AnaliseReembolso.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98d9d5c2",
   "metadata": {},
   "source": [
    "# Exercícios"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c8e594",
   "metadata": {},
   "source": [
    "Sua equipe  está trabalhando na auditoria da cota parlamentar para o período de 2008 a 2019. O objetivo final da sua análise é apontar possíveis irregularidades nos reembolsos, identificar solicitações suspeitas, deputados que devem ser investigados e partidos com maior índice de irregularidades. Construa uma análise utilizando dados e justificativas plausíveis possa ser utilizada na auditoria.\n",
    "\n",
    "Escolha **três** categorias de cota parlamentar para realizar a análise. A narrativa com os dados e pensamento analítico sobre a categoria e os valores fazem parte da avaliação, que devem responder obrigatoriamente as seguintes perguntas **para cada categoria** analisada:\n",
    "\n",
    "* Quais solicitações de reembolso são suspeitas ?\n",
    "* Das solicitações suspeitas, escolha uma para analisar mais afundo e justifique o porque devemos indiciar o parlamentar ?\n",
    "* Quais parlamentares devem ser investigados (top 5)?\n",
    "* Eleja um parlamentar para ser premiado por boa conduta. Justifique sua escolha com dados. \n",
    "\n",
    "Escolha **um** parlamentar de sua preferência (alguém que você votou se lembrar). Analise os gastos específicos desse parlamentar e responda às seguintes perguntas:\n",
    "\n",
    "* O parlamentar gasta a verba completa ou tem um gasto moderado ?\n",
    "* Qual o perfil de gastos do parlamentar, quais categorias o parlamentar mais gasta a verba ?\n",
    "* Existe algum reembolso suspeito em relação às categorias analisadas ?\n",
    "* Analisando as empresas prestadoras de serviços (cnpj_cpf), existe algo suspeito ? \n",
    "\n",
    "**TENTE APLICAR A METODOLOGIA CRISP-DM**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3acea88",
   "metadata": {},
   "source": [
    "# Iniciando a Análise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80a306a",
   "metadata": {},
   "source": [
    "## Leitura do Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6a93483",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.spatial.distance import euclidean\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9833759a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cota_parlamentar = pd.read_csv('dadosBrutos\\CEAPS_2008_2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0035a850",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>SENADOR</th>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th>CNPJ_CPF</th>\n",
       "      <th>FORNECEDOR</th>\n",
       "      <th>DOCUMENTO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>DETALHAMENTO</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Contratação de consultorias, assessorias, pesq...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12351.52</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>386.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Contratação de consultorias, assessorias, pesq...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12351.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2610.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Contratação de consultorias, assessorias, pesq...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12351.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268894</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEZÉ PERRELLA</td>\n",
       "      <td>Passagens aéreas, aquáticas e terrestres nacio...</td>\n",
       "      <td>09.296.295/0001-60</td>\n",
       "      <td>Azul</td>\n",
       "      <td>O954UL</td>\n",
       "      <td>19/01/2019</td>\n",
       "      <td>Companhia Aérea: AZUL, Localizador: O954UL. Pa...</td>\n",
       "      <td>769.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268895</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEZÉ PERRELLA</td>\n",
       "      <td>Passagens aéreas, aquáticas e terrestres nacio...</td>\n",
       "      <td>09.296.295/0001-60</td>\n",
       "      <td>Azul</td>\n",
       "      <td>G9Z36V</td>\n",
       "      <td>24/01/2019</td>\n",
       "      <td>Companhia Aérea: AZUL, Localizador: G9Z36V. Pa...</td>\n",
       "      <td>310.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268896</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEZÉ PERRELLA</td>\n",
       "      <td>Passagens aéreas, aquáticas e terrestres nacio...</td>\n",
       "      <td>07.575.651/0001-59</td>\n",
       "      <td>GOL</td>\n",
       "      <td>WNQI4A</td>\n",
       "      <td>29/01/2019</td>\n",
       "      <td>Companhia Aérea: GOL, Localizador: WNQI4A. Pas...</td>\n",
       "      <td>1168.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268897</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEZÉ PERRELLA</td>\n",
       "      <td>Passagens aéreas, aquáticas e terrestres nacio...</td>\n",
       "      <td>33.937.681/0001-78</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>NCCPIM</td>\n",
       "      <td>23/01/2019</td>\n",
       "      <td>Companhia Aérea: LATAM, Localizador: NCCPIM. P...</td>\n",
       "      <td>772.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268898</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEZÉ PERRELLA</td>\n",
       "      <td>Passagens aéreas, aquáticas e terrestres nacio...</td>\n",
       "      <td>33.937.681/0001-78</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>MEYLWB</td>\n",
       "      <td>26/01/2019</td>\n",
       "      <td>Companhia Aérea: LATAM, Localizador: MEYLWB. P...</td>\n",
       "      <td>1755.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>268899 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ANO  MES        SENADOR  \\\n",
       "0       2008    9      ADA MELLO   \n",
       "1       2008    9      ADA MELLO   \n",
       "2       2008   10      ADA MELLO   \n",
       "3       2008   10      ADA MELLO   \n",
       "4       2008   11      ADA MELLO   \n",
       "...      ...  ...            ...   \n",
       "268894  2019    1  ZEZÉ PERRELLA   \n",
       "268895  2019    1  ZEZÉ PERRELLA   \n",
       "268896  2019    1  ZEZÉ PERRELLA   \n",
       "268897  2019    1  ZEZÉ PERRELLA   \n",
       "268898  2019    1  ZEZÉ PERRELLA   \n",
       "\n",
       "                                             TIPO_DESPESA            CNPJ_CPF  \\\n",
       "0       Contratação de consultorias, assessorias, pesq...                 NaN   \n",
       "1       Locomoção, hospedagem, alimentação, combustíve...                 NaN   \n",
       "2       Contratação de consultorias, assessorias, pesq...                 NaN   \n",
       "3       Locomoção, hospedagem, alimentação, combustíve...                 NaN   \n",
       "4       Contratação de consultorias, assessorias, pesq...                 NaN   \n",
       "...                                                   ...                 ...   \n",
       "268894  Passagens aéreas, aquáticas e terrestres nacio...  09.296.295/0001-60   \n",
       "268895  Passagens aéreas, aquáticas e terrestres nacio...  09.296.295/0001-60   \n",
       "268896  Passagens aéreas, aquáticas e terrestres nacio...  07.575.651/0001-59   \n",
       "268897  Passagens aéreas, aquáticas e terrestres nacio...  33.937.681/0001-78   \n",
       "268898  Passagens aéreas, aquáticas e terrestres nacio...  33.937.681/0001-78   \n",
       "\n",
       "       FORNECEDOR DOCUMENTO        DATA  \\\n",
       "0             NaN       NaN         NaN   \n",
       "1             NaN       NaN         NaN   \n",
       "2             NaN       NaN         NaN   \n",
       "3             NaN       NaN         NaN   \n",
       "4             NaN       NaN         NaN   \n",
       "...           ...       ...         ...   \n",
       "268894       Azul    O954UL  19/01/2019   \n",
       "268895       Azul    G9Z36V  24/01/2019   \n",
       "268896        GOL    WNQI4A  29/01/2019   \n",
       "268897      LATAM    NCCPIM  23/01/2019   \n",
       "268898      LATAM    MEYLWB  26/01/2019   \n",
       "\n",
       "                                             DETALHAMENTO  VALOR_REEMBOLSADO  \n",
       "0                                                     NaN           12351.52  \n",
       "1                                                     NaN             386.60  \n",
       "2                                                     NaN           12351.52  \n",
       "3                                                     NaN            2610.68  \n",
       "4                                                     NaN           12351.52  \n",
       "...                                                   ...                ...  \n",
       "268894  Companhia Aérea: AZUL, Localizador: O954UL. Pa...             769.29  \n",
       "268895  Companhia Aérea: AZUL, Localizador: G9Z36V. Pa...             310.62  \n",
       "268896  Companhia Aérea: GOL, Localizador: WNQI4A. Pas...            1168.10  \n",
       "268897  Companhia Aérea: LATAM, Localizador: NCCPIM. P...             772.05  \n",
       "268898  Companhia Aérea: LATAM, Localizador: MEYLWB. P...            1755.17  \n",
       "\n",
       "[268899 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cota_parlamentar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e30d6157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 268899 entries, 0 to 268898\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   ANO                268899 non-null  int64  \n",
      " 1   MES                268899 non-null  int64  \n",
      " 2   SENADOR            268899 non-null  object \n",
      " 3   TIPO_DESPESA       268899 non-null  object \n",
      " 4   CNPJ_CPF           265755 non-null  object \n",
      " 5   FORNECEDOR         265755 non-null  object \n",
      " 6   DOCUMENTO          246750 non-null  object \n",
      " 7   DATA               265755 non-null  object \n",
      " 8   DETALHAMENTO       154368 non-null  object \n",
      " 9   VALOR_REEMBOLSADO  268897 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(7)\n",
      "memory usage: 20.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cota_parlamentar.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f80cf36",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>MES</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>268899.000000</td>\n",
       "      <td>268899.000000</td>\n",
       "      <td>268897.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.099413</td>\n",
       "      <td>6.651040</td>\n",
       "      <td>930.433800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.022301</td>\n",
       "      <td>3.282404</td>\n",
       "      <td>2453.968535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-243.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>99.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>251.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>270000.000000</td>\n",
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      ],
      "text/plain": [
       "                 ANO            MES  VALOR_REEMBOLSADO\n",
       "count  268899.000000  268899.000000      268897.000000\n",
       "mean     2014.099413       6.651040         930.433800\n",
       "std         3.022301       3.282404        2453.968535\n",
       "min      2008.000000       1.000000        -243.400000\n",
       "25%      2012.000000       4.000000          99.700000\n",
       "50%      2014.000000       7.000000         251.970000\n",
       "75%      2017.000000       9.000000         900.000000\n",
       "max      2019.000000      12.000000      270000.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_cota_parlamentar.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6811d40a",
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    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"TIPO_DESPESA\", data=df_cota_parlamentar).set_title(\"Quantidade de Reembolsos por Categoria\",  fontdict = { \"fontsize\": 16})\n",
    "plt.xlabel(\"Tipo de despesas\")\n",
    "plt.ylabel(\"Quantidade de reembolsos\")\n",
    "plt.xticks([0, 1, 2, 3, 4, 5, 6], [\"Consultorias\",\n",
    "                                  \"Locomoção\",\n",
    "                                  \"Escritório\",\n",
    "                                  \"Divulgação\",\n",
    "                                  \"Imóveis\",\n",
    "                                  \"Passagens\",\n",
    "                                  \"Segurança\"],\n",
    "          rotation=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75de678f",
   "metadata": {},
   "source": [
    "## Agregamento por tipo de despesa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a4f4770",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tipo_despesa_info = df_cota_parlamentar.groupby(\"TIPO_DESPESA\")[\"VALOR_REEMBOLSADO\"].agg([\"count\", np.std, np.min, np.max, np.sum, np.mean, np.median])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c94f808c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>std</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aluguel de imóveis para escritório político, compreendendo despesas concernentes a eles.</th>\n",
       "      <td>51070</td>\n",
       "      <td>1588.392262</td>\n",
       "      <td>0.01</td>\n",
       "      <td>18793.95</td>\n",
       "      <td>47922809.61</td>\n",
       "      <td>938.374968</td>\n",
       "      <td>335.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aquisição de material de consumo para uso no escritório político, inclusive aquisição ou locação de software, despesas postais, aquisição de publicações, locação de móveis e de equipamentos.</th>\n",
       "      <td>22073</td>\n",
       "      <td>1017.514499</td>\n",
       "      <td>0.01</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>9806573.89</td>\n",
       "      <td>444.279160</td>\n",
       "      <td>121.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contratação de consultorias, assessorias, pesquisas, trabalhos técnicos e outros serviços de apoio ao exercício do mandato parlamentar</th>\n",
       "      <td>9827</td>\n",
       "      <td>6523.676345</td>\n",
       "      <td>-243.40</td>\n",
       "      <td>150000.00</td>\n",
       "      <td>37193016.49</td>\n",
       "      <td>3784.778314</td>\n",
       "      <td>1500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Divulgação da atividade parlamentar</th>\n",
       "      <td>10048</td>\n",
       "      <td>7249.372000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>31828000.34</td>\n",
       "      <td>3167.595575</td>\n",
       "      <td>1500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Locomoção, hospedagem, alimentação, combustíveis e lubrificantes</th>\n",
       "      <td>115533</td>\n",
       "      <td>1611.214386</td>\n",
       "      <td>0.01</td>\n",
       "      <td>66730.00</td>\n",
       "      <td>64056430.15</td>\n",
       "      <td>554.442715</td>\n",
       "      <td>114.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passagens aéreas, aquáticas e terrestres nacionais</th>\n",
       "      <td>59495</td>\n",
       "      <td>715.221197</td>\n",
       "      <td>-9.00</td>\n",
       "      <td>84000.00</td>\n",
       "      <td>56388198.82</td>\n",
       "      <td>947.780466</td>\n",
       "      <td>800.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serviços de Segurança Privada</th>\n",
       "      <td>851</td>\n",
       "      <td>6269.709108</td>\n",
       "      <td>24.00</td>\n",
       "      <td>28922.72</td>\n",
       "      <td>2995828.11</td>\n",
       "      <td>3520.362056</td>\n",
       "      <td>520.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     count          std  \\\n",
       "TIPO_DESPESA                                                              \n",
       "Aluguel de imóveis para escritório político, co...   51070  1588.392262   \n",
       "Aquisição de material de consumo para uso no es...   22073  1017.514499   \n",
       "Contratação de consultorias, assessorias, pesqu...    9827  6523.676345   \n",
       "Divulgação da atividade parlamentar                  10048  7249.372000   \n",
       "Locomoção, hospedagem, alimentação, combustívei...  115533  1611.214386   \n",
       "Passagens aéreas, aquáticas e terrestres nacionais   59495   715.221197   \n",
       "Serviços de Segurança Privada                          851  6269.709108   \n",
       "\n",
       "                                                      amin       amax  \\\n",
       "TIPO_DESPESA                                                            \n",
       "Aluguel de imóveis para escritório político, co...    0.01   18793.95   \n",
       "Aquisição de material de consumo para uso no es...    0.01   40000.00   \n",
       "Contratação de consultorias, assessorias, pesqu... -243.40  150000.00   \n",
       "Divulgação da atividade parlamentar                   0.60  270000.00   \n",
       "Locomoção, hospedagem, alimentação, combustívei...    0.01   66730.00   \n",
       "Passagens aéreas, aquáticas e terrestres nacionais   -9.00   84000.00   \n",
       "Serviços de Segurança Privada                        24.00   28922.72   \n",
       "\n",
       "                                                            sum         mean  \\\n",
       "TIPO_DESPESA                                                                   \n",
       "Aluguel de imóveis para escritório político, co...  47922809.61   938.374968   \n",
       "Aquisição de material de consumo para uso no es...   9806573.89   444.279160   \n",
       "Contratação de consultorias, assessorias, pesqu...  37193016.49  3784.778314   \n",
       "Divulgação da atividade parlamentar                 31828000.34  3167.595575   \n",
       "Locomoção, hospedagem, alimentação, combustívei...  64056430.15   554.442715   \n",
       "Passagens aéreas, aquáticas e terrestres nacionais  56388198.82   947.780466   \n",
       "Serviços de Segurança Privada                        2995828.11  3520.362056   \n",
       "\n",
       "                                                      median  \n",
       "TIPO_DESPESA                                                  \n",
       "Aluguel de imóveis para escritório político, co...   335.675  \n",
       "Aquisição de material de consumo para uso no es...   121.850  \n",
       "Contratação de consultorias, assessorias, pesqu...  1500.000  \n",
       "Divulgação da atividade parlamentar                 1500.000  \n",
       "Locomoção, hospedagem, alimentação, combustívei...   114.600  \n",
       "Passagens aéreas, aquáticas e terrestres nacionais   800.390  \n",
       "Serviços de Segurança Privada                        520.000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tipo_despesa_info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ba824c7",
   "metadata": {},
   "source": [
    "Visualizando do maior para o menor número de ocorrências"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8fd8eaef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>std</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Locomoção, hospedagem, alimentação, combustíveis e lubrificantes</th>\n",
       "      <td>115533</td>\n",
       "      <td>1611.214386</td>\n",
       "      <td>0.01</td>\n",
       "      <td>66730.00</td>\n",
       "      <td>64056430.15</td>\n",
       "      <td>554.442715</td>\n",
       "      <td>114.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passagens aéreas, aquáticas e terrestres nacionais</th>\n",
       "      <td>59495</td>\n",
       "      <td>715.221197</td>\n",
       "      <td>-9.00</td>\n",
       "      <td>84000.00</td>\n",
       "      <td>56388198.82</td>\n",
       "      <td>947.780466</td>\n",
       "      <td>800.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aluguel de imóveis para escritório político, compreendendo despesas concernentes a eles.</th>\n",
       "      <td>51070</td>\n",
       "      <td>1588.392262</td>\n",
       "      <td>0.01</td>\n",
       "      <td>18793.95</td>\n",
       "      <td>47922809.61</td>\n",
       "      <td>938.374968</td>\n",
       "      <td>335.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aquisição de material de consumo para uso no escritório político, inclusive aquisição ou locação de software, despesas postais, aquisição de publicações, locação de móveis e de equipamentos.</th>\n",
       "      <td>22073</td>\n",
       "      <td>1017.514499</td>\n",
       "      <td>0.01</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>9806573.89</td>\n",
       "      <td>444.279160</td>\n",
       "      <td>121.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Divulgação da atividade parlamentar</th>\n",
       "      <td>10048</td>\n",
       "      <td>7249.372000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>31828000.34</td>\n",
       "      <td>3167.595575</td>\n",
       "      <td>1500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contratação de consultorias, assessorias, pesquisas, trabalhos técnicos e outros serviços de apoio ao exercício do mandato parlamentar</th>\n",
       "      <td>9827</td>\n",
       "      <td>6523.676345</td>\n",
       "      <td>-243.40</td>\n",
       "      <td>150000.00</td>\n",
       "      <td>37193016.49</td>\n",
       "      <td>3784.778314</td>\n",
       "      <td>1500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serviços de Segurança Privada</th>\n",
       "      <td>851</td>\n",
       "      <td>6269.709108</td>\n",
       "      <td>24.00</td>\n",
       "      <td>28922.72</td>\n",
       "      <td>2995828.11</td>\n",
       "      <td>3520.362056</td>\n",
       "      <td>520.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     count          std  \\\n",
       "TIPO_DESPESA                                                              \n",
       "Locomoção, hospedagem, alimentação, combustívei...  115533  1611.214386   \n",
       "Passagens aéreas, aquáticas e terrestres nacionais   59495   715.221197   \n",
       "Aluguel de imóveis para escritório político, co...   51070  1588.392262   \n",
       "Aquisição de material de consumo para uso no es...   22073  1017.514499   \n",
       "Divulgação da atividade parlamentar                  10048  7249.372000   \n",
       "Contratação de consultorias, assessorias, pesqu...    9827  6523.676345   \n",
       "Serviços de Segurança Privada                          851  6269.709108   \n",
       "\n",
       "                                                      amin       amax  \\\n",
       "TIPO_DESPESA                                                            \n",
       "Locomoção, hospedagem, alimentação, combustívei...    0.01   66730.00   \n",
       "Passagens aéreas, aquáticas e terrestres nacionais   -9.00   84000.00   \n",
       "Aluguel de imóveis para escritório político, co...    0.01   18793.95   \n",
       "Aquisição de material de consumo para uso no es...    0.01   40000.00   \n",
       "Divulgação da atividade parlamentar                   0.60  270000.00   \n",
       "Contratação de consultorias, assessorias, pesqu... -243.40  150000.00   \n",
       "Serviços de Segurança Privada                        24.00   28922.72   \n",
       "\n",
       "                                                            sum         mean  \\\n",
       "TIPO_DESPESA                                                                   \n",
       "Locomoção, hospedagem, alimentação, combustívei...  64056430.15   554.442715   \n",
       "Passagens aéreas, aquáticas e terrestres nacionais  56388198.82   947.780466   \n",
       "Aluguel de imóveis para escritório político, co...  47922809.61   938.374968   \n",
       "Aquisição de material de consumo para uso no es...   9806573.89   444.279160   \n",
       "Divulgação da atividade parlamentar                 31828000.34  3167.595575   \n",
       "Contratação de consultorias, assessorias, pesqu...  37193016.49  3784.778314   \n",
       "Serviços de Segurança Privada                        2995828.11  3520.362056   \n",
       "\n",
       "                                                      median  \n",
       "TIPO_DESPESA                                                  \n",
       "Locomoção, hospedagem, alimentação, combustívei...   114.600  \n",
       "Passagens aéreas, aquáticas e terrestres nacionais   800.390  \n",
       "Aluguel de imóveis para escritório político, co...   335.675  \n",
       "Aquisição de material de consumo para uso no es...   121.850  \n",
       "Divulgação da atividade parlamentar                 1500.000  \n",
       "Contratação de consultorias, assessorias, pesqu...  1500.000  \n",
       "Serviços de Segurança Privada                        520.000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tipo_despesa_info.sort_values(by=\"count\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e985d9",
   "metadata": {},
   "source": [
    "## Escolha das categorias da Análise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f62956a3",
   "metadata": {},
   "source": [
    "1°) Locomoção, hospedagem, alimentação, combustíveis e lubrificantes.\n",
    "\n",
    "    Por ter o maior número de ocorrências.\n",
    "    \n",
    "2°) Serviços de Segurança Privada.\n",
    "\n",
    "    Por ter o menor número de ocorrências.\n",
    "    \n",
    "3°) Divulgação da atividade parlamentar.\n",
    "\n",
    "    Por ter o maior desvio padrão."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb0f8fd",
   "metadata": {},
   "source": [
    "# Primeira Análise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "323ddabf",
   "metadata": {},
   "source": [
    "## Locomoção, hospedagem, alimentação, combustíveis e lubrificantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b450b783",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>SENADOR</th>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th>CNPJ_CPF</th>\n",
       "      <th>FORNECEDOR</th>\n",
       "      <th>DOCUMENTO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>DETALHAMENTO</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
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       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>386.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2610.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2615.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>ADA MELLO</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4943.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>ADELMIR SANTANA</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1151.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ANO  MES          SENADOR  \\\n",
       "1  2008    9        ADA MELLO   \n",
       "3  2008   10        ADA MELLO   \n",
       "5  2008   11        ADA MELLO   \n",
       "7  2008   12        ADA MELLO   \n",
       "9  2008    2  ADELMIR SANTANA   \n",
       "\n",
       "                                        TIPO_DESPESA CNPJ_CPF FORNECEDOR  \\\n",
       "1  Locomoção, hospedagem, alimentação, combustíve...      NaN        NaN   \n",
       "3  Locomoção, hospedagem, alimentação, combustíve...      NaN        NaN   \n",
       "5  Locomoção, hospedagem, alimentação, combustíve...      NaN        NaN   \n",
       "7  Locomoção, hospedagem, alimentação, combustíve...      NaN        NaN   \n",
       "9  Locomoção, hospedagem, alimentação, combustíve...      NaN        NaN   \n",
       "\n",
       "  DOCUMENTO DATA DETALHAMENTO  VALOR_REEMBOLSADO  \n",
       "1       NaN  NaN          NaN             386.60  \n",
       "3       NaN  NaN          NaN            2610.68  \n",
       "5       NaN  NaN          NaN            2615.63  \n",
       "7       NaN  NaN          NaN            4943.47  \n",
       "9       NaN  NaN          NaN            1151.13  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preparação do data frame\n",
    "df_loc = df_cota_parlamentar[df_cota_parlamentar[\"TIPO_DESPESA\"] == \"Locomoção, hospedagem, alimentação, combustíveis e lubrificantes\"]\n",
    "\n",
    "df_loc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5a7ff933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1170x827 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Criação do histograma do valor reembolsado\n",
    "ax = sns.histplot(df_loc[\"VALOR_REEMBOLSADO\"], kde=True)\n",
    "\n",
    "# Configuração do título e eixos do gráfico\n",
    "ax.set(xlabel=\"Valor Reembolsado\", ylabel=\"Quantidade de Reembolsos\", title=\"Distribuição de reembolsos por ticket em locomoção\")\n",
    "\n",
    "# Configuração do eixo y para limitar a altura do gráfico\n",
    "ax.set_ylim([0, 4000])\n",
    "ax.set_xlim([0, 3000])\n",
    "\n",
    "# Exibição do gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "355bf6c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    115533.000000\n",
       "mean        554.442715\n",
       "std        1611.214386\n",
       "min           0.010000\n",
       "25%          50.000000\n",
       "50%         114.600000\n",
       "75%         209.030000\n",
       "max       66730.000000\n",
       "Name: VALOR_REEMBOLSADO, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc[\"VALOR_REEMBOLSADO\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eabaf4ce",
   "metadata": {},
   "source": [
    "Agora procurando o parlamentar que teve o maior reembolso nesse tipo de despesa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e06d4463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANO                                                               2019\n",
       "MES                                                                  3\n",
       "SENADOR                                                DAVI ALCOLUMBRE\n",
       "TIPO_DESPESA         Locomoção, hospedagem, alimentação, combustíve...\n",
       "CNPJ_CPF                                            06.180.439/0001-20\n",
       "FORNECEDOR                               AEROTOP TÁXI AÉREO LTDA - EPP\n",
       "DOCUMENTO                                                          766\n",
       "DATA                                                        12/03/2019\n",
       "DETALHAMENTO                                                       NaN\n",
       "VALOR_REEMBOLSADO                                              66730.0\n",
       "Name: 251545, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc.loc[df_loc[\"VALOR_REEMBOLSADO\"].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c4e64b",
   "metadata": {},
   "source": [
    "Agora buscando os maiores 100 reembolsos nesse subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "805f7302",
   "metadata": {
    "scrolled": false
   },
   "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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>SENADOR</th>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th>CNPJ_CPF</th>\n",
       "      <th>FORNECEDOR</th>\n",
       "      <th>DOCUMENTO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>DETALHAMENTO</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251545</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>DAVI ALCOLUMBRE</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>06.180.439/0001-20</td>\n",
       "      <td>AEROTOP TÁXI AÉREO LTDA - EPP</td>\n",
       "      <td>766</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66730.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16669</th>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>ROMEU TUMA</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>02.296.299/0001-07</td>\n",
       "      <td>REALI TAXI AÉREO LTDA</td>\n",
       "      <td>2792</td>\n",
       "      <td>23/12/2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>ROMEU TUMA</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41239.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>VALDIR RAUPP</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13022</th>\n",
       "      <td>2009</td>\n",
       "      <td>11</td>\n",
       "      <td>MAURO FECURY</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>01.097.044/0001-44</td>\n",
       "      <td>OPERADORA SÃO PAULO RENAISSANCE LTDA</td>\n",
       "      <td>00176486</td>\n",
       "      <td>09/11/2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37346.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>ROMEU TUMA</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33390.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224870</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>ÂNGELA PORTELA</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>07.442.073/0001-82</td>\n",
       "      <td>REAL TUR TRANSPORTE E SERVIÇOS LTDO -ME</td>\n",
       "      <td>91</td>\n",
       "      <td>01/11/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>ROMEU TUMA</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32262.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>EXPEDITO JUNIOR</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31453.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60769</th>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>VICENTINHO ALVES</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>26.633.834/0001-28</td>\n",
       "      <td>AEROPOSTO DE PALMAS LTDA</td>\n",
       "      <td>0001297</td>\n",
       "      <td>20/07/2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ANO  MES           SENADOR  \\\n",
       "251545  2019    3   DAVI ALCOLUMBRE   \n",
       "16669   2009   12        ROMEU TUMA   \n",
       "2090    2008   11        ROMEU TUMA   \n",
       "2343    2008    8      VALDIR RAUPP   \n",
       "13022   2009   11      MAURO FECURY   \n",
       "2086    2008    7        ROMEU TUMA   \n",
       "224870  2018   10    ÂNGELA PORTELA   \n",
       "2082    2008    3        ROMEU TUMA   \n",
       "551     2008    7   EXPEDITO JUNIOR   \n",
       "60769   2011    7  VICENTINHO ALVES   \n",
       "\n",
       "                                             TIPO_DESPESA            CNPJ_CPF  \\\n",
       "251545  Locomoção, hospedagem, alimentação, combustíve...  06.180.439/0001-20   \n",
       "16669   Locomoção, hospedagem, alimentação, combustíve...  02.296.299/0001-07   \n",
       "2090    Locomoção, hospedagem, alimentação, combustíve...                 NaN   \n",
       "2343    Locomoção, hospedagem, alimentação, combustíve...                 NaN   \n",
       "13022   Locomoção, hospedagem, alimentação, combustíve...  01.097.044/0001-44   \n",
       "2086    Locomoção, hospedagem, alimentação, combustíve...                 NaN   \n",
       "224870  Locomoção, hospedagem, alimentação, combustíve...  07.442.073/0001-82   \n",
       "2082    Locomoção, hospedagem, alimentação, combustíve...                 NaN   \n",
       "551     Locomoção, hospedagem, alimentação, combustíve...                 NaN   \n",
       "60769   Locomoção, hospedagem, alimentação, combustíve...  26.633.834/0001-28   \n",
       "\n",
       "                                     FORNECEDOR DOCUMENTO        DATA  \\\n",
       "251545            AEROTOP TÁXI AÉREO LTDA - EPP       766  12/03/2019   \n",
       "16669                     REALI TAXI AÉREO LTDA      2792  23/12/2009   \n",
       "2090                                        NaN       NaN         NaN   \n",
       "2343                                        NaN       NaN         NaN   \n",
       "13022      OPERADORA SÃO PAULO RENAISSANCE LTDA  00176486  09/11/2009   \n",
       "2086                                        NaN       NaN         NaN   \n",
       "224870  REAL TUR TRANSPORTE E SERVIÇOS LTDO -ME        91  01/11/2018   \n",
       "2082                                        NaN       NaN         NaN   \n",
       "551                                         NaN       NaN         NaN   \n",
       "60769                  AEROPOSTO DE PALMAS LTDA   0001297  20/07/2011   \n",
       "\n",
       "       DETALHAMENTO  VALOR_REEMBOLSADO  \n",
       "251545          NaN           66730.00  \n",
       "16669           NaN           42400.00  \n",
       "2090            NaN           41239.32  \n",
       "2343            NaN           38250.00  \n",
       "13022           NaN           37346.22  \n",
       "2086            NaN           33390.00  \n",
       "224870          NaN           33300.00  \n",
       "2082            NaN           32262.69  \n",
       "551             NaN           31453.30  \n",
       "60769           NaN           30000.00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc_outliers = df_loc.nlargest(100, \"VALOR_REEMBOLSADO\")\n",
    "df_loc_outliers.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab846d9f",
   "metadata": {},
   "source": [
    "### Solicitação para investigar mais a fundo\n",
    "\n",
    "Romeu Tuma, a terceira maior solicitação de reembolso, não informa o nome do fornecedor, detalhamento e data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff395486",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANO                                                               2008\n",
       "MES                                                                 11\n",
       "SENADOR                                                     ROMEU TUMA\n",
       "TIPO_DESPESA         Locomoção, hospedagem, alimentação, combustíve...\n",
       "CNPJ_CPF                                                           NaN\n",
       "FORNECEDOR                                                         NaN\n",
       "DOCUMENTO                                                          NaN\n",
       "DATA                                                               NaN\n",
       "DETALHAMENTO                                                       NaN\n",
       "VALOR_REEMBOLSADO                                             41239.32\n",
       "Name: 2090, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc.loc[2090]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "865c98f6",
   "metadata": {},
   "source": [
    "### Top 5 parlamentares para serem investigados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11927df6",
   "metadata": {},
   "source": [
    "Somando os valors de reembolso de cada senador na categoria: Locomoção, hospedagem, alimentação, combustíveis e lubrificantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0719479a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CIRO NOGUEIRA</th>\n",
       "      <td>2423362.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEXA RIBEIRO</th>\n",
       "      <td>1487410.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICENTINHO ALVES</th>\n",
       "      <td>1449320.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HUMBERTO COSTA</th>\n",
       "      <td>1365448.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO DURVAL</th>\n",
       "      <td>1325686.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  VALOR_REEMBOLSADO\n",
       "SENADOR                            \n",
       "CIRO NOGUEIRA            2423362.67\n",
       "FLEXA RIBEIRO            1487410.74\n",
       "VICENTINHO ALVES         1449320.62\n",
       "HUMBERTO COSTA           1365448.64\n",
       "JOÃO DURVAL              1325686.69"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc_investigacao = df_loc.groupby([\"SENADOR\"])[\"VALOR_REEMBOLSADO\"].sum().sort_values(ascending=False)\n",
    "df_loc_investigacao = pd.DataFrame(df_loc_investigacao, columns=[\"VALOR_REEMBOLSADO\"])\n",
    "df_loc_investigacao.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008d82be",
   "metadata": {},
   "source": [
    "Identificando o total de informações faltantes informadas nos reembolsos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b6bd38d",
   "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>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JOÃO CAPIBERIBE</th>\n",
       "      <td>3276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO BAUER</th>\n",
       "      <td>2762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MÁRIO COUTO</th>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARCELO CRIVELLA</th>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLÁVIO ARNS</th>\n",
       "      <td>2164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  TOTAL_NULO\n",
       "SENADOR                     \n",
       "JOÃO CAPIBERIBE         3276\n",
       "PAULO BAUER             2762\n",
       "MÁRIO COUTO             2401\n",
       "MARCELO CRIVELLA        2200\n",
       "FLÁVIO ARNS             2164"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc_null = df_loc.loc[:, [\"CNPJ_CPF\", \"FORNECEDOR\", \"DOCUMENTO\", \"DATA\", \n",
    "                             \"DETALHAMENTO\"]].isnull().groupby(df_loc[\"SENADOR\"]).sum()\n",
    "df_loc_null[\"TOTAL_NULO\"] = df_loc_null.sum(axis=1).sort_values(ascending=False)\n",
    "df_loc_null.drop([\"CNPJ_CPF\", \"FORNECEDOR\", \"DOCUMENTO\", \"DATA\",\n",
    "                 \"DETALHAMENTO\"], axis=1, inplace=True)\n",
    "df_loc_null.sort_values(\"TOTAL_NULO\", ascending=False, inplace=True)\n",
    "df_loc_null.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66d078e",
   "metadata": {},
   "source": [
    "Unificando o total do valor reembolsado e o total de ocorrências nulas em um único DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b59e9542",
   "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>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.540000e+02</td>\n",
       "      <td>254.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.521907e+05</td>\n",
       "      <td>300.448819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.344850e+05</td>\n",
       "      <td>477.743448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.264845e+04</td>\n",
       "      <td>27.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.040381e+05</td>\n",
       "      <td>129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.435855e+05</td>\n",
       "      <td>352.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.423363e+06</td>\n",
       "      <td>3276.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VALOR_REEMBOLSADO   TOTAL_NULO\n",
       "count       2.540000e+02   254.000000\n",
       "mean        2.521907e+05   300.448819\n",
       "std         3.344850e+05   477.743448\n",
       "min         1.000000e+02     0.000000\n",
       "25%         3.264845e+04    27.500000\n",
       "50%         1.040381e+05   129.000000\n",
       "75%         3.435855e+05   352.500000\n",
       "max         2.423363e+06  3276.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc_investigacao_final = pd.merge(df_loc_investigacao, df_loc_null, on=\"SENADOR\").sort_values([\"VALOR_REEMBOLSADO\", \"TOTAL_NULO\"], ascending=[False,False])\n",
    "df_loc_investigacao_final.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ddf720e7",
   "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>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CIRO NOGUEIRA</th>\n",
       "      <td>2423362.67</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEXA RIBEIRO</th>\n",
       "      <td>1487410.74</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICENTINHO ALVES</th>\n",
       "      <td>1449320.62</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HUMBERTO COSTA</th>\n",
       "      <td>1365448.64</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO DURVAL</th>\n",
       "      <td>1325686.69</td>\n",
       "      <td>1535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVO CASSOL</th>\n",
       "      <td>1174359.06</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>1145895.36</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACIR GURGACZ</th>\n",
       "      <td>1117874.96</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ AGRIPINO</th>\n",
       "      <td>1116724.34</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO AMORIM</th>\n",
       "      <td>944706.71</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CÍCERO LUCENA</th>\n",
       "      <td>928512.63</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO PAIM</th>\n",
       "      <td>926441.36</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTÔNIO CARLOS VALADARES</th>\n",
       "      <td>915219.04</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPITÁCIO CAFETEIRA</th>\n",
       "      <td>906923.51</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO CAPIBERIBE</th>\n",
       "      <td>901231.95</td>\n",
       "      <td>3276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO ALBERTO SOUZA</th>\n",
       "      <td>895337.78</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO BAUER</th>\n",
       "      <td>885371.30</td>\n",
       "      <td>2762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>851877.10</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JORGE VIANA</th>\n",
       "      <td>847856.79</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LÚCIA VÂNIA</th>\n",
       "      <td>844748.12</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ PIMENTEL</th>\n",
       "      <td>795697.52</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEZÉ PERRELLA</th>\n",
       "      <td>781158.08</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>766003.58</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>755143.29</td>\n",
       "      <td>1236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLADSON CAMELI</th>\n",
       "      <td>745837.07</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOZARILDO CAVALCANTI</th>\n",
       "      <td>722666.83</td>\n",
       "      <td>1765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAYME CAMPOS</th>\n",
       "      <td>720786.63</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLINGTON DIAS</th>\n",
       "      <td>714749.95</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MÁRIO COUTO</th>\n",
       "      <td>679127.02</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INÁCIO ARRUDA</th>\n",
       "      <td>678180.10</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          VALOR_REEMBOLSADO  TOTAL_NULO\n",
       "SENADOR                                                \n",
       "CIRO NOGUEIRA                    2423362.67         533\n",
       "FLEXA RIBEIRO                    1487410.74         339\n",
       "VICENTINHO ALVES                 1449320.62         368\n",
       "HUMBERTO COSTA                   1365448.64        1744\n",
       "JOÃO DURVAL                      1325686.69        1535\n",
       "IVO CASSOL                       1174359.06         757\n",
       "VALDIR RAUPP                     1145895.36         128\n",
       "ACIR GURGACZ                     1117874.96         208\n",
       "JOSÉ AGRIPINO                    1116724.34         257\n",
       "EDUARDO AMORIM                    944706.71         431\n",
       "CÍCERO LUCENA                     928512.63         176\n",
       "PAULO PAIM                        926441.36        2026\n",
       "ANTÔNIO CARLOS VALADARES          915219.04         158\n",
       "EPITÁCIO CAFETEIRA                906923.51         235\n",
       "JOÃO CAPIBERIBE                   901231.95        3276\n",
       "JOÃO ALBERTO SOUZA                895337.78         205\n",
       "PAULO BAUER                       885371.30        2762\n",
       "VANESSA GRAZZIOTIN                851877.10         580\n",
       "JORGE VIANA                       847856.79         353\n",
       "LÚCIA VÂNIA                       844748.12         383\n",
       "JOSÉ PIMENTEL                     795697.52         589\n",
       "ZEZÉ PERRELLA                     781158.08        1263\n",
       "SÉRGIO PETECÃO                    766003.58         162\n",
       "TELMÁRIO MOTA                     755143.29        1236\n",
       "GLADSON CAMELI                    745837.07         351\n",
       "MOZARILDO CAVALCANTI              722666.83        1765\n",
       "JAYME CAMPOS                      720786.63         257\n",
       "WELLINGTON DIAS                   714749.95         213\n",
       "MÁRIO COUTO                       679127.02        2401\n",
       "INÁCIO ARRUDA                     678180.10         642"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc_investigacao_final.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62064d85",
   "metadata": {},
   "source": [
    "### Buscando outliers usando interquartil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28e15410",
   "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>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CIRO NOGUEIRA</th>\n",
       "      <td>2423362.67</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEXA RIBEIRO</th>\n",
       "      <td>1487410.74</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICENTINHO ALVES</th>\n",
       "      <td>1449320.62</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HUMBERTO COSTA</th>\n",
       "      <td>1365448.64</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO DURVAL</th>\n",
       "      <td>1325686.69</td>\n",
       "      <td>1535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVO CASSOL</th>\n",
       "      <td>1174359.06</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>1145895.36</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACIR GURGACZ</th>\n",
       "      <td>1117874.96</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ AGRIPINO</th>\n",
       "      <td>1116724.34</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO AMORIM</th>\n",
       "      <td>944706.71</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CÍCERO LUCENA</th>\n",
       "      <td>928512.63</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO PAIM</th>\n",
       "      <td>926441.36</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTÔNIO CARLOS VALADARES</th>\n",
       "      <td>915219.04</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPITÁCIO CAFETEIRA</th>\n",
       "      <td>906923.51</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO CAPIBERIBE</th>\n",
       "      <td>901231.95</td>\n",
       "      <td>3276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO ALBERTO SOUZA</th>\n",
       "      <td>895337.78</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO BAUER</th>\n",
       "      <td>885371.30</td>\n",
       "      <td>2762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>851877.10</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JORGE VIANA</th>\n",
       "      <td>847856.79</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LÚCIA VÂNIA</th>\n",
       "      <td>844748.12</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ PIMENTEL</th>\n",
       "      <td>795697.52</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEZÉ PERRELLA</th>\n",
       "      <td>781158.08</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>755143.29</td>\n",
       "      <td>1236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOZARILDO CAVALCANTI</th>\n",
       "      <td>722666.83</td>\n",
       "      <td>1765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MÁRIO COUTO</th>\n",
       "      <td>679127.02</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INÁCIO ARRUDA</th>\n",
       "      <td>678180.10</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANÍBAL DINIZ</th>\n",
       "      <td>544836.20</td>\n",
       "      <td>829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>521799.44</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO REQUIÃO</th>\n",
       "      <td>496718.46</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LÍDICE DA MATA</th>\n",
       "      <td>445448.20</td>\n",
       "      <td>1136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASILDO MALDANER</th>\n",
       "      <td>419973.84</td>\n",
       "      <td>971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO LOPES</th>\n",
       "      <td>419603.05</td>\n",
       "      <td>1806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELISEU RESENDE</th>\n",
       "      <td>372715.88</td>\n",
       "      <td>1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARCELO CRIVELLA</th>\n",
       "      <td>347978.98</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RANDOLFE RODRIGUES</th>\n",
       "      <td>309359.68</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ NERY</th>\n",
       "      <td>302278.61</td>\n",
       "      <td>1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLÁVIO ARNS</th>\n",
       "      <td>280545.00</td>\n",
       "      <td>2164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSMAR DIAS</th>\n",
       "      <td>246412.48</td>\n",
       "      <td>984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA CLEIDE</th>\n",
       "      <td>233571.24</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RONALDO CAIADO</th>\n",
       "      <td>210062.93</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VITAL DO RÊGO</th>\n",
       "      <td>208239.38</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO SOUZA</th>\n",
       "      <td>198998.61</td>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARISA SERRANO</th>\n",
       "      <td>192477.25</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIMUNDO COLOMBO</th>\n",
       "      <td>192020.02</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CÉSAR BORGES</th>\n",
       "      <td>186609.14</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA BEZERRA</th>\n",
       "      <td>129807.29</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENAN CALHEIROS</th>\n",
       "      <td>118342.17</td>\n",
       "      <td>746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLINGTON SALGADO DE OLIVEIRA</th>\n",
       "      <td>106193.41</td>\n",
       "      <td>646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARINOR BRITO</th>\n",
       "      <td>104419.97</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GERSON CAMATA</th>\n",
       "      <td>93555.31</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO SUPLICY</th>\n",
       "      <td>92596.65</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRANCISCO DORNELLES</th>\n",
       "      <td>90871.82</td>\n",
       "      <td>802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALOIZIO MERCADANTE</th>\n",
       "      <td>41253.86</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                VALOR_REEMBOLSADO  TOTAL_NULO\n",
       "SENADOR                                                      \n",
       "CIRO NOGUEIRA                          2423362.67         533\n",
       "FLEXA RIBEIRO                          1487410.74         339\n",
       "VICENTINHO ALVES                       1449320.62         368\n",
       "HUMBERTO COSTA                         1365448.64        1744\n",
       "JOÃO DURVAL                            1325686.69        1535\n",
       "IVO CASSOL                             1174359.06         757\n",
       "VALDIR RAUPP                           1145895.36         128\n",
       "ACIR GURGACZ                           1117874.96         208\n",
       "JOSÉ AGRIPINO                          1116724.34         257\n",
       "EDUARDO AMORIM                          944706.71         431\n",
       "CÍCERO LUCENA                           928512.63         176\n",
       "PAULO PAIM                              926441.36        2026\n",
       "ANTÔNIO CARLOS VALADARES                915219.04         158\n",
       "EPITÁCIO CAFETEIRA                      906923.51         235\n",
       "JOÃO CAPIBERIBE                         901231.95        3276\n",
       "JOÃO ALBERTO SOUZA                      895337.78         205\n",
       "PAULO BAUER                             885371.30        2762\n",
       "VANESSA GRAZZIOTIN                      851877.10         580\n",
       "JORGE VIANA                             847856.79         353\n",
       "LÚCIA VÂNIA                             844748.12         383\n",
       "JOSÉ PIMENTEL                           795697.52         589\n",
       "ZEZÉ PERRELLA                           781158.08        1263\n",
       "TELMÁRIO MOTA                           755143.29        1236\n",
       "MOZARILDO CAVALCANTI                    722666.83        1765\n",
       "MÁRIO COUTO                             679127.02        2401\n",
       "INÁCIO ARRUDA                           678180.10         642\n",
       "ANÍBAL DINIZ                            544836.20         829\n",
       "FERNANDO COLLOR                         521799.44         651\n",
       "ROBERTO REQUIÃO                         496718.46         601\n",
       "LÍDICE DA MATA                          445448.20        1136\n",
       "CASILDO MALDANER                        419973.84         971\n",
       "EDUARDO LOPES                           419603.05        1806\n",
       "ELISEU RESENDE                          372715.88        1333\n",
       "MARCELO CRIVELLA                        347978.98        2200\n",
       "RANDOLFE RODRIGUES                      309359.68         525\n",
       "JOSÉ NERY                               302278.61        1250\n",
       "FLÁVIO ARNS                             280545.00        2164\n",
       "OSMAR DIAS                              246412.48         984\n",
       "FÁTIMA CLEIDE                           233571.24         566\n",
       "RONALDO CAIADO                          210062.93        1235\n",
       "VITAL DO RÊGO                           208239.38         629\n",
       "SÉRGIO SOUZA                            198998.61        1289\n",
       "MARISA SERRANO                          192477.25         527\n",
       "RAIMUNDO COLOMBO                        192020.02         688\n",
       "CÉSAR BORGES                            186609.14         630\n",
       "FÁTIMA BEZERRA                          129807.29        1008\n",
       "RENAN CALHEIROS                         118342.17         746\n",
       "WELLINGTON SALGADO DE OLIVEIRA          106193.41         646\n",
       "MARINOR BRITO                           104419.97         961\n",
       "GERSON CAMATA                            93555.31         598\n",
       "EDUARDO SUPLICY                          92596.65         730\n",
       "FRANCISCO DORNELLES                      90871.82         802\n",
       "ALOIZIO MERCADANTE                       41253.86         636"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculando quartis e interquartil\n",
    "loc_nulo_q1 = np.percentile(df_loc_investigacao_final[\"TOTAL_NULO\"], 25)\n",
    "loc_nulo_q3 = np.percentile(df_loc_investigacao_final[\"TOTAL_NULO\"], 75)\n",
    "loc_nulo_iqr = loc_nulo_q3 - loc_nulo_q1\n",
    "lower_loc_nulo = loc_nulo_q1 - 1.5*loc_nulo_iqr\n",
    "upper_loc_nulo = loc_nulo_q1 + 1.5*loc_nulo_iqr\n",
    "\n",
    "loc_valor_q1 = np.percentile(df_loc_investigacao_final[\"VALOR_REEMBOLSADO\"], 25)\n",
    "loc_valor_q3 = np.percentile(df_loc_investigacao_final[\"VALOR_REEMBOLSADO\"], 75)\n",
    "loc_valor_iqr = loc_valor_q3 - loc_valor_q1\n",
    "lower_loc_valor = loc_valor_q1 - 1.5*loc_valor_iqr\n",
    "upper_loc_valor = loc_valor_q3 + 1.5*loc_valor_iqr\n",
    "\n",
    "expressao_loc_outliers = \"(TOTAL_NULO >= @upper_loc_nulo) | (VALOR_REEMBOLSADO >= @upper_loc_valor)\"\n",
    "\n",
    "\n",
    "df_loc_investigacao_final_outliers = df_loc_investigacao_final.query(expressao_loc_outliers)\n",
    "\n",
    "df_loc_investigacao_final_outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4463fa6",
   "metadata": {},
   "source": [
    "Parlamentares a serem investigados:\n",
    "\n",
    "    Humberto Costa;\n",
    "    João Durval;\n",
    "    Paulo Paim;\n",
    "    João Capiberibe;\n",
    "    Paulo Bauer;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f801a78",
   "metadata": {},
   "source": [
    "### Parlamentar Exemplo da Categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b88e6480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VALOR_REEMBOLSADO    435.15\n",
       "TOTAL_NULO             0.00\n",
       "Name: EDISON LOBÃO, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc_investigacao_final.loc[\"EDISON LOBÃO\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4afc0d66",
   "metadata": {},
   "source": [
    "Embora Edison Lobão não seja o parlamentar que menos gastou na categoria, ela se destaca por ser o primeiro parlamentar, seguindo a ordem crescente de gastos, a ter informado todos os dados de reembolso, mesmo tendo utilizado a cota parlamentar de forma mais parcimoniosa."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b599982b",
   "metadata": {},
   "source": [
    "# Segunda Análise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795ac78c",
   "metadata": {},
   "source": [
    "## Serviços de Segurança Privada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2c07710d",
   "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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>SENADOR</th>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th>CNPJ_CPF</th>\n",
       "      <th>FORNECEDOR</th>\n",
       "      <th>DOCUMENTO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>DETALHAMENTO</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38794</th>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>ANA RITA</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>01.887.130/0001-50</td>\n",
       "      <td>ECOS SEGURANÇA ELETRONICA LTDA ME</td>\n",
       "      <td>06437</td>\n",
       "      <td>18/08/2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38841</th>\n",
       "      <td>2011</td>\n",
       "      <td>9</td>\n",
       "      <td>ANA RITA</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>07.633.895/0001-40</td>\n",
       "      <td>DIGITAL SERVIÇOS EM SEGURANÇA  ELETRÔNICA LTDA...</td>\n",
       "      <td>05752</td>\n",
       "      <td>01/09/2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38946</th>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>ANA RITA</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>01.887.130/0001-50</td>\n",
       "      <td>ECOS SEGURANÇA ELETRÔNICA LTDA-ME</td>\n",
       "      <td>07365</td>\n",
       "      <td>15/12/2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39699</th>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>ANÍBAL DINIZ</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>06.373.173/0001-31</td>\n",
       "      <td>Inviolável R. Branco Alarmes Monitorados</td>\n",
       "      <td>0407</td>\n",
       "      <td>13/01/2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42102</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>CLÉSIO ANDRADE</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>02.059.753/0001-06</td>\n",
       "      <td>Emive Patrulha 24 horas Ltda</td>\n",
       "      <td>2011/88415</td>\n",
       "      <td>15/06/2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ANO  MES         SENADOR                   TIPO_DESPESA  \\\n",
       "38794  2011    8        ANA RITA  Serviços de Segurança Privada   \n",
       "38841  2011    9        ANA RITA  Serviços de Segurança Privada   \n",
       "38946  2011   12        ANA RITA  Serviços de Segurança Privada   \n",
       "39699  2011   12    ANÍBAL DINIZ  Serviços de Segurança Privada   \n",
       "42102  2011    6  CLÉSIO ANDRADE  Serviços de Segurança Privada   \n",
       "\n",
       "                 CNPJ_CPF                                         FORNECEDOR  \\\n",
       "38794  01.887.130/0001-50                  ECOS SEGURANÇA ELETRONICA LTDA ME   \n",
       "38841  07.633.895/0001-40  DIGITAL SERVIÇOS EM SEGURANÇA  ELETRÔNICA LTDA...   \n",
       "38946  01.887.130/0001-50                  ECOS SEGURANÇA ELETRÔNICA LTDA-ME   \n",
       "39699  06.373.173/0001-31           Inviolável R. Branco Alarmes Monitorados   \n",
       "42102  02.059.753/0001-06                       Emive Patrulha 24 horas Ltda   \n",
       "\n",
       "        DOCUMENTO        DATA DETALHAMENTO  VALOR_REEMBOLSADO  \n",
       "38794       06437  18/08/2011          NaN              100.0  \n",
       "38841       05752  01/09/2011          NaN              150.0  \n",
       "38946       07365  15/12/2011          NaN              100.0  \n",
       "39699        0407  13/01/2012          NaN              180.0  \n",
       "42102  2011/88415  15/06/2011          NaN              109.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg = df_cota_parlamentar[df_cota_parlamentar[\"TIPO_DESPESA\"] == \"Serviços de Segurança Privada\"]\n",
    "df_seg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8afb29e5",
   "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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>851.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>851.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.698002</td>\n",
       "      <td>6.388954</td>\n",
       "      <td>3520.362056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.143495</td>\n",
       "      <td>3.482436</td>\n",
       "      <td>6269.709108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>189.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4300.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>28922.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ANO         MES  VALOR_REEMBOLSADO\n",
       "count   851.000000  851.000000         851.000000\n",
       "mean   2014.698002    6.388954        3520.362056\n",
       "std       2.143495    3.482436        6269.709108\n",
       "min    2011.000000    1.000000          24.000000\n",
       "25%    2013.000000    3.000000         189.000000\n",
       "50%    2015.000000    6.000000         520.000000\n",
       "75%    2016.000000    9.000000        4300.110000\n",
       "max    2019.000000   12.000000       28922.720000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9790e7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Valor Reembolsado'),\n",
       " Text(0, 0.5, 'Quantidade de Reembolsos'),\n",
       " Text(0.5, 1.0, 'Distribuição de reembolsos por ticket na categoria de Segurança')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1170x827 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_seg[\"VALOR_REEMBOLSADO\"], kde=True).set(xlabel=\"Valor Reembolsado\", ylabel=\"Quantidade de Reembolsos\", title=\"Distribuição de reembolsos por ticket na categoria de Segurança\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3d56e74f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANO                                           2016\n",
       "MES                                              2\n",
       "SENADOR                            FERNANDO COLLOR\n",
       "TIPO_DESPESA         Serviços de Segurança Privada\n",
       "CNPJ_CPF                        04.850.792/0001-44\n",
       "FORNECEDOR                           Citel Service\n",
       "DOCUMENTO                                     1176\n",
       "DATA                                    01/02/2016\n",
       "DETALHAMENTO                                   NaN\n",
       "VALOR_REEMBOLSADO                         28922.72\n",
       "Name: 177257, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg.loc[df_seg[\"VALOR_REEMBOLSADO\"].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "470c8251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>SENADOR</th>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th>CNPJ_CPF</th>\n",
       "      <th>FORNECEDOR</th>\n",
       "      <th>DOCUMENTO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>DETALHAMENTO</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177257</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1176</td>\n",
       "      <td>01/02/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28922.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151346</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1030</td>\n",
       "      <td>01/02/2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28174.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177259</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1187</td>\n",
       "      <td>01/03/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177267</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1208</td>\n",
       "      <td>01/07/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177268</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1220</td>\n",
       "      <td>26/08/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177271</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1228</td>\n",
       "      <td>01/10/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177273</th>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1237</td>\n",
       "      <td>01/11/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177274</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1242</td>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204319</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1245</td>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204320</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>Serviços de Segurança Privada</td>\n",
       "      <td>04.850.792/0001-44</td>\n",
       "      <td>Citel Service</td>\n",
       "      <td>1251</td>\n",
       "      <td>01/05/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ANO  MES          SENADOR                   TIPO_DESPESA  \\\n",
       "177257  2016    2  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "151346  2015    2  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "177259  2016    3  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "177267  2016    8  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "177268  2016    8  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "177271  2016   10  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "177273  2016   11  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "177274  2016   12  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "204319  2017    1  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "204320  2017    2  FERNANDO COLLOR  Serviços de Segurança Privada   \n",
       "\n",
       "                  CNPJ_CPF     FORNECEDOR DOCUMENTO        DATA DETALHAMENTO  \\\n",
       "177257  04.850.792/0001-44  Citel Service      1176  01/02/2016          NaN   \n",
       "151346  04.850.792/0001-44  Citel Service      1030  01/02/2015          NaN   \n",
       "177259  04.850.792/0001-44  Citel Service      1187  01/03/2016          NaN   \n",
       "177267  04.850.792/0001-44  Citel Service      1208  01/07/2016          NaN   \n",
       "177268  04.850.792/0001-44  Citel Service      1220  26/08/2016          NaN   \n",
       "177271  04.850.792/0001-44  Citel Service      1228  01/10/2016          NaN   \n",
       "177273  04.850.792/0001-44  Citel Service      1237  01/11/2016          NaN   \n",
       "177274  04.850.792/0001-44  Citel Service      1242  01/12/2016          NaN   \n",
       "204319  04.850.792/0001-44  Citel Service      1245  01/12/2016          NaN   \n",
       "204320  04.850.792/0001-44  Citel Service      1251  01/05/2017          NaN   \n",
       "\n",
       "        VALOR_REEMBOLSADO  \n",
       "177257           28922.72  \n",
       "151346           28174.24  \n",
       "177259           27655.07  \n",
       "177267           27655.07  \n",
       "177268           27655.07  \n",
       "177271           27655.07  \n",
       "177273           27655.07  \n",
       "177274           27655.07  \n",
       "204319           27655.07  \n",
       "204320           27655.07  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg_outliers = df_seg.nlargest(100, \"VALOR_REEMBOLSADO\")\n",
    "df_seg_outliers.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74bdfc2a",
   "metadata": {},
   "source": [
    "### Solicitação para investigar mais a fundo\n",
    "\n",
    "Fernando color, valores altos recorrentes com a mesma empresa, faltando detalhamento de alguns registros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8fd1d396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANO                                           2016\n",
       "MES                                              2\n",
       "SENADOR                            FERNANDO COLLOR\n",
       "TIPO_DESPESA         Serviços de Segurança Privada\n",
       "CNPJ_CPF                        04.850.792/0001-44\n",
       "FORNECEDOR                           Citel Service\n",
       "DOCUMENTO                                     1176\n",
       "DATA                                    01/02/2016\n",
       "DETALHAMENTO                                   NaN\n",
       "VALOR_REEMBOLSADO                         28922.72\n",
       "Name: 177257, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg_outliers.loc[177257]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de62908",
   "metadata": {},
   "source": [
    "### Top 5 parlamentares para serem investigados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1ac25b45",
   "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>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>1691331.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ AGRIPINO</th>\n",
       "      <td>361630.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO ROCHA</th>\n",
       "      <td>232510.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOBÃO FILHO</th>\n",
       "      <td>219600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO CAPIBERIBE</th>\n",
       "      <td>76360.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VALOR_REEMBOLSADO\n",
       "SENADOR                           \n",
       "FERNANDO COLLOR         1691331.81\n",
       "JOSÉ AGRIPINO            361630.75\n",
       "ROBERTO ROCHA            232510.51\n",
       "LOBÃO FILHO              219600.00\n",
       "JOÃO CAPIBERIBE           76360.94"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg_investigacao = df_seg.groupby([\"SENADOR\"])[\"VALOR_REEMBOLSADO\"].sum().sort_values(ascending=False)\n",
    "df_seg_investigacao = pd.DataFrame(df_seg_investigacao, columns=[\"VALOR_REEMBOLSADO\"])\n",
    "df_seg_investigacao.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3eb71151",
   "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>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JOSÉ AGRIPINO</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLADSON CAMELI</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOBÃO FILHO</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA BEZERRA</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TOTAL_NULO\n",
       "SENADOR                    \n",
       "JOSÉ AGRIPINO            78\n",
       "GLADSON CAMELI           69\n",
       "FERNANDO COLLOR          56\n",
       "LOBÃO FILHO              38\n",
       "FÁTIMA BEZERRA           29"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg_null = df_seg.loc[:, [\"CNPJ_CPF\", \"FORNECEDOR\", \"DOCUMENTO\", \"DATA\", \n",
    "                              \"DETALHAMENTO\"]].isnull().groupby(df_seg[\"SENADOR\"]).sum()\n",
    "df_seg_null[\"TOTAL_NULO\"] = df_seg_null.sum(axis=1).sort_values(ascending=False)\n",
    "df_seg_null.drop([\"CNPJ_CPF\", \"FORNECEDOR\", \"DOCUMENTO\", \"DATA\", \n",
    "                  \"DETALHAMENTO\"], axis=1, inplace=True)\n",
    "df_seg_null.sort_values(\"TOTAL_NULO\", ascending=False, inplace=True)\n",
    "df_seg_null.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "11ee4308",
   "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>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.991656e+04</td>\n",
       "      <td>9.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.446479e+05</td>\n",
       "      <td>16.991727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.234875e+02</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.996900e+03</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.862144e+04</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.691332e+06</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VALOR_REEMBOLSADO  TOTAL_NULO\n",
       "count       5.000000e+01   50.000000\n",
       "mean        5.991656e+04    9.660000\n",
       "std         2.446479e+05   16.991727\n",
       "min         4.500000e+01    0.000000\n",
       "25%         7.234875e+02    0.250000\n",
       "50%         3.996900e+03    4.000000\n",
       "75%         1.862144e+04    9.000000\n",
       "max         1.691332e+06   78.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg_investigacao_final = pd.merge(df_seg_investigacao, df_seg_null, on=\"SENADOR\").sort_values([\"VALOR_REEMBOLSADO\", \"TOTAL_NULO\"], ascending=[False,False])\n",
    "df_seg_investigacao_final.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3b517ae3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>1691331.81</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ AGRIPINO</th>\n",
       "      <td>361630.75</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO ROCHA</th>\n",
       "      <td>232510.51</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOBÃO FILHO</th>\n",
       "      <td>219600.00</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO CAPIBERIBE</th>\n",
       "      <td>76360.94</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KÁTIA ABREU</th>\n",
       "      <td>74986.68</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JARBAS VASCONCELOS</th>\n",
       "      <td>40874.12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEXA RIBEIRO</th>\n",
       "      <td>37099.13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAILZA GOMES</th>\n",
       "      <td>28200.00</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO LOPES</th>\n",
       "      <td>24900.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLADSON CAMELI</th>\n",
       "      <td>21457.84</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDRO CHAVES DOS SANTOS FILHO</th>\n",
       "      <td>21145.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDISON LOBÃO</th>\n",
       "      <td>19520.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SODRÉ SANTORO</th>\n",
       "      <td>15925.75</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA BEZERRA</th>\n",
       "      <td>15051.52</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLÉSIO ANDRADE</th>\n",
       "      <td>14563.37</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>12872.32</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELMANO FÉRRER</th>\n",
       "      <td>10015.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO ALBERTO SOUZA</th>\n",
       "      <td>9600.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LÚCIA VÂNIA</th>\n",
       "      <td>9048.63</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO AMORIM</th>\n",
       "      <td>8790.91</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOZARILDO CAVALCANTI</th>\n",
       "      <td>6767.82</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARA GABRILLI</th>\n",
       "      <td>6682.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELIZIANE GAMA</th>\n",
       "      <td>5850.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYRO MIRANDA</th>\n",
       "      <td>4343.80</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WILDER MORAIS</th>\n",
       "      <td>3650.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>3000.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO PAIM</th>\n",
       "      <td>2907.92</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RONALDO CAIADO</th>\n",
       "      <td>2543.02</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAVI ALCOLUMBRE</th>\n",
       "      <td>2259.56</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO REQUIÃO</th>\n",
       "      <td>1561.55</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALDEMIR MOKA</th>\n",
       "      <td>1400.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROMERO JUCÁ</th>\n",
       "      <td>1080.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANÍBAL DINIZ</th>\n",
       "      <td>972.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARIBALDI ALVES FILHO</th>\n",
       "      <td>933.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARINOR BRITO</th>\n",
       "      <td>900.00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JORGE VIANA</th>\n",
       "      <td>779.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HUMBERTO COSTA</th>\n",
       "      <td>704.80</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO SOUZA</th>\n",
       "      <td>679.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESPERIDIÃO AMIN</th>\n",
       "      <td>575.67</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THIERES PINTO</th>\n",
       "      <td>540.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAURO ANTÔNIO</th>\n",
       "      <td>487.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANA RITA</th>\n",
       "      <td>450.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEMÓSTENES TORRES</th>\n",
       "      <td>441.87</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AÉCIO NEVES</th>\n",
       "      <td>237.12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGINA SOUSA</th>\n",
       "      <td>220.08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELCIDIO DO AMARAL</th>\n",
       "      <td>151.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLEISI HOFFMANN</th>\n",
       "      <td>105.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LÍDICE DA MATA</th>\n",
       "      <td>75.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO BAUER</th>\n",
       "      <td>45.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               VALOR_REEMBOLSADO  TOTAL_NULO\n",
       "SENADOR                                                     \n",
       "FERNANDO COLLOR                       1691331.81          56\n",
       "JOSÉ AGRIPINO                          361630.75          78\n",
       "ROBERTO ROCHA                          232510.51           5\n",
       "LOBÃO FILHO                            219600.00          38\n",
       "JOÃO CAPIBERIBE                         76360.94           0\n",
       "KÁTIA ABREU                             74986.68           5\n",
       "JARBAS VASCONCELOS                      40874.12           9\n",
       "FLEXA RIBEIRO                           37099.13          10\n",
       "MAILZA GOMES                            28200.00          13\n",
       "EDUARDO LOPES                           24900.00           7\n",
       "GLADSON CAMELI                          21457.84          69\n",
       "PEDRO CHAVES DOS SANTOS FILHO           21145.00           1\n",
       "EDISON LOBÃO                            19520.00           1\n",
       "SODRÉ SANTORO                           15925.75           2\n",
       "FÁTIMA BEZERRA                          15051.52          29\n",
       "CLÉSIO ANDRADE                          14563.37          27\n",
       "TELMÁRIO MOTA                           12872.32          25\n",
       "ELMANO FÉRRER                           10015.33           1\n",
       "JOÃO ALBERTO SOUZA                       9600.00           0\n",
       "LÚCIA VÂNIA                              9048.63           7\n",
       "EDUARDO AMORIM                           8790.91           7\n",
       "MOZARILDO CAVALCANTI                     6767.82           6\n",
       "MARA GABRILLI                            6682.00           0\n",
       "ELIZIANE GAMA                            5850.00           0\n",
       "CYRO MIRANDA                             4343.80           9\n",
       "WILDER MORAIS                            3650.00           0\n",
       "SÉRGIO PETECÃO                           3000.00           7\n",
       "PAULO PAIM                               2907.92           4\n",
       "RONALDO CAIADO                           2543.02          10\n",
       "DAVI ALCOLUMBRE                          2259.56           3\n",
       "ROBERTO REQUIÃO                          1561.55          10\n",
       "WALDEMIR MOKA                            1400.00           0\n",
       "ROMERO JUCÁ                              1080.00           9\n",
       "ANÍBAL DINIZ                              972.00           1\n",
       "GARIBALDI ALVES FILHO                     933.40           0\n",
       "MARINOR BRITO                             900.00           6\n",
       "JORGE VIANA                               779.55           0\n",
       "HUMBERTO COSTA                            704.80           6\n",
       "SÉRGIO SOUZA                              679.00           7\n",
       "ESPERIDIÃO AMIN                           575.67           3\n",
       "THIERES PINTO                             540.00           3\n",
       "LAURO ANTÔNIO                             487.90           1\n",
       "ANA RITA                                  450.00           4\n",
       "DEMÓSTENES TORRES                         441.87           3\n",
       "AÉCIO NEVES                               237.12           0\n",
       "REGINA SOUSA                              220.08           0\n",
       "DELCIDIO DO AMARAL                        151.00           0\n",
       "GLEISI HOFFMANN                           105.44           0\n",
       "LÍDICE DA MATA                             75.00           0\n",
       "PAULO BAUER                                45.00           1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg_investigacao_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1279e2ce",
   "metadata": {},
   "source": [
    "### Buscando outliers usando interquartil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e1d7e0fb",
   "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>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>1691331.81</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ AGRIPINO</th>\n",
       "      <td>361630.75</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO ROCHA</th>\n",
       "      <td>232510.51</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOBÃO FILHO</th>\n",
       "      <td>219600.00</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO CAPIBERIBE</th>\n",
       "      <td>76360.94</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KÁTIA ABREU</th>\n",
       "      <td>74986.68</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLADSON CAMELI</th>\n",
       "      <td>21457.84</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA BEZERRA</th>\n",
       "      <td>15051.52</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLÉSIO ANDRADE</th>\n",
       "      <td>14563.37</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>12872.32</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VALOR_REEMBOLSADO  TOTAL_NULO\n",
       "SENADOR                                       \n",
       "FERNANDO COLLOR         1691331.81          56\n",
       "JOSÉ AGRIPINO            361630.75          78\n",
       "ROBERTO ROCHA            232510.51           5\n",
       "LOBÃO FILHO              219600.00          38\n",
       "JOÃO CAPIBERIBE           76360.94           0\n",
       "KÁTIA ABREU               74986.68           5\n",
       "GLADSON CAMELI            21457.84          69\n",
       "FÁTIMA BEZERRA            15051.52          29\n",
       "CLÉSIO ANDRADE            14563.37          27\n",
       "TELMÁRIO MOTA             12872.32          25"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_nulo_q1 = np.percentile(df_seg_investigacao_final[\"TOTAL_NULO\"], 25)\n",
    "seg_nulo_q3 = np.percentile(df_seg_investigacao_final[\"TOTAL_NULO\"], 75)\n",
    "seg_nulo_iqr = seg_nulo_q3 - seg_nulo_q1\n",
    "lower_seg_nulo = seg_nulo_q1 - 1.5*seg_nulo_iqr\n",
    "upper_seg_nulo = seg_nulo_q1 + 1.5*seg_nulo_iqr\n",
    "\n",
    "seg_valor_q1 = np.percentile(df_seg_investigacao_final[\"VALOR_REEMBOLSADO\"], 25)\n",
    "seg_valor_q3 = np.percentile(df_seg_investigacao_final[\"VALOR_REEMBOLSADO\"], 75)\n",
    "seg_valor_iqr = seg_valor_q3 - seg_valor_q1\n",
    "lower_seg_valor = seg_valor_q1 - 1.5*seg_valor_iqr\n",
    "upper_seg_valor = seg_valor_q3 + 1.5*seg_valor_iqr\n",
    "\n",
    "expressao_seg_outliers = \"(TOTAL_NULO >= @upper_seg_nulo) | (VALOR_REEMBOLSADO >= @upper_seg_valor)\"\n",
    "\n",
    "\n",
    "df_seg_investigacao_final_outliers = df_seg_investigacao_final.query(expressao_seg_outliers)\n",
    "\n",
    "df_seg_investigacao_final_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5bf3e5",
   "metadata": {},
   "source": [
    "Parlamentares a serem investigados:\n",
    "\n",
    "    Fernando Collor;\n",
    "    José Agripino;\n",
    "    Lobão Filho;\n",
    "    Fátima Bezerra;\n",
    "    Clésio Andrade;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e79f5cd",
   "metadata": {},
   "source": [
    "### Parlamentar Exemplo da Categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3774a5b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VALOR_REEMBOLSADO    75.0\n",
       "TOTAL_NULO            0.0\n",
       "Name: LÍDICE DA MATA, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg_investigacao_final.loc[\"LÍDICE DA MATA\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ba1055",
   "metadata": {},
   "source": [
    "Embora Lídice da Mata não seja o parlamentar que menos gastou na categoria, ela se destaca por ser o primeiro parlamentar, seguindo a ordem crescente de gastos, a ter informado todos os dados de reembolso."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a576358",
   "metadata": {},
   "source": [
    "# Terceira Análise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1d0826",
   "metadata": {},
   "source": [
    "## Divulgação da atividade parlamentar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "daa2cff1",
   "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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>SENADOR</th>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th>CNPJ_CPF</th>\n",
       "      <th>FORNECEDOR</th>\n",
       "      <th>DOCUMENTO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>DETALHAMENTO</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>ADELMIR SANTANA</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>ALMEIDA LIMA</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>ALMEIDA LIMA</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>ALOIZIO MERCADANTE</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>ALOIZIO MERCADANTE</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1575.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ANO  MES             SENADOR                         TIPO_DESPESA  \\\n",
       "26  2008   11     ADELMIR SANTANA  Divulgação da atividade parlamentar   \n",
       "33  2008    2        ALMEIDA LIMA  Divulgação da atividade parlamentar   \n",
       "64  2008   12        ALMEIDA LIMA  Divulgação da atividade parlamentar   \n",
       "72  2008    3  ALOIZIO MERCADANTE  Divulgação da atividade parlamentar   \n",
       "76  2008    4  ALOIZIO MERCADANTE  Divulgação da atividade parlamentar   \n",
       "\n",
       "   CNPJ_CPF FORNECEDOR DOCUMENTO DATA DETALHAMENTO  VALOR_REEMBOLSADO  \n",
       "26      NaN        NaN       NaN  NaN          NaN            15000.0  \n",
       "33      NaN        NaN       NaN  NaN          NaN            10500.0  \n",
       "64      NaN        NaN       NaN  NaN          NaN            35000.0  \n",
       "72      NaN        NaN       NaN  NaN          NaN             6300.0  \n",
       "76      NaN        NaN       NaN  NaN          NaN             1575.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul = df_cota_parlamentar[df_cota_parlamentar[\"TIPO_DESPESA\"] == \"Divulgação da atividade parlamentar\"]\n",
    "df_divul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4299462e",
   "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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10048.000000</td>\n",
       "      <td>10048.000000</td>\n",
       "      <td>10048.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.101413</td>\n",
       "      <td>6.927747</td>\n",
       "      <td>3167.595575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.078542</td>\n",
       "      <td>3.535788</td>\n",
       "      <td>7249.372000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>270000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ANO           MES  VALOR_REEMBOLSADO\n",
       "count  10048.000000  10048.000000       10048.000000\n",
       "mean    2014.101413      6.927747        3167.595575\n",
       "std        3.078542      3.535788        7249.372000\n",
       "min     2008.000000      1.000000           0.600000\n",
       "25%     2012.000000      4.000000         800.000000\n",
       "50%     2014.000000      7.000000        1500.000000\n",
       "75%     2017.000000     10.000000        3000.000000\n",
       "max     2019.000000     12.000000      270000.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6d0fdfc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Valor Reembolsado'),\n",
       " Text(0, 0.5, 'Quantidade de Reembolsos'),\n",
       " Text(0.5, 1.0, 'Distribuição de reembolsos por ticket na categoria de Divulgação')]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1170x827 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_divul[\"VALOR_REEMBOLSADO\"], kde=True).set(xlabel=\"Valor Reembolsado\", ylabel=\"Quantidade de Reembolsos\", title=\"Distribuição de reembolsos por ticket na categoria de Divulgação\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "39ab83c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANO                                                   2017\n",
       "MES                                                     12\n",
       "SENADOR                                    DAVI ALCOLUMBRE\n",
       "TIPO_DESPESA           Divulgação da atividade parlamentar\n",
       "CNPJ_CPF                                29.337.655/0001-31\n",
       "FORNECEDOR           Start print Comunicação Visual Eireli\n",
       "DOCUMENTO                                               36\n",
       "DATA                                            15/03/2018\n",
       "DETALHAMENTO                                           NaN\n",
       "VALOR_REEMBOLSADO                                 270000.0\n",
       "Name: 201299, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul.loc[df_divul[\"VALOR_REEMBOLSADO\"].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8bb4e597",
   "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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>SENADOR</th>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th>CNPJ_CPF</th>\n",
       "      <th>FORNECEDOR</th>\n",
       "      <th>DOCUMENTO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>DETALHAMENTO</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201299</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>DAVI ALCOLUMBRE</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>29.337.655/0001-31</td>\n",
       "      <td>Start print Comunicação Visual Eireli</td>\n",
       "      <td>36</td>\n",
       "      <td>15/03/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174534</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>DAVI ALCOLUMBRE</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>04.953.008/0001-23</td>\n",
       "      <td>ARTE &amp; IMAGEM GRAFICA E EDITORA EIRELI ME</td>\n",
       "      <td>202</td>\n",
       "      <td>22/03/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>256980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148380</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>DAVI ALCOLUMBRE</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>38.006.631/0001-90</td>\n",
       "      <td>Gráfica e Editora Paranaíba Ltda - ME</td>\n",
       "      <td>242</td>\n",
       "      <td>29/03/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138773</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>PAULO PAIM</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>07.053.387/0001-93</td>\n",
       "      <td>Algo Mais Artes Gráficas e Editora Ltda</td>\n",
       "      <td>12786</td>\n",
       "      <td>17/12/2014</td>\n",
       "      <td>Impressão de 800 mil informativos, 30 mil esta...</td>\n",
       "      <td>162400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166059</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>ROSE DE FREITAS</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>28.130.334/0001-07</td>\n",
       "      <td>Gráfica Samorini Ltda Epp</td>\n",
       "      <td>269</td>\n",
       "      <td>29/03/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244762</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>ROSE DE FREITAS</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>05.461.408/0001-84</td>\n",
       "      <td>Grafitusa S/A</td>\n",
       "      <td>9681</td>\n",
       "      <td>29/03/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80571</th>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>LINDBERGH FARIAS</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>00.971.215/0001-50</td>\n",
       "      <td>3 GRAPH GRAFICA E EDITORA LTDA</td>\n",
       "      <td>00000998</td>\n",
       "      <td>11/12/2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169207</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>WELLINGTON FAGUNDES</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>08.513.041/0001-93</td>\n",
       "      <td>Rodovias e Vias Editora e Produções LTDA - ME</td>\n",
       "      <td>224</td>\n",
       "      <td>23/12/2015</td>\n",
       "      <td>Despesas com serviços técnicos de comunicação ...</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192789</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>SÉRGIO PETECÃO</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>12.974.269/0001-58</td>\n",
       "      <td>GADELHA E SILVEIRA EMPRESA DE COMUNICAÇÃO LTDA</td>\n",
       "      <td>140</td>\n",
       "      <td>23/12/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192606</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>ROSE DE FREITAS</td>\n",
       "      <td>Divulgação da atividade parlamentar</td>\n",
       "      <td>05.461.408/0001-84</td>\n",
       "      <td>Grafitusa S/A</td>\n",
       "      <td>32577</td>\n",
       "      <td>31/03/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79680.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ANO  MES              SENADOR                         TIPO_DESPESA  \\\n",
       "201299  2017   12      DAVI ALCOLUMBRE  Divulgação da atividade parlamentar   \n",
       "174534  2016   12      DAVI ALCOLUMBRE  Divulgação da atividade parlamentar   \n",
       "148380  2015   12      DAVI ALCOLUMBRE  Divulgação da atividade parlamentar   \n",
       "138773  2014   12           PAULO PAIM  Divulgação da atividade parlamentar   \n",
       "166059  2015   12      ROSE DE FREITAS  Divulgação da atividade parlamentar   \n",
       "244762  2018   12      ROSE DE FREITAS  Divulgação da atividade parlamentar   \n",
       "80571   2012   12     LINDBERGH FARIAS  Divulgação da atividade parlamentar   \n",
       "169207  2015   12  WELLINGTON FAGUNDES  Divulgação da atividade parlamentar   \n",
       "192789  2016   12       SÉRGIO PETECÃO  Divulgação da atividade parlamentar   \n",
       "192606  2016   12      ROSE DE FREITAS  Divulgação da atividade parlamentar   \n",
       "\n",
       "                  CNPJ_CPF                                      FORNECEDOR  \\\n",
       "201299  29.337.655/0001-31           Start print Comunicação Visual Eireli   \n",
       "174534  04.953.008/0001-23       ARTE & IMAGEM GRAFICA E EDITORA EIRELI ME   \n",
       "148380  38.006.631/0001-90           Gráfica e Editora Paranaíba Ltda - ME   \n",
       "138773  07.053.387/0001-93         Algo Mais Artes Gráficas e Editora Ltda   \n",
       "166059  28.130.334/0001-07                       Gráfica Samorini Ltda Epp   \n",
       "244762  05.461.408/0001-84                                   Grafitusa S/A   \n",
       "80571   00.971.215/0001-50                  3 GRAPH GRAFICA E EDITORA LTDA   \n",
       "169207  08.513.041/0001-93   Rodovias e Vias Editora e Produções LTDA - ME   \n",
       "192789  12.974.269/0001-58  GADELHA E SILVEIRA EMPRESA DE COMUNICAÇÃO LTDA   \n",
       "192606  05.461.408/0001-84                                   Grafitusa S/A   \n",
       "\n",
       "       DOCUMENTO        DATA  \\\n",
       "201299        36  15/03/2018   \n",
       "174534       202  22/03/2017   \n",
       "148380       242  29/03/2016   \n",
       "138773     12786  17/12/2014   \n",
       "166059       269  29/03/2016   \n",
       "244762      9681  29/03/2019   \n",
       "80571   00000998  11/12/2012   \n",
       "169207       224  23/12/2015   \n",
       "192789       140  23/12/2016   \n",
       "192606     32577  31/03/2017   \n",
       "\n",
       "                                             DETALHAMENTO  VALOR_REEMBOLSADO  \n",
       "201299                                                NaN           270000.0  \n",
       "174534                                                NaN           256980.0  \n",
       "148380                                                NaN           216780.0  \n",
       "138773  Impressão de 800 mil informativos, 30 mil esta...           162400.0  \n",
       "166059                                                NaN           134100.0  \n",
       "244762                                                NaN           103900.0  \n",
       "80571                                                 NaN            88245.0  \n",
       "169207  Despesas com serviços técnicos de comunicação ...            85000.0  \n",
       "192789                                                NaN            80000.0  \n",
       "192606                                                NaN            79680.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul_outliers = df_divul.nlargest(100, \"VALOR_REEMBOLSADO\")\n",
    "df_divul_outliers.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4fdf0c",
   "metadata": {},
   "source": [
    "### Solicitação para investigar mais a fundo\n",
    "\n",
    "Davi Alcolombre, ticket alto, faltando detalhamento de alguns registros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "93a0f935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANO                                                   2017\n",
       "MES                                                     12\n",
       "SENADOR                                    DAVI ALCOLUMBRE\n",
       "TIPO_DESPESA           Divulgação da atividade parlamentar\n",
       "CNPJ_CPF                                29.337.655/0001-31\n",
       "FORNECEDOR           Start print Comunicação Visual Eireli\n",
       "DOCUMENTO                                               36\n",
       "DATA                                            15/03/2018\n",
       "DETALHAMENTO                                           NaN\n",
       "VALOR_REEMBOLSADO                                 270000.0\n",
       "Name: 201299, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul.loc[201299]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f43f2599",
   "metadata": {},
   "source": [
    "### Top 5 parlamentares para serem investigados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bcbf5771",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>1398246.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>1353410.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>1076650.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVO CASSOL</th>\n",
       "      <td>975047.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÂNGELA PORTELA</th>\n",
       "      <td>857662.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIÃO VIANA</th>\n",
       "      <td>413.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRAJÁ</th>\n",
       "      <td>214.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JARBAS VASCONCELOS</th>\n",
       "      <td>156.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEURY</th>\n",
       "      <td>140.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FABIANO CONTARATO</th>\n",
       "      <td>27.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VALOR_REEMBOLSADO\n",
       "SENADOR                              \n",
       "VALDIR RAUPP               1398246.15\n",
       "SÉRGIO PETECÃO             1353410.69\n",
       "VANESSA GRAZZIOTIN         1076650.34\n",
       "IVO CASSOL                  975047.01\n",
       "ÂNGELA PORTELA              857662.16\n",
       "...                               ...\n",
       "TIÃO VIANA                     413.00\n",
       "IRAJÁ                          214.02\n",
       "JARBAS VASCONCELOS             156.70\n",
       "FLEURY                         140.00\n",
       "FABIANO CONTARATO               27.00\n",
       "\n",
       "[206 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul_investigacao = df_divul.groupby([\"SENADOR\"])[\"VALOR_REEMBOLSADO\"].sum().sort_values(ascending=False)\n",
    "df_divul_investigacao = pd.DataFrame(df_divul_investigacao, columns=[\"VALOR_REEMBOLSADO\"])\n",
    "df_divul_investigacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "05f21a37",
   "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>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WALDEMIR MOKA</th>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÂNGELA PORTELA</th>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ MARANHÃO</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOZARILDO CAVALCANTI</th>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIVAGO TENÓRIO</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DALIRIO BEBER</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELBER BATALHA</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DECA</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ AMAURI</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      TOTAL_NULO\n",
       "SENADOR                         \n",
       "WALDEMIR MOKA                343\n",
       "ÂNGELA PORTELA               311\n",
       "JOSÉ MARANHÃO                309\n",
       "MOZARILDO CAVALCANTI         280\n",
       "VALDIR RAUPP                 274\n",
       "...                          ...\n",
       "GIVAGO TENÓRIO                 0\n",
       "DALIRIO BEBER                  0\n",
       "ELBER BATALHA                  0\n",
       "DECA                           0\n",
       "JOSÉ AMAURI                    0\n",
       "\n",
       "[206 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul_null = df_divul.loc[:, [\"CNPJ_CPF\", \"FORNECEDOR\", \"DOCUMENTO\", \"DATA\", \n",
    "                             \"DETALHAMENTO\"]].isnull().groupby(df_divul[\"SENADOR\"]).sum()\n",
    "df_divul_null[\"TOTAL_NULO\"] = df_divul_null.sum(axis=1).sort_values(ascending=False)\n",
    "df_divul_null.drop([\"CNPJ_CPF\", \"FORNECEDOR\", \"DOCUMENTO\", \"DATA\", \n",
    "                    \"DETALHAMENTO\"], axis=1, inplace=True)\n",
    "df_divul_null.sort_values(\"TOTAL_NULO\", ascending=False, inplace=True)\n",
    "df_divul_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b241f70b",
   "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>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.060000e+02</td>\n",
       "      <td>206.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.545049e+05</td>\n",
       "      <td>31.422330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.382740e+05</td>\n",
       "      <td>56.409576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.945200e+04</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.733050e+04</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.612847e+05</td>\n",
       "      <td>32.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.398246e+06</td>\n",
       "      <td>343.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VALOR_REEMBOLSADO  TOTAL_NULO\n",
       "count       2.060000e+02  206.000000\n",
       "mean        1.545049e+05   31.422330\n",
       "std         2.382740e+05   56.409576\n",
       "min         2.700000e+01    0.000000\n",
       "25%         1.945200e+04    2.000000\n",
       "50%         5.733050e+04   11.000000\n",
       "75%         1.612847e+05   32.750000\n",
       "max         1.398246e+06  343.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul_investigacao_final = pd.merge(df_divul_investigacao, df_divul_null, on=\"SENADOR\").sort_values([\"VALOR_REEMBOLSADO\", \"TOTAL_NULO\"], ascending=[False,False])\n",
    "df_divul_investigacao_final.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "04b70fdf",
   "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>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>1398246.15</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>1353410.69</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>1076650.34</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVO CASSOL</th>\n",
       "      <td>975047.01</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÂNGELA PORTELA</th>\n",
       "      <td>857662.16</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIÃO VIANA</th>\n",
       "      <td>413.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRAJÁ</th>\n",
       "      <td>214.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JARBAS VASCONCELOS</th>\n",
       "      <td>156.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEURY</th>\n",
       "      <td>140.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FABIANO CONTARATO</th>\n",
       "      <td>27.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    VALOR_REEMBOLSADO  TOTAL_NULO\n",
       "SENADOR                                          \n",
       "VALDIR RAUPP               1398246.15         274\n",
       "SÉRGIO PETECÃO             1353410.69          31\n",
       "VANESSA GRAZZIOTIN         1076650.34          82\n",
       "IVO CASSOL                  975047.01         258\n",
       "ÂNGELA PORTELA              857662.16         311\n",
       "...                               ...         ...\n",
       "TIÃO VIANA                     413.00           7\n",
       "IRAJÁ                          214.02           1\n",
       "JARBAS VASCONCELOS             156.70           1\n",
       "FLEURY                         140.00           0\n",
       "FABIANO CONTARATO               27.00           0\n",
       "\n",
       "[206 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul_investigacao_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f982d629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "      <th>TOTAL_NULO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENADOR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>1398246.15</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>1353410.69</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>1076650.34</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVO CASSOL</th>\n",
       "      <td>975047.01</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÂNGELA PORTELA</th>\n",
       "      <td>857662.16</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTÔNIO CARLOS VALADARES</th>\n",
       "      <td>784409.30</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAVI ALCOLUMBRE</th>\n",
       "      <td>767375.60</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO PAIM</th>\n",
       "      <td>757952.37</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO BRAGA</th>\n",
       "      <td>744051.66</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOZARILDO CAVALCANTI</th>\n",
       "      <td>729352.75</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIM ARGELLO</th>\n",
       "      <td>714410.74</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIMUNDO LIRA</th>\n",
       "      <td>663400.00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACIR GURGACZ</th>\n",
       "      <td>627538.12</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROMERO JUCÁ</th>\n",
       "      <td>571509.90</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JADER BARBALHO</th>\n",
       "      <td>569799.97</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ MARANHÃO</th>\n",
       "      <td>561210.00</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALDEMIR MOKA</th>\n",
       "      <td>557456.37</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO CAPIBERIBE</th>\n",
       "      <td>547276.00</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>518385.90</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATAÍDES OLIVEIRA</th>\n",
       "      <td>479199.20</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CÁSSIO CUNHA LIMA</th>\n",
       "      <td>449689.20</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VITAL DO RÊGO</th>\n",
       "      <td>449268.69</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICENTINHO ALVES</th>\n",
       "      <td>434297.83</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO VICENTE CLAUDINO</th>\n",
       "      <td>432602.99</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LÚCIA VÂNIA</th>\n",
       "      <td>427242.66</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO LOPES</th>\n",
       "      <td>403768.43</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROSE DE FREITAS</th>\n",
       "      <td>387420.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO REQUIÃO</th>\n",
       "      <td>386007.37</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ MEDEIROS</th>\n",
       "      <td>381979.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLADSON CAMELI</th>\n",
       "      <td>338366.99</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAYME CAMPOS</th>\n",
       "      <td>325312.07</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>293022.97</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RANDOLFE RODRIGUES</th>\n",
       "      <td>253267.37</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDRA BRAGA</th>\n",
       "      <td>249724.00</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARIBALDI ALVES FILHO</th>\n",
       "      <td>235830.70</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIDINHO SANTOS</th>\n",
       "      <td>233200.00</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INÁCIO ARRUDA</th>\n",
       "      <td>222870.44</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO RIBEIRO</th>\n",
       "      <td>219582.20</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZENAIDE MAIA</th>\n",
       "      <td>202500.00</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ AGRIPINO</th>\n",
       "      <td>200427.60</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REDITARIO CASSOL</th>\n",
       "      <td>199154.12</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VENEZIANO VITAL DO RÊGO</th>\n",
       "      <td>175100.00</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KÁTIA ABREU</th>\n",
       "      <td>167568.21</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROSALBA CIARLINI</th>\n",
       "      <td>159953.86</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRISTOVAM BUARQUE</th>\n",
       "      <td>139805.35</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUGUSTO BOTELHO</th>\n",
       "      <td>133047.15</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAPALÉO PAES</th>\n",
       "      <td>127100.00</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA BEZERRA</th>\n",
       "      <td>110692.00</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSMAR DIAS</th>\n",
       "      <td>99162.50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALTER PEREIRA</th>\n",
       "      <td>86550.60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ NERY</th>\n",
       "      <td>53088.39</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARISA SERRANO</th>\n",
       "      <td>35135.60</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARINOR BRITO</th>\n",
       "      <td>32241.25</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          VALOR_REEMBOLSADO  TOTAL_NULO\n",
       "SENADOR                                                \n",
       "VALDIR RAUPP                     1398246.15         274\n",
       "SÉRGIO PETECÃO                   1353410.69          31\n",
       "VANESSA GRAZZIOTIN               1076650.34          82\n",
       "IVO CASSOL                        975047.01         258\n",
       "ÂNGELA PORTELA                    857662.16         311\n",
       "ANTÔNIO CARLOS VALADARES          784409.30         124\n",
       "DAVI ALCOLUMBRE                   767375.60           8\n",
       "PAULO PAIM                        757952.37         119\n",
       "EDUARDO BRAGA                     744051.66          90\n",
       "MOZARILDO CAVALCANTI              729352.75         280\n",
       "GIM ARGELLO                       714410.74          29\n",
       "RAIMUNDO LIRA                     663400.00           6\n",
       "ACIR GURGACZ                      627538.12          59\n",
       "ROMERO JUCÁ                       571509.90          73\n",
       "JADER BARBALHO                    569799.97          17\n",
       "JOSÉ MARANHÃO                     561210.00         309\n",
       "WALDEMIR MOKA                     557456.37         343\n",
       "JOÃO CAPIBERIBE                   547276.00          32\n",
       "FERNANDO COLLOR                   518385.90          33\n",
       "ATAÍDES OLIVEIRA                  479199.20         205\n",
       "CÁSSIO CUNHA LIMA                 449689.20          24\n",
       "VITAL DO RÊGO                     449268.69          75\n",
       "VICENTINHO ALVES                  434297.83         112\n",
       "JOÃO VICENTE CLAUDINO             432602.99          73\n",
       "LÚCIA VÂNIA                       427242.66          47\n",
       "EDUARDO LOPES                     403768.43          29\n",
       "ROSE DE FREITAS                   387420.00           9\n",
       "ROBERTO REQUIÃO                   386007.37          47\n",
       "JOSÉ MEDEIROS                     381979.00          23\n",
       "GLADSON CAMELI                    338366.99          96\n",
       "JAYME CAMPOS                      325312.07          74\n",
       "TELMÁRIO MOTA                     293022.97         109\n",
       "RANDOLFE RODRIGUES                253267.37          65\n",
       "SANDRA BRAGA                      249724.00          49\n",
       "GARIBALDI ALVES FILHO             235830.70         145\n",
       "CIDINHO SANTOS                    233200.00          70\n",
       "INÁCIO ARRUDA                     222870.44          65\n",
       "JOÃO RIBEIRO                      219582.20          57\n",
       "ZENAIDE MAIA                      202500.00         105\n",
       "JOSÉ AGRIPINO                     200427.60          99\n",
       "REDITARIO CASSOL                  199154.12          55\n",
       "VENEZIANO VITAL DO RÊGO           175100.00         109\n",
       "KÁTIA ABREU                       167568.21          53\n",
       "ROSALBA CIARLINI                  159953.86         123\n",
       "CRISTOVAM BUARQUE                 139805.35          55\n",
       "AUGUSTO BOTELHO                   133047.15          58\n",
       "PAPALÉO PAES                      127100.00          59\n",
       "FÁTIMA BEZERRA                    110692.00         134\n",
       "OSMAR DIAS                         99162.50          50\n",
       "VALTER PEREIRA                     86550.60          60\n",
       "JOSÉ NERY                          53088.39          50\n",
       "MARISA SERRANO                     35135.60          49\n",
       "MARINOR BRITO                      32241.25          61"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divul_nulo_q1 = np.percentile(df_divul_investigacao_final[\"TOTAL_NULO\"], 25)\n",
    "divul_nulo_q3 = np.percentile(df_divul_investigacao_final[\"TOTAL_NULO\"], 75)\n",
    "divul_nulo_iqr = divul_nulo_q3 - divul_nulo_q1\n",
    "lower_divul_nulo = divul_nulo_q1 - 1.5*divul_nulo_iqr\n",
    "upper_divul_nulo = divul_nulo_q1 + 1.5*divul_nulo_iqr\n",
    "\n",
    "divul_valor_q1 = np.percentile(df_divul_investigacao_final[\"VALOR_REEMBOLSADO\"], 25)\n",
    "divul_valor_q3 = np.percentile(df_divul_investigacao_final[\"VALOR_REEMBOLSADO\"], 75)\n",
    "divul_valor_iqr = divul_valor_q3 - divul_valor_q1\n",
    "lower_divul_valor = divul_valor_q1 - 1.5*divul_valor_iqr\n",
    "upper_divul_valor = divul_valor_q3 + 1.5*divul_valor_iqr\n",
    "\n",
    "expressao_divul_outliers = \"(TOTAL_NULO >= @upper_divul_nulo) | (VALOR_REEMBOLSADO >= @upper_divul_valor)\"\n",
    "\n",
    "\n",
    "df_divul_investigacao_final_outliers = df_divul_investigacao_final.query(expressao_divul_outliers)\n",
    "\n",
    "df_divul_investigacao_final_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c78966",
   "metadata": {},
   "source": [
    "Parlamentares a serem investigados:\n",
    "\n",
    "    Valdir Raupp;\n",
    "    Ivo Cassol;\n",
    "    Mozarildo Cavalcanti;\n",
    "    José Maranhão;\n",
    "    Waldemir Moka;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce9ea89",
   "metadata": {},
   "source": [
    "### Parlamentar Exemplo da Categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "70bbb18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VALOR_REEMBOLSADO    27.0\n",
       "TOTAL_NULO            0.0\n",
       "Name: FABIANO CONTARATO, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divul_investigacao_final.loc[\"FABIANO CONTARATO\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd6e272",
   "metadata": {},
   "source": [
    "Fabiano Contarato é parlamentar que menos gastou na categoria mas também se destaca por não possuir dados faltantes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b39e462",
   "metadata": {},
   "source": [
    "# Análise Parlamentar Especifico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bffc0801",
   "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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.016173</td>\n",
       "      <td>6.648248</td>\n",
       "      <td>1266.893275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.402622</td>\n",
       "      <td>3.305660</td>\n",
       "      <td>1671.761572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>271.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>580.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1651.935000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10007.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ANO         MES  VALOR_REEMBOLSADO\n",
       "count   742.000000  742.000000         742.000000\n",
       "mean   2017.016173    6.648248        1266.893275\n",
       "std       1.402622    3.305660        1671.761572\n",
       "min    2015.000000    1.000000          50.000000\n",
       "25%    2016.000000    4.000000         271.480000\n",
       "50%    2017.000000    6.000000         580.480000\n",
       "75%    2018.000000   10.000000        1651.935000\n",
       "max    2019.000000   12.000000       10007.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_otto_alencar = df_cota_parlamentar[df_cota_parlamentar[\"SENADOR\"].isin([\"OTTO ALENCAR\"])].sort_values(\"VALOR_REEMBOLSADO\", ascending=False)\n",
    "df_otto_alencar.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c073a903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Reembolsos por semestre do Senador Otto Alencar,\\n entre 2015 a 2019'),\n",
       " Text(0, 0.5, 'Valor reembolsado'),\n",
       " Text(0.5, 0, 'Semestres')]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg_fun ={\n",
    "    'VALOR_REEMBOLSADO': 'sum'\n",
    "}\n",
    "df_otto_alencar_ano = df_otto_alencar.groupby('ANO', as_index=False).agg(agg_fun)\n",
    "sns.lineplot(x='ANO', y='VALOR_REEMBOLSADO', data = df_otto_alencar_ano).set(title=\"Reembolsos por semestre do Senador Otto Alencar,\\n entre 2015 a 2019\",\n",
    "                                                                            ylabel=\"Valor reembolsado\", xlabel=\"Semestres\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bee68a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico do total de reembolsos do senador por categoria, não possui gastos com segurança\n",
    "df_otto_alencar_despesas_por_tipo = df_otto_alencar.groupby([\"TIPO_DESPESA\"])[\"VALOR_REEMBOLSADO\"].sum().sort_values(ascending=True)\n",
    "df_otto_alencar_despesas_por_tipo.plot(kind=\"bar\", title=\"Valor reembolsado por tipo de despesa do Senador Otto Alencar, \\n entre 2015 a 2019, segundo dados públicos\")\n",
    "plt.xlabel(\"Tipo de despesas\")\n",
    "plt.ylabel(\"Valor reembolsado\")\n",
    "plt.xticks([0, 1, 2, 3, 4, 5], [\"Escritório\",\n",
    "                                  \"Divulgação\",\n",
    "                                  \"Consultorias\",\n",
    "                                  \"Passagens\",\n",
    "                                  \"Imóveis\",\n",
    "                                  \"Locomoção\"],\n",
    "          rotation=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14dd6ed7",
   "metadata": {},
   "source": [
    "Distribuição de reembolsos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6becbfea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Valor Reembolsado'),\n",
       " Text(0, 0.5, 'Quantidade de Reembolsos'),\n",
       " Text(0.5, 1.0, 'Distribuição de reembolsos por ticket do Senador Otto Alencar,\\n entre 2015 a 2019, segundo dados públicos')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição \n",
    "sns.histplot(df_otto_alencar[\"VALOR_REEMBOLSADO\"], kde=True).set(xlabel=\"Valor Reembolsado\", ylabel=\"Quantidade de Reembolsos\", title=\"Distribuição de reembolsos por ticket do Senador Otto Alencar,\\n entre 2015 a 2019, segundo dados públicos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "151ca951",
   "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>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>SENADOR</th>\n",
       "      <th>TIPO_DESPESA</th>\n",
       "      <th>CNPJ_CPF</th>\n",
       "      <th>FORNECEDOR</th>\n",
       "      <th>DOCUMENTO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>DETALHAMENTO</th>\n",
       "      <th>VALOR_REEMBOLSADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160882</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Contratação de consultorias, assessorias, pesq...</td>\n",
       "      <td>14.780.753/0001-71</td>\n",
       "      <td>ENGENHONOVO COMUNICAÇÃO LTDA</td>\n",
       "      <td>8604</td>\n",
       "      <td>11/12/2015</td>\n",
       "      <td>Projeto gráfico de criação e finalização de re...</td>\n",
       "      <td>10007.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261964</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>00.389.481/0001-79</td>\n",
       "      <td>LM Transportes Interestaduais e Comércio Ltda</td>\n",
       "      <td>00014376</td>\n",
       "      <td>21/01/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7004.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238667</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>00.389.481/0001-79</td>\n",
       "      <td>LM Transportes Interestaduais e Comércio Ltda</td>\n",
       "      <td>14117</td>\n",
       "      <td>13/12/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7004.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238652</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>00.389.481/0001-79</td>\n",
       "      <td>LM Transportes Interestaduais e Comércio Ltda</td>\n",
       "      <td>13902</td>\n",
       "      <td>20/11/2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7004.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261976</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Locomoção, hospedagem, alimentação, combustíve...</td>\n",
       "      <td>00.389.481/0001-79</td>\n",
       "      <td>LM Transportes Interestaduais e Comércio Ltda</td>\n",
       "      <td>00014631</td>\n",
       "      <td>18/02/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7004.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213258</th>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Aluguel de imóveis para escritório político, c...</td>\n",
       "      <td>055.095.218-72</td>\n",
       "      <td>David Granja</td>\n",
       "      <td>RECIBO 07/2017</td>\n",
       "      <td>03/07/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2538.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186267</th>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Aluguel de imóveis para escritório político, c...</td>\n",
       "      <td>055.095.218-72</td>\n",
       "      <td>David Granja</td>\n",
       "      <td>11</td>\n",
       "      <td>10/11/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2538.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213269</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Aluguel de imóveis para escritório político, c...</td>\n",
       "      <td>055.095.218-72</td>\n",
       "      <td>David Granja</td>\n",
       "      <td>RECIBO 08/2017</td>\n",
       "      <td>31/07/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2538.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213303</th>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Aluguel de imóveis para escritório político, c...</td>\n",
       "      <td>055.095.218-72</td>\n",
       "      <td>David Granja</td>\n",
       "      <td>RECIBO - 10</td>\n",
       "      <td>05/10/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2538.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262038</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>OTTO ALENCAR</td>\n",
       "      <td>Aluguel de imóveis para escritório político, c...</td>\n",
       "      <td>055.095.218-72</td>\n",
       "      <td>David Granja</td>\n",
       "      <td>recibi - 06/2019</td>\n",
       "      <td>03/06/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2538.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ANO  MES       SENADOR  \\\n",
       "160882  2015   12  OTTO ALENCAR   \n",
       "261964  2019    1  OTTO ALENCAR   \n",
       "238667  2018   12  OTTO ALENCAR   \n",
       "238652  2018   11  OTTO ALENCAR   \n",
       "261976  2019    2  OTTO ALENCAR   \n",
       "...      ...  ...           ...   \n",
       "213258  2017    7  OTTO ALENCAR   \n",
       "186267  2016   11  OTTO ALENCAR   \n",
       "213269  2017    8  OTTO ALENCAR   \n",
       "213303  2017   10  OTTO ALENCAR   \n",
       "262038  2019    6  OTTO ALENCAR   \n",
       "\n",
       "                                             TIPO_DESPESA            CNPJ_CPF  \\\n",
       "160882  Contratação de consultorias, assessorias, pesq...  14.780.753/0001-71   \n",
       "261964  Locomoção, hospedagem, alimentação, combustíve...  00.389.481/0001-79   \n",
       "238667  Locomoção, hospedagem, alimentação, combustíve...  00.389.481/0001-79   \n",
       "238652  Locomoção, hospedagem, alimentação, combustíve...  00.389.481/0001-79   \n",
       "261976  Locomoção, hospedagem, alimentação, combustíve...  00.389.481/0001-79   \n",
       "...                                                   ...                 ...   \n",
       "213258  Aluguel de imóveis para escritório político, c...      055.095.218-72   \n",
       "186267  Aluguel de imóveis para escritório político, c...      055.095.218-72   \n",
       "213269  Aluguel de imóveis para escritório político, c...      055.095.218-72   \n",
       "213303  Aluguel de imóveis para escritório político, c...      055.095.218-72   \n",
       "262038  Aluguel de imóveis para escritório político, c...      055.095.218-72   \n",
       "\n",
       "                                           FORNECEDOR         DOCUMENTO  \\\n",
       "160882                   ENGENHONOVO COMUNICAÇÃO LTDA              8604   \n",
       "261964  LM Transportes Interestaduais e Comércio Ltda          00014376   \n",
       "238667  LM Transportes Interestaduais e Comércio Ltda             14117   \n",
       "238652  LM Transportes Interestaduais e Comércio Ltda             13902   \n",
       "261976  LM Transportes Interestaduais e Comércio Ltda          00014631   \n",
       "...                                               ...               ...   \n",
       "213258                                   David Granja    RECIBO 07/2017   \n",
       "186267                                   David Granja                11   \n",
       "213269                                   David Granja    RECIBO 08/2017   \n",
       "213303                                   David Granja       RECIBO - 10   \n",
       "262038                                   David Granja  recibi - 06/2019   \n",
       "\n",
       "              DATA                                       DETALHAMENTO  \\\n",
       "160882  11/12/2015  Projeto gráfico de criação e finalização de re...   \n",
       "261964  21/01/2019                                                NaN   \n",
       "238667  13/12/2018                                                NaN   \n",
       "238652  20/11/2018                                                NaN   \n",
       "261976  18/02/2019                                                NaN   \n",
       "...            ...                                                ...   \n",
       "213258  03/07/2017                                                NaN   \n",
       "186267  10/11/2016                                                NaN   \n",
       "213269  31/07/2017                                                NaN   \n",
       "213303  05/10/2017                                                NaN   \n",
       "262038  03/06/2019                                                NaN   \n",
       "\n",
       "        VALOR_REEMBOLSADO  \n",
       "160882           10007.00  \n",
       "261964            7004.37  \n",
       "238667            7004.37  \n",
       "238652            7004.37  \n",
       "261976            7004.37  \n",
       "...                   ...  \n",
       "213258            2538.00  \n",
       "186267            2538.00  \n",
       "213269            2538.00  \n",
       "213303            2538.00  \n",
       "262038            2538.00  \n",
       "\n",
       "[100 rows x 10 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_otto_alencar_outliers = df_otto_alencar.nlargest(100, \"VALOR_REEMBOLSADO\").sort_values(\"VALOR_REEMBOLSADO\", ascending=False)\n",
    "df_otto_alencar_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6fdc99e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de quantidade de dados faltantes do parlamentar.\n",
    "df_otto_alencar[[\"TIPO_DESPESA\", \"CNPJ_CPF\", \"FORNECEDOR\", \"DOCUMENTO\", \"DATA\", \"DETALHAMENTO\"]].isna().sum().plot(kind=\"bar\")\n",
    "plt.xlabel(\"Tipo de dado\")\n",
    "plt.ylabel(\"Quantidade de faltantes\")\n",
    "plt.xticks([0, 1, 2, 3, 4, 5], [\"Categoria\",\n",
    "                                           \"CPF/CNPJ\",\n",
    "                                           \"Fornecedor\",\n",
    "                                           \"Documento\",\n",
    "                                           \"Data\",\n",
    "                                           \"Detalhamento\",],\n",
    "          rotation=30)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e561f5a",
   "metadata": {},
   "source": [
    "Em comparação aos demais senadores, Otto Alencar apresenta um ticket médio de reembolso abaixo da média. Os maiores valores de reembolso são registrados na seguinte categoria: locomoção, hospedagem, alimentação, combustíveis e lubrificantes. Contudo, há uma quantidade significativa de dados faltantes na coluna detalhamento."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}