>\n",
       "      <td>01/01/1982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 NOME  REMUNERAÇÃO BÁSICA BRUTA (R$)  \\\n",
       "0         ANA REGINA E SOUZA CAMPELLO                        8639.50   \n",
       "1             ANA MARIA ALMEIDA BRITO                       29060.91   \n",
       "2             ANA MARIA ALMEIDA BRITO                       29060.91   \n",
       "3                        CARLOS THOME                        3536.88   \n",
       "4                 SERGIO GEBARA RAMOS                        2173.85   \n",
       "5             MARCIO REZENDE FERREIRA                        4348.75   \n",
       "6             MARCIO REZENDE FERREIRA                        4348.75   \n",
       "7  DANILO MEDEIROS DE SANTANA COLLADO                        4292.58   \n",
       "8                RITA LIMA DE ALMEIDA                        8650.81   \n",
       "9                ADEMIR ALVES FONSECA                        4980.03   \n",
       "\n",
       "                            DESCRICAO_CARGO  \\\n",
       "0          PROFESSOR DO MAGISTERIO SUPERIOR   \n",
       "1      ANALISTA DE PLANEJAMENTO E ORCAMENTO   \n",
       "2                                         \u0000   \n",
       "3               ASSISTENTE EM ADMINISTRACAO   \n",
       "4  PROFESSOR MAGISTERIO SUPERIOR-SUBSTITUTO   \n",
       "5                                         \u0000   \n",
       "6                     AGENTE ADMINISTRATIVO   \n",
       "7               ASSISTENTE EM ADMINISTRACAO   \n",
       "8                        ANALISTA AMBIENTAL   \n",
       "9               ASSISTENTE EM ADMINISTRACAO   \n",
       "\n",
       "                           ORGSUP_EXERCICIO  \\\n",
       "0                    MINISTERIO DA EDUCACAO   \n",
       "1  MINISTERIO DO PLANEJ.,ORCAMENTO E GESTAO   \n",
       "2                MINISTERIO DOS TRANSPORTES   \n",
       "3                    MINISTERIO DA EDUCACAO   \n",
       "4                    MINISTERIO DA EDUCACAO   \n",
       "5          MINISTERIO DO TRABALHO E EMPREGO   \n",
       "6          MINISTERIO DO TRABALHO E EMPREGO   \n",
       "7                    MINISTERIO DA EDUCACAO   \n",
       "8               MINISTERIO DO MEIO AMBIENTE   \n",
       "9                    MINISTERIO DA EDUCACAO   \n",
       "\n",
       "  DATA_DIPLOMA_INGRESSO_SERVICOPUBLICO  \n",
       "0                           17/02/2010  \n",
       "1                           10/05/1988  \n",
       "2                           19/05/2010  \n",
       "3                           31/12/2009  \n",
       "4                           30/04/2015  \n",
       "5                           06/05/2009  \n",
       "6                           06/05/2009  \n",
       "7                           30/09/2009  \n",
       "8                           23/06/2014  \n",
       "9                           01/01/1982  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging both our records and salaries entries\n",
    "\n",
    "merge_left = pd.merge(salaries, records, on=\"NOME\")\n",
    "\n",
    "merge_left[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O primeiro gráfico que vamos gerar é sobre a média da distribuição salárial em função da data de ingresso no serviço público. Isso permitirá a observação do comportamento salarial ao longo da carreira pública.\n",
    "\n",
    "A expectativa ideal é de que quanto mais tempo de serviço um funcionário possui, maior o salário dele (em termos de progressões dentro do serviço público).\n",
    "\n",
    "Para plotar o gráfico, agruparemos os dados de acordo com o Ano de ingresso no serviço público. Também removeremos todos os grupos que tiverem menos do que 100 pessoas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16279080>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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weT3mvNY4dz/J+u21mPL2mQ0B7c8o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\njDvnBUj/CTPiNMygo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+bg5MmTSE5OxjvvvIP3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\nnWCIcFE/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x368fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#grouping by the year this person joined the public service, removing groups with less than 100 members\n",
    "x = merge_left.groupby(merge_left[u'DATA_DIPLOMA_INGRESSO_SERVICOPUBLICO'].map(lambda x: x.split(r\"/\")[-1])).filter(lambda k: len(k) > 100)\n",
    "x = x.groupby(merge_left[u'DATA_DIPLOMA_INGRESSO_SERVICOPUBLICO'].map(lambda x: x.split(r\"/\")[-1]))\n",
    "\n",
    "#removing Data Não Informada\n",
    "#merge_left.drop( x.get_group('N\\xe3o informada').index )\n",
    "\n",
    "#ploting a bar graph containing the mean of all salaries from people who joined each yearh\n",
    "x[u'REMUNERAÇÃO BÁSICA BRUTA (R$)'].mean().plot(kind='bar', figsize=(40,20))\n",
    "#error deviation\n",
    "x[u'REMUNERAÇÃO BÁSICA BRUTA (R$)'].std().plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conforme pode ser observado na figura anterior, o comportamento do gráfico é diferente do esperado. É possível notar uma razoável estabilidade no salário mediano dos que ingressaram no serviço público federal nos anos 1960, 1970 e 1980. Porém a curva cresce com a chegada dos anos 1990 e atinge o pico no início dos anos 2000.\n",
    "\n",
    "O salário médio dos servidores que ingressam a partir de 2002 começa uma curva decrescente.\n",
    "\n",
    "Vamos analizar isso de forma mais profunda, gerando um histograma de cada um dos anos plotados no gráfico superior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1637cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf7920f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16174668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128d9e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x159215c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAIxtFX1DdAAAAADGNnEKAAAAgDQVf0P0a3Ho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\nAEAacQoAAACANP8PMhWBQqIut98AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19d88400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11663a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AUMz/BwOypi0p407uAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e36a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19da42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ea9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e53ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f5fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ed1f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fc3b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f80c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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avn17Dh06lBMnTuT3fu/30tfXlw0bNmTr1q3Z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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fdda90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fdf978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UI5wCAAAAoBjhFAAAAADFCKcAAAAAKEY4BQAA\nAEAxwikAAAAAihFOAQAAAFCMcAoAAACAYoRTAAAAABQjnAIAAACgGOEUAAAAAMU0lC4A4HxQratL\n/Y9/WLqMuaelNWOXLC1dBQAAcB4TTgFMx8nhjGx9oHQVc86CP30k9UMDpcuYc86859Jk0eLSZQAA\nwHlBOAXAO3fylYxs+/PSVcw59RsfEU4BAMA0WXMKAAAAgGKEUwAAAAAUI5wCAAAAoBjhFAAAAADF\nCKcAAAAAKMbd+gCAmqh/+XgyNFC6jDnlzHsudWdHAOCCJ5wCAGpjaCAjX7q3dBVzSv3GR4RTAMAF\nz7Q+AAAAAIqp2ZVTu3btyve+970sXrw4Dz/8cJLk1KlT2bp1awYGBtLW1paenp40NjYmSfbs2ZN9\n+/alvr4+69evz6pVq2pVKgAAAAA1UrMrp26++eZs3LjxrH179+7N1VdfnW3btqWjoyN79uxJkhw5\nciTPPvtstmzZkvvvvz9/8zd/k2q1WqtSAQAAAKiRmoVTV1xxRS6++OKz9h04cCA33nhjkuSmm27K\n/v37J/avWbMm9fX1aWtry7Jly9Lf31+rUgEAAACokaJrTg0PD6e5uTlJ0tzcnOHh4STJ0NBQli5d\nOvG4lpaWDA0NFakRAAAAgNkzpxZEr1QqpUsAAAAAoIZqtiD6VJqbm3PixImJfxcvfv1Wyi0tLTl+\n/PjE4wYHB9PS0jLlMfr6+tLX1zex3d3dnaamptkt/Dz0i/qiP+q5Sx46JUHxFLwkU9IrU6urq3gv\nmoL3osn0Cufioosu0i9Mi17hXOgXzsXu3bsnPu/o6EhHR8eMHLemvyVWq9WzFja/9tpr09vbm66u\nrvT29qazszNJ0tnZme3bt+e2227L0NBQjh49mvb29imPOdWLcfLkydk7ifNU/dho6RLmJuvsT8kN\nCKbgJZmSXpna+HjVe9EUvBdNplc4F01NTfqFadErnAv9wnQ1NTWlu7t7Vo5ds3Bq27ZtOXToUE6e\nPJnPfOYz6e7uTldXV7Zs2ZJ9+/altbU1PT09SZLly5fn+uuvT09PTxoaGrJhwwZ/nQcAAACYh2oW\nTt19991T7t+0adOU+9etW5d169bNZkkAAAAAFDanFkQHAAAA4MIinAIAAACgGOEUAAAAAMUIpwAA\nAAAoRjgFAAAAQDHCKQAAAACKEU4BAAAAUExD6QIAYL6p1tWl/sc/LF3GnFMZPVO6BAAA5iDhFADM\ntJPDGdn6QOkq5pwFd28uXQIAAHOQaX0AAAAAFCOcAgAAAKAY4RQAAAAAxQinAAAAAChGOAUAAABA\nMcIpAAAAAIoRTgEAAABQjHAKAAAAgGKEUwAAAAAUI5wCAAAAoBjhFAAAAADFNJQuYDbUv/CD0iXM\nKZV3vSvVarV0GQAAAACTzMtwauSrXyhdwpxSt3xF3vWJT5cuAwAAAGAS0/oAAAAAKEY4BQAAAEAx\nwikAAAAAihFOAQAAAFCMcAoAAACAYoRTAAAAABQjnAIAAACgGOEUAAAAAMUIpwAAAAAoRjgFAAAA\nQDENpQsAAIBfVv/y8WRooHQZc86Z91yaLFpcugwAmHHCKQAA5pahgYx86d7SVcw59RsfEU4BMC+Z\n1gcAAABAMcIpAAAAAIoRTgEAAABQjHAKAAAAgGKEUwAAAAAU4259AACFVOvqUv/jH5YuY86pjJ4p\nXQIAUEPCKQCAUk4OZ2TrA6WrmHMW3L25dAkAQA2Z1gcAAABAMcIpAAAAAIoRTgEAAABQjHAKAAAA\ngGKEUwAAAAAUI5wCAAAAoBjhFAAAAADFCKcAAAAAKEY4BQAAAEAxwikAAAAAihFOAQAAAFCMcAoA\nAACAYoRTAAAAABQjnAIAAACgGOEUAAAAAMUIpwAAAAAopqF0AQAAAO9U/cvHk6GB0mXMKWfec2my\naHHpMgCmTTgFAACcv4YGMvKle0tXMafUb3xEOAWcV0zrAwAAAKAYV04BAMB5oFpXl/of/7B0GXNO\nZfRM6RIA+BXN+XDq4MGDefzxx1OtVnPzzTenq6urdEkAAFB7J4czsvWB0lXMOQvu3ly6hDlHkDm1\nysVNqf7fk6XLmHOsUcZcMKfDqfHx8fzt3/5t/uzP/iyXXHJJ7r///qxevTqXXXZZ6dIAAADmJkHm\nlBbcvTkj2/68dBlzjjXKmAvmdDjV39+fZcuWpbW1NUnygQ98IPv37xdOAQAAALPGnUCncOmls3bo\nOR1ODQ0NZcmSJRPbLS0t6e/vL1gRAAAAzB+mgU6tMnomv3j4T0uXMbd88MCsHXpOh1Pv1Ls+/n9K\nlzCnVJpcogkAAMAUTAOdkvXsaqtSrVarpYt4Ky+88EK+853vZOPGjUmSvXv3JslZi6L39fWlr69v\nYru7u7u2RQIAAABcAHbv3j3xeUdHRzo6OmbkuHUzcpRZ0t7enqNHj2ZgYCCjo6N55pln0tnZedZj\nOjo60t3dPfHxyy8UvB39wnTpFc6FfmG69ArnQr8wXXqFc6FfmK7du3eflb/MVDCVzPFpfXV1dfn9\n3//9PPTQQ6lWq/nQhz6U5cuXly4LAAAAgBkyp8OpJHn/+9+fbdu2lS4DAAAAgFlQ/8ADDzxQuoiZ\n1tbWVroEziP6henSK5wL/cJ06RXOhX5huvQK50K/MF2z1StzekF0AAAAAOa3Ob0gOgAAAADzm3AK\nAAAAgGLm/ILo5+LgwYN5/PHHU61Wc/PNN6erq6t0SRTw2c9+No2NjalUKqmvr89f/uVf5tSpU9m6\ndWsGBgbS1taWnp6eNDY2Jkn27NmTffv2pb6+PuvXr8+qVauSJD/5yU/y9a9/PWfOnMk111yT9evX\nFzwrZsquXbvyve99L4sXL87DDz+cJDPaH6Ojo9mxY0d+8pOfpKmpKT09PVm6dGmRc+VXM1WvfOc7\n38m//uu/ZvHixUmSO+64I+9///uT6JUL2eDgYHbs2JHh4eFUKpWsXbs2t956q7GFKb25Xz784Q/n\nt3/7t40vTHLmzJls3rw5o6OjGRsby3XXXZePfexjxham9Fb9YmzhrYyPj+f+++9PS0tL7r333vJj\nS3WeGBsbq955553VY8eOVc+cOVP94z/+4+qRI0dKl0UBn/3sZ6snT548a98//MM/VPfu3VutVqvV\nPXv2VL/5zW9Wq9Vq9cUXX6x+/vOfr46OjlZfeuml6p133lkdHx+vVqvV6v3331/90Y9+VK1Wq9Uv\nfvGL1eeee66GZ8Fs+eEPf1j96U9/Wv2jP/qjiX0z2R//8i//Un3ssceq1Wq1+swzz1S3bNlSs3Nj\nZk3VK7t3764++eSTkx6rVy5sL7/8cvWnP/1ptVqtVl977bXq5z73ueqRI0eMLUzprfrF+MJUTp8+\nXa1WX/+/zhe+8IXqj370I2MLb2mqfjG28FaefPLJ6rZt26pf+tKXqtVq+f8TzZtpff39/Vm2bFla\nW1vT0NCQD3zgA9m/f3/psiigWq2m+qZ1/g8cOJAbb7wxSXLTTTdN9MaBAweyZs2a1NfXp62tLcuW\nLUt/f39OnDiR1157Le3t7UmSG264QT/NE1dccUUuvvjis/bNZH/s379/4ljXXXddnn/++VqdGjNs\nql5JMml8SfTKha65uTkrVqxIkixcuDCXXXZZBgcHjS1Maap+GRoaSmJ8YbIFCxYkef2qmLGxsSR+\nb+GtTdUvibGFyQYHB/Pcc89l7dq1E/tKjy3zZlrf0NBQlixZMrHd0tKS/v7+ghVRSqVSyUMPPZS6\nurp8+MMfztq1azM8PJzm5uYkr/9SODw8nOT1vlm5cuXEc1taWjI0NJT6+vqz+mnJkiUTvzgy/8xk\nf/zyWFRXV5eLL744p06dyqJFi2p1Osyyf/7nf85TTz2V9773vfmd3/mdNDY26hUmHDt2LD//+c+z\ncuVKYwtv641+ed/73pfDhw8bX5hkfHw89913X1566aXccsstaW9vN7bwlqbql+eee87YwiTf+MY3\n8slPfjKvvvrqxL7SY8u8CafgDQ8++GAuueSSvPLKK3nooYdy6aWXTnpMpVIpUBnni5nsj6n+UsX5\n65ZbbslHP/rRVCqVfOtb38rf//3f59Of/vSMHFuvnP9Onz6dr33ta1m/fn0WLlw46evGFn7Zm/vF\n+MJU6urq8pWvfCWvvvpqHn744bz44ouTHmNs4Q1v7pcjR44YW5jkjTVVV6xYkb6+vrd8XK3Hlnkz\nra+lpSXHjx+f2B4aGkpLS0vBiijlkksuSZK8+93vzurVq9Pf35/m5uacOHEiSXLixImJBQHf3DeD\ng4NpaWlJS0tLBgcHJ+1nfprJ/vjlr42Pj+e1117z16R55N3vfvfEG/XatWsnrtDVK4yNjeWRRx7J\nDTfckNWrVycxtvDWpuoX4wv/P42Njbnyyitz8OBBYwtv65f7xdjCmx0+fDgHDhzInXfemW3btuUH\nP/hBHn300eJjy7wJp9rb23P06NEMDAxkdHQ0zzzzTDo7O0uXRY394he/yOnTp5O8/hfJ73//+7n8\n8stz7bXXpre3N0nS29s70RudnZ357ne/m9HR0Rw7dixHjx5Ne3t7mpub09jYmP7+/lSr1Tz11FMT\nvzxy/nvzumQz2R+dnZ3593//9yTJs88+m6uuuqq2J8eMenOvvPGGnST/9V//lV//9V9Pold4/e6O\ny5cvz6233jqxz9jCW5mqX4wvvNkrr7wyMeVmZGQkzz//fC677DJjC1Oaql8uvfRSYwuTfPzjH8+u\nXbuyY8eO3HPPPbnqqqty1113FR9bKtV5dC3ewYMH83d/93epVqv50Ic+lK6urtIlUWPHjh3LV7/6\n1VQqlYyNjeWDH/xgurq6curUqWzZsiXHjx9Pa2trenp6JhY63rNnT/7t3/4tDQ0Nk26LuXPnzonb\nYv7u7/5uyVNjhmzbti2HDh3KyZMns3jx4nR3d2f16tUz1h9nzpzJo48+mp/97GdpamrK3Xffnba2\ntmLnyzs3Va/09fXlZz/7WSqVSlpbW/MHf/AHE3Pz9cqF6/Dhw9m8eXMuv/zyVCqVVCqV3HHHHWlv\nbze2MMlb9ct//Md/GF84y//8z/9k586dGR8fT7VazZo1a/KRj3xkRn+v1Svzx1v1y44dO4wtvKVD\nhw7lySefzL333lt8bJlX4RQAAAAA55d5M60PAAAAgPOPcAoAAACAYoRTAAAAABQjnAIAAACgGOEU\nAAAAAMUIpwAAAAAoRjgFAAAAQDHCKQAAAACK+X+7gxNOoihxHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a094d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAIDiiFYAAAAAFEe0AgAAAKA4ohUA\nAAAAxRGtAAAAACjO/wNIigCEX2sckgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a211c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a112860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a27ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3341d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a29f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CCy9UVqxYMW3nxtSaqFfWrl1bee65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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3a2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a359cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a411ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3bd8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a4bca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a429b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19754b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a4d92e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WMRlavR+/c/X/q3UJxVI/7loAAADG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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a637278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a59bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a69e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a64ab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a6f4e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a6c01d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a812f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7595f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a885c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a829ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9364a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a89bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9535c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9dbd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9becf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aad4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa79a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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