Python Nordeste Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Data Analisys and Visualization\n",
    "\n",
    "\n",
    "## O que veremos?\n",
    "* Dados?\n",
    "    * onde conseguir\n",
    "    * como importar\n",
    "    \n",
    "* Limpando, manipulando e validando\n",
    "* Entendendo os dados\n",
    "* Respondendo as perguntas que temos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dados?\n",
    "\n",
    "Neste curso usaremos dados publicos providos pela [Operação Serenata de Amor](https://serenatadeamor.org/), que captura e processa os dados de senadores e deputados.\n",
    "\n",
    "Você pode ver o ultimo relatorio dessa operação [aqui](https://medium.com/data-science-brigade/relat%C3%B3rio-mensal-da-opera%C3%A7%C3%A3o-serenata-de-amor-008-88c40c0930cd).\n",
    "\n",
    "Para usar os dados do serenata você pode instalar o [serenata-toolbox](https://github.com/datasciencebr/serenata-toolbox). Para instalar você pode fazer:\n",
    "\n",
    "    $ pip install git+https://github.com/datasciencebr/serenata-toolbox.git#egg=serenata-toolbox\n",
    "\n",
    "Depois podemos baixar os dados de reembolso dos nossos senadores assim:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from serenata_toolbox.datasets import Datasets\n",
    "from serenata_toolbox.federal_senate.federal_senate_dataset import FederalSenateDataset\n",
    "dataset_dir = '.'\n",
    "\n",
    "senate = FederalSenateDataset(dataset_dir)\n",
    "senate.fetch()\n",
    "senate.translate()\n",
    "senate.clean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ele vai baixar um arquivo compactado com os dados do senado desde 2009, mas como isso demora um pouco, nós vamos usar o arquivo **federal-senate-2016** que eu ja disponibilizei aqui."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>congressperson_name</th>\n",
       "      <th>expense_type</th>\n",
       "      <th>cnpj_cpf</th>\n",
       "      <th>supplier</th>\n",
       "      <th>document_id</th>\n",
       "      <th>date</th>\n",
       "      <th>expense_details</th>\n",
       "      <th>reimbursement_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Rent of real estate for political office, comp...</td>\n",
       "      <td>05.914.650/0001-66</td>\n",
       "      <td>ELETROBRÁS DISTRIBUIÇÃO RONDÔNIA</td>\n",
       "      <td>000011328</td>\n",
       "      <td>13/01/2016</td>\n",
       "      <td>Despesa com pagamento de energia elétrica para...</td>\n",
       "      <td>73,6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Rent of real estate for political office, comp...</td>\n",
       "      <td>05.914.650/0001-66</td>\n",
       "      <td>ELETROBRÁS DISTRIBUIÇÃO RONDÔNIA</td>\n",
       "      <td>000011327</td>\n",
       "      <td>18/01/2016</td>\n",
       "      <td>Pagamento de energia elétrica para o escritóri...</td>\n",
       "      <td>206,84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Rent of real estate for political office, comp...</td>\n",
       "      <td>004.948.028-63</td>\n",
       "      <td>GILBERTO PISELO DO NASCIMENTO</td>\n",
       "      <td>001/16</td>\n",
       "      <td>04/01/2016</td>\n",
       "      <td>Despesa com pagamento de aluguel, utilizado pa...</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Rent of real estate for political office, comp...</td>\n",
       "      <td>05.423.963/0001-11</td>\n",
       "      <td>OI MÓVEL S.A.</td>\n",
       "      <td>661639001</td>\n",
       "      <td>16/01/2016</td>\n",
       "      <td>despesa com pagamento de telefonia para o escr...</td>\n",
       "      <td>407,48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Acquisition of consumables for use in the poli...</td>\n",
       "      <td>62.652.961/0001-38</td>\n",
       "      <td>AGÊNCIA ESTADO S.A.</td>\n",
       "      <td>00608918</td>\n",
       "      <td>06/01/2016</td>\n",
       "      <td>Aquisição de publicação virtual de conteúdo jo...</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month congressperson_name  \\\n",
       "0  2016      1        ACIR GURGACZ   \n",
       "1  2016      1        ACIR GURGACZ   \n",
       "2  2016      1        ACIR GURGACZ   \n",
       "3  2016      1        ACIR GURGACZ   \n",
       "4  2016      1        ACIR GURGACZ   \n",
       "\n",
       "                                        expense_type            cnpj_cpf  \\\n",
       "0  Rent of real estate for political office, comp...  05.914.650/0001-66   \n",
       "1  Rent of real estate for political office, comp...  05.914.650/0001-66   \n",
       "2  Rent of real estate for political office, comp...      004.948.028-63   \n",
       "3  Rent of real estate for political office, comp...  05.423.963/0001-11   \n",
       "4  Acquisition of consumables for use in the poli...  62.652.961/0001-38   \n",
       "\n",
       "                           supplier document_id        date  \\\n",
       "0  ELETROBRÁS DISTRIBUIÇÃO RONDÔNIA   000011328  13/01/2016   \n",
       "1  ELETROBRÁS DISTRIBUIÇÃO RONDÔNIA   000011327  18/01/2016   \n",
       "2     GILBERTO PISELO DO NASCIMENTO      001/16  04/01/2016   \n",
       "3                     OI MÓVEL S.A.   661639001  16/01/2016   \n",
       "4               AGÊNCIA ESTADO S.A.    00608918  06/01/2016   \n",
       "\n",
       "                                     expense_details reimbursement_value  \n",
       "0  Despesa com pagamento de energia elétrica para...                73,6  \n",
       "1  Pagamento de energia elétrica para o escritóri...              206,84  \n",
       "2  Despesa com pagamento de aluguel, utilizado pa...                6000  \n",
       "3  despesa com pagamento de telefonia para o escr...              407,48  \n",
       "4  Aquisição de publicação virtual de conteúdo jo...                 550  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv(\"/dados/federal-senate-2016\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com pandas podemos importar arquivos de varios formatos e transforma-los em Data Frames, que são um formato de dados tabular com linhas e colunas indexaveis.\n",
    "\n",
    "-----------------------------------\n",
    "\n",
    "### lendo html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Club</th>\n",
       "      <th>Position in 2016</th>\n",
       "      <th>First season in  top division</th>\n",
       "      <th>Seasons in Taça  Brasil</th>\n",
       "      <th>Seasons in Robertão</th>\n",
       "      <th>Seasons in Brasileirão</th>\n",
       "      <th>Top  division titles</th>\n",
       "      <th>Last top division  title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlético Goianiense</td>\n",
       "      <td>1st in  Série B</td>\n",
       "      <td>1965</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Atlético Mineiro</td>\n",
       "      <td>4th</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1971.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atlético Paranaense</td>\n",
       "      <td>6th</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avaí</td>\n",
       "      <td>2nd in Série B</td>\n",
       "      <td>1974</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>4th in Série B</td>\n",
       "      <td>1962</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Club Position in 2016  First season in  top division  \\\n",
       "0  Atlético Goianiense  1st in  Série B                           1965   \n",
       "1     Atlético Mineiro              4th                           1959   \n",
       "2  Atlético Paranaense              6th                           1959   \n",
       "3                 Avaí   2nd in Série B                           1974   \n",
       "4                Bahia   4th in Série B                           1962   \n",
       "\n",
       "   Seasons in Taça  Brasil  Seasons in Robertão  Seasons in Brasileirão  \\\n",
       "0                        2                    0                       8   \n",
       "1                        4                    4                      46   \n",
       "2                        1                    2                      38   \n",
       "3                        0                    0                       9   \n",
       "4                        6                    3                      36   \n",
       "\n",
       "   Top  division titles  Last top division  title  \n",
       "0                     0                       NaN  \n",
       "1                     1                    1971.0  \n",
       "2                     1                    2001.0  \n",
       "3                     0                       NaN  \n",
       "4                     2                    1988.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brasileirao = pandas.read_html(\"brasileirao2016.html\", encoding=\"utf-8\")[0]\n",
    "brasileirao.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------\n",
    "\n",
    "Depois de importarmos os dados, vamos conhece-lo melhor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26691, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (linhas, colunas)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'month', 'congressperson_name', 'expense_type', 'cnpj_cpf',\n",
       "       'supplier', 'document_id', 'date', 'expense_details',\n",
       "       'reimbursement_value'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                    int64\n",
       "month                   int64\n",
       "congressperson_name    object\n",
       "expense_type           object\n",
       "cnpj_cpf               object\n",
       "supplier               object\n",
       "document_id            object\n",
       "date                   object\n",
       "expense_details        object\n",
       "reimbursement_value    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tipo de cada coluna\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>congressperson_name</th>\n",
       "      <th>expense_type</th>\n",
       "      <th>cnpj_cpf</th>\n",
       "      <th>supplier</th>\n",
       "      <th>document_id</th>\n",
       "      <th>date</th>\n",
       "      <th>expense_details</th>\n",
       "      <th>reimbursement_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Rent of real estate for political office, comp...</td>\n",
       "      <td>05.914.650/0001-66</td>\n",
       "      <td>ELETROBRÁS DISTRIBUIÇÃO RONDÔNIA</td>\n",
       "      <td>000011328</td>\n",
       "      <td>13/01/2016</td>\n",
       "      <td>Despesa com pagamento de energia elétrica para...</td>\n",
       "      <td>73,6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Rent of real estate for political office, comp...</td>\n",
       "      <td>05.914.650/0001-66</td>\n",
       "      <td>ELETROBRÁS DISTRIBUIÇÃO RONDÔNIA</td>\n",
       "      <td>000011327</td>\n",
       "      <td>18/01/2016</td>\n",
       "      <td>Pagamento de energia elétrica para o escritóri...</td>\n",
       "      <td>206,84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Rent of real estate for political office, comp...</td>\n",
       "      <td>004.948.028-63</td>\n",
       "      <td>GILBERTO PISELO DO NASCIMENTO</td>\n",
       "      <td>001/16</td>\n",
       "      <td>04/01/2016</td>\n",
       "      <td>Despesa com pagamento de aluguel, utilizado pa...</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Rent of real estate for political office, comp...</td>\n",
       "      <td>05.423.963/0001-11</td>\n",
       "      <td>OI MÓVEL S.A.</td>\n",
       "      <td>661639001</td>\n",
       "      <td>16/01/2016</td>\n",
       "      <td>despesa com pagamento de telefonia para o escr...</td>\n",
       "      <td>407,48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>Acquisition of consumables for use in the poli...</td>\n",
       "      <td>62.652.961/0001-38</td>\n",
       "      <td>AGÊNCIA ESTADO S.A.</td>\n",
       "      <td>00608918</td>\n",
       "      <td>06/01/2016</td>\n",
       "      <td>Aquisição de publicação virtual de conteúdo jo...</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month congressperson_name  \\\n",
       "0  2016      1        ACIR GURGACZ   \n",
       "1  2016      1        ACIR GURGACZ   \n",
       "2  2016      1        ACIR GURGACZ   \n",
       "3  2016      1        ACIR GURGACZ   \n",
       "4  2016      1        ACIR GURGACZ   \n",
       "\n",
       "                                        expense_type            cnpj_cpf  \\\n",
       "0  Rent of real estate for political office, comp...  05.914.650/0001-66   \n",
       "1  Rent of real estate for political office, comp...  05.914.650/0001-66   \n",
       "2  Rent of real estate for political office, comp...      004.948.028-63   \n",
       "3  Rent of real estate for political office, comp...  05.423.963/0001-11   \n",
       "4  Acquisition of consumables for use in the poli...  62.652.961/0001-38   \n",
       "\n",
       "                           supplier document_id        date  \\\n",
       "0  ELETROBRÁS DISTRIBUIÇÃO RONDÔNIA   000011328  13/01/2016   \n",
       "1  ELETROBRÁS DISTRIBUIÇÃO RONDÔNIA   000011327  18/01/2016   \n",
       "2     GILBERTO PISELO DO NASCIMENTO      001/16  04/01/2016   \n",
       "3                     OI MÓVEL S.A.   661639001  16/01/2016   \n",
       "4               AGÊNCIA ESTADO S.A.    00608918  06/01/2016   \n",
       "\n",
       "                                     expense_details reimbursement_value  \n",
       "0  Despesa com pagamento de energia elétrica para...                73,6  \n",
       "1  Pagamento de energia elétrica para o escritóri...              206,84  \n",
       "2  Despesa com pagamento de aluguel, utilizado pa...                6000  \n",
       "3  despesa com pagamento de telefonia para o escr...              407,48  \n",
       "4  Aquisição de publicação virtual de conteúdo jo...                 550  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# os primeiros 5 elementos do Data Frame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year     2016.000000\n",
       "month       6.470908\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------\n",
    "Opa, temos um problema. Não foi gerado media para a coluna de `reimbursement_value`, porque ela não é está representada como um tipo numerico, e sim como object.\n",
    "\n",
    "Teremos que transforma-la em uma coluna tipo `float64`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.reimbursement_value = df.reimbursement_value.str.replace(\",\", \".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.reimbursement_value = df.reimbursement_value.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      73.60\n",
       "1     206.84\n",
       "2    6000.00\n",
       "3     407.48\n",
       "4     550.00\n",
       "Name: reimbursement_value, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------\n",
    "Pronto, agora vamos verificar se deu certo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                   2016.000000\n",
       "month                     6.470908\n",
       "reimbursement_value     947.484513\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------\n",
    "As medias das colunas `month` e `year` não importam pra gente, então vamos olhar só pra coluna de reembolso.\n",
    "Com pandas podemos pegar varias estatisticas que descrevem uma distribuição."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "947.48451313176724"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "324.79"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2733.8200368509724"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7473771.9938878519"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256980.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------\n",
    "### estatisticas descritivas\n",
    "São metricas que nos ajudam a entender uma distribuição de valores, nesse caso os dados de reembolso dos nossos senadores.\n",
    "\n",
    "- Media: soma dos valores / quantidade de valores\n",
    "- Mediana: valor que divide a distribuição ao meio. Tambem pode ser referenciada como percentil 50.\n",
    "- Moda: valor mais frequente da distribuição\n",
    "- Desvio Padrão: valor mais comum de variação da media\n",
    "- Variancia: mostra a dispersão quadratica da distribuição. Quanto maior mais dispersa é a distribuição.\n",
    "- Max: valor mais alto da distribuição\n",
    "- Min: valor mais baixo da distribuição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     26691.000000\n",
       "mean        947.484513\n",
       "std        2733.820037\n",
       "min           0.010000\n",
       "25%         124.000000\n",
       "50%         324.790000\n",
       "75%         956.865000\n",
       "max      256980.000000\n",
       "Name: reimbursement_value, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# podemos ver essas metricas juntas\n",
    "df.reimbursement_value.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------\n",
    "Outra parte importante da analise é saber se seus dados estão completos e, se incompletos, podem ser usados.\n",
    "No caso desses dados, temos alguns dados faltando mas isso não vai impactar na analise que faremos aqui."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26691 entries, 0 to 26690\n",
      "Data columns (total 10 columns):\n",
      "year                   26691 non-null int64\n",
      "month                  26691 non-null int64\n",
      "congressperson_name    26691 non-null object\n",
      "expense_type           26691 non-null object\n",
      "cnpj_cpf               26691 non-null object\n",
      "supplier               26691 non-null object\n",
      "document_id            24647 non-null object\n",
      "date                   26691 non-null object\n",
      "expense_details        21308 non-null object\n",
      "reimbursement_value    26691 non-null float64\n",
      "dtypes: float64(1), int64(2), object(7)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# quantos dados não estão nulos no seu DataFrame.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------\n",
    "Quem pediu o maior reembolso e quem pediu o menor?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                                                        2016\n",
       "month                                                         12\n",
       "congressperson_name                              DAVI ALCOLUMBRE\n",
       "expense_type                 Publicity of parliamentary activity\n",
       "cnpj_cpf                                      04.953.008/0001-23\n",
       "supplier               ARTE & IMAGEM GRAFICA E EDITORA EIRELI ME\n",
       "document_id                                                  202\n",
       "date                                                  22/03/2017\n",
       "expense_details                                              NaN\n",
       "reimbursement_value                                       256980\n",
       "Name: 4637, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.reimbursement_value.idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                                                                2016\n",
       "month                                                                  3\n",
       "congressperson_name                              FERNANDO BEZERRA COELHO\n",
       "expense_type                      National air, water and land transport\n",
       "cnpj_cpf                                              07.575.651/0001-59\n",
       "supplier                                                             GOL\n",
       "document_id                                                       NFPJ4Z\n",
       "date                                                          22/03/2016\n",
       "expense_details        Companhia Aérea: GOL, Localizador: NFPJ4Z. Pas...\n",
       "reimbursement_value                                                 0.01\n",
       "Name: 6847, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.reimbursement_value.idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "\n",
    "## Conhecendo mais dos dados\n",
    "\n",
    "Estes numeros sozinhos não dizem muita coisa, por isso vamos plotar alguns graficos para nos ajudar a entender melhor o que eles representam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "# essa linha serve para exibir os graficos direto no jupyter notebook\n",
    "% matplotlib inline\n",
    "matplotlib.style.use('ggplot') # graficos mais bonitos com ggplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb5782f5a90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HWXyhMLvoooty1VVXJUkeeuihPPjgg1m0aNFXurB96evrS19f3+jnO3bsGPPHPFwc6tdq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\nhBkAQBHCDACgCGEGAFDE/wN7JShxe6j5vgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb578535668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.reimbursement_value.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb57835e8d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OHwBxqBcAYBzhB7iN+/gBAHyC8AMAAAgIwg/wChN+AADDCD/AbdzHDwDgE4QfAABAQBB+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\nBQVxrmXH18jpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5782e3710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.reimbursement_value.plot(kind=\"density\", figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Não ajudou muito pois nossa distribuição é muito esparsa. Podemos pegar um `sample` da distribuição para tentar entende-la melhor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56ba603c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KAKBATAEAFIgpAIACMQUAUCCmAAAKxBQAQIGYAgAoEFMAAAViCgCgQEwBABSIKQCAAjEF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\nAAAKxBQAQIGYAgAo+L86H4Y6fXUEAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56ba6a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sample(100).reimbursement_value.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melhorou bastante, mas ainda podemos melhorar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56bb22b70>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56ba5dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bin é uma coluna do histograma que tem um range de valor. Quanto mais bins, menor esse range.\n",
    "df.sample(100).reimbursement_value.hist(figsize=(10,10), bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------\n",
    "Vimos que os valores são bem dispersos, mas podemos fazer analises especificas.\n",
    "\n",
    "Vamos ver como os gastos estão distribuidos por mês"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56bb3a6a0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g+AEAAFiC4gcAAGAJih8AAIAlKH4AAACWoPgBAABYguIHAABgCYofAACAJSh+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56bb22cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column=\"reimbursement_value\", by=\"month\", figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------\n",
    "Os graficos dessa distribuição não nos ajudam muito, pois estão muito dispersos, mas podemos aplicar uma transformação para que os dados sejam mais faceis de visualizar.\n",
    "\n",
    "Podemos aplicar diversas transformações, e nesse caso vamos usar raiz quadrada pois nao queremos dados negativos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "from matplotlib import pyplot\n",
    "df_sqrt = df.copy()\n",
    "df_sqrt.reimbursement_value = df.reimbursement_value.apply(numpy.sqrt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7fb56b64ff60>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QmAADAChMgAEAFCbAAAAKE2AAAIUJMACAwgQYAEBhAgwAoDABBgBQmAADAChMgAEAFCbA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\nfWSnAfUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56b726d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sqrt.reimbursement_value.hist(figsize=(10,10), bins=100)\n",
    "pyplot.vlines(df_sqrt.mean(), # Plot black line at mean\n",
    "              ymin=0,\n",
    "              ymax=6000,\n",
    "              color=\"purple\")\n",
    "\n",
    "pyplot.vlines(df_sqrt.median(), # Plot red line at median\n",
    "            ymin=0,\n",
    "            ymax=6000,\n",
    "            color=\"yellow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56b657c88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I+nUkEtGpU6dmPSYYDKqyslLDw8Oqra3VqVOn9MILL+jChQvasWOHgsFgRtec1NXVpa6u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\nHn1JZiICAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56b6b75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sqrt.reimbursement_value.plot(kind=\"density\", figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56afe6e48>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UqFEjzZkzRwMGDNDy5ctrPzUAAMBlolplLT8/Xx999JH69OkjSbIsS59++qni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\nxmKNiIiIyMBYrBEREREZ2P8D+at49JSRXusAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56af78208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sqrt.boxplot(column=\"reimbursement_value\", by=\"month\", figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos notar que muitos valores altos, fugindo do padrão da distribuição, chamamos esses valores de outliers.\n",
    "\n",
    "Outliers mexem com a media e outras estatisticas, as vezes até podem atrapalhar a avaliação dos dados.\n",
    "\n",
    "----------------\n",
    "\n",
    "Vamos ver os reembolsos agrupados pelos meses e senadores.\n",
    "\n",
    "Assim conseguimos dizer quem gastou mais num ano e num mês."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "bymonth = df.groupby([\"month\"])\n",
    "bysenator = df.groupby([\"congressperson_name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></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>1</th>\n",
       "      <td>1550.0</td>\n",
       "      <td>943.931329</td>\n",
       "      <td>1963.071017</td>\n",
       "      <td>1.70</td>\n",
       "      <td>111.1200</td>\n",
       "      <td>268.510</td>\n",
       "      <td>908.3150</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2127.0</td>\n",
       "      <td>929.316794</td>\n",
       "      <td>2041.279175</td>\n",
       "      <td>0.30</td>\n",
       "      <td>130.0000</td>\n",
       "      <td>329.090</td>\n",
       "      <td>839.9050</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2546.0</td>\n",
       "      <td>893.277565</td>\n",
       "      <td>1818.623741</td>\n",
       "      <td>0.01</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>345.820</td>\n",
       "      <td>900.0000</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2367.0</td>\n",
       "      <td>887.507533</td>\n",
       "      <td>1871.631119</td>\n",
       "      <td>0.37</td>\n",
       "      <td>116.8850</td>\n",
       "      <td>305.630</td>\n",
       "      <td>935.9450</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2422.0</td>\n",
       "      <td>885.057981</td>\n",
       "      <td>2031.555902</td>\n",
       "      <td>3.00</td>\n",
       "      <td>115.0325</td>\n",
       "      <td>317.645</td>\n",
       "      <td>876.2500</td>\n",
       "      <td>44500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2562.0</td>\n",
       "      <td>862.780254</td>\n",
       "      <td>1816.950951</td>\n",
       "      <td>0.01</td>\n",
       "      <td>120.6800</td>\n",
       "      <td>300.695</td>\n",
       "      <td>856.2475</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2360.0</td>\n",
       "      <td>888.361089</td>\n",
       "      <td>1725.941843</td>\n",
       "      <td>3.60</td>\n",
       "      <td>123.8875</td>\n",
       "      <td>300.000</td>\n",
       "      <td>901.4800</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2462.0</td>\n",
       "      <td>904.189886</td>\n",
       "      <td>1888.202808</td>\n",
       "      <td>2.40</td>\n",
       "      <td>119.3250</td>\n",
       "      <td>321.450</td>\n",
       "      <td>947.7100</td>\n",
       "      <td>27655.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2343.0</td>\n",
       "      <td>890.552548</td>\n",
       "      <td>1927.864138</td>\n",
       "      <td>2.40</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>273.400</td>\n",
       "      <td>927.2800</td>\n",
       "      <td>40000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2181.0</td>\n",
       "      <td>926.244191</td>\n",
       "      <td>1732.743468</td>\n",
       "      <td>0.90</td>\n",
       "      <td>137.8400</td>\n",
       "      <td>356.680</td>\n",
       "      <td>1069.4700</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2042.0</td>\n",
       "      <td>998.678918</td>\n",
       "      <td>2589.771275</td>\n",
       "      <td>0.49</td>\n",
       "      <td>134.1525</td>\n",
       "      <td>409.435</td>\n",
       "      <td>1079.6175</td>\n",
       "      <td>78000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1729.0</td>\n",
       "      <td>1533.740364</td>\n",
       "      <td>7761.248515</td>\n",
       "      <td>0.01</td>\n",
       "      <td>142.7500</td>\n",
       "      <td>398.000</td>\n",
       "      <td>1125.6900</td>\n",
       "      <td>256980.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count         mean          std   min       25%      50%        75%  \\\n",
       "month                                                                         \n",
       "1      1550.0   943.931329  1963.071017  1.70  111.1200  268.510   908.3150   \n",
       "2      2127.0   929.316794  2041.279175  0.30  130.0000  329.090   839.9050   \n",
       "3      2546.0   893.277565  1818.623741  0.01  120.0000  345.820   900.0000   \n",
       "4      2367.0   887.507533  1871.631119  0.37  116.8850  305.630   935.9450   \n",
       "5      2422.0   885.057981  2031.555902  3.00  115.0325  317.645   876.2500   \n",
       "6      2562.0   862.780254  1816.950951  0.01  120.6800  300.695   856.2475   \n",
       "7      2360.0   888.361089  1725.941843  3.60  123.8875  300.000   901.4800   \n",
       "8      2462.0   904.189886  1888.202808  2.40  119.3250  321.450   947.7100   \n",
       "9      2343.0   890.552548  1927.864138  2.40  120.0000  273.400   927.2800   \n",
       "10     2181.0   926.244191  1732.743468  0.90  137.8400  356.680  1069.4700   \n",
       "11     2042.0   998.678918  2589.771275  0.49  134.1525  409.435  1079.6175   \n",
       "12     1729.0  1533.740364  7761.248515  0.01  142.7500  398.000  1125.6900   \n",
       "\n",
       "             max  \n",
       "month             \n",
       "1       30000.00  \n",
       "2       30000.00  \n",
       "3       30000.00  \n",
       "4       30000.00  \n",
       "5       44500.00  \n",
       "6       30000.00  \n",
       "7       30000.00  \n",
       "8       27655.07  \n",
       "9       40000.00  \n",
       "10      30000.00  \n",
       "11      78000.00  \n",
       "12     256980.00  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agora sabemos as estatisticas por mês\n",
    "bymonth.reimbursement_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56b037c50>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAChAiAMAQAFCHAAAChDiAABQgBAHAIAChDgAABQgxAEAoAAhDgAABQhxAAAoQIgDAEAB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\nAQCgACEOAAAFCHEAAChAiAMAQAH/HwIUY6Z6SrOEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56b0cca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# valores gastos por mês\n",
    "s = bymonth.reimbursement_value.sum()\n",
    "s.plot.bar(figsize=(12,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>congressperson_name</th>\n",
       "      <th></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>ACIR GURGACZ</th>\n",
       "      <td>140.0</td>\n",
       "      <td>2156.934857</td>\n",
       "      <td>2864.690150</td>\n",
       "      <td>40.00</td>\n",
       "      <td>591.3275</td>\n",
       "      <td>1547.590</td>\n",
       "      <td>2084.8775</td>\n",
       "      <td>19800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALOYSIO NUNES FERREIRA</th>\n",
       "      <td>432.0</td>\n",
       "      <td>661.846296</td>\n",
       "      <td>1357.902019</td>\n",
       "      <td>5.55</td>\n",
       "      <td>104.4000</td>\n",
       "      <td>178.485</td>\n",
       "      <td>777.6025</td>\n",
       "      <td>8266.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALVARO DIAS</th>\n",
       "      <td>150.0</td>\n",
       "      <td>886.473000</td>\n",
       "      <td>435.867230</td>\n",
       "      <td>178.00</td>\n",
       "      <td>541.0450</td>\n",
       "      <td>795.505</td>\n",
       "      <td>1120.8400</td>\n",
       "      <td>2196.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANA AMÉLIA</th>\n",
       "      <td>397.0</td>\n",
       "      <td>420.882695</td>\n",
       "      <td>498.887655</td>\n",
       "      <td>8.00</td>\n",
       "      <td>72.4900</td>\n",
       "      <td>195.000</td>\n",
       "      <td>628.8600</td>\n",
       "      <td>2759.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTONIO ANASTASIA</th>\n",
       "      <td>273.0</td>\n",
       "      <td>1144.545458</td>\n",
       "      <td>1532.358356</td>\n",
       "      <td>22.00</td>\n",
       "      <td>362.9800</td>\n",
       "      <td>633.940</td>\n",
       "      <td>1017.0000</td>\n",
       "      <td>6613.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTÔNIO CARLOS VALADARES</th>\n",
       "      <td>155.0</td>\n",
       "      <td>1713.440452</td>\n",
       "      <td>1650.345721</td>\n",
       "      <td>250.83</td>\n",
       "      <td>577.5350</td>\n",
       "      <td>1045.000</td>\n",
       "      <td>2316.6500</td>\n",
       "      <td>5900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARMANDO MONTEIRO</th>\n",
       "      <td>232.0</td>\n",
       "      <td>675.458707</td>\n",
       "      <td>1027.924855</td>\n",
       "      <td>4.08</td>\n",
       "      <td>169.9225</td>\n",
       "      <td>412.800</td>\n",
       "      <td>812.5225</td>\n",
       "      <td>7420.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATAÍDES OLIVEIRA</th>\n",
       "      <td>175.0</td>\n",
       "      <td>1712.092629</td>\n",
       "      <td>3079.333122</td>\n",
       "      <td>85.00</td>\n",
       "      <td>793.4100</td>\n",
       "      <td>979.400</td>\n",
       "      <td>1500.0000</td>\n",
       "      <td>17500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AÉCIO NEVES</th>\n",
       "      <td>307.0</td>\n",
       "      <td>962.006026</td>\n",
       "      <td>1854.973271</td>\n",
       "      <td>15.00</td>\n",
       "      <td>72.5000</td>\n",
       "      <td>326.600</td>\n",
       "      <td>900.0000</td>\n",
       "      <td>9000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BENEDITO DE LIRA</th>\n",
       "      <td>268.0</td>\n",
       "      <td>645.377910</td>\n",
       "      <td>856.757007</td>\n",
       "      <td>16.73</td>\n",
       "      <td>173.2675</td>\n",
       "      <td>247.790</td>\n",
       "      <td>776.2950</td>\n",
       "      <td>4000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLAIRO MAGGI</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1372.065570</td>\n",
       "      <td>2252.756787</td>\n",
       "      <td>8.80</td>\n",
       "      <td>230.3750</td>\n",
       "      <td>455.630</td>\n",
       "      <td>900.0000</td>\n",
       "      <td>7800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIDINHO SANTOS</th>\n",
       "      <td>156.0</td>\n",
       "      <td>1489.743462</td>\n",
       "      <td>2054.534316</td>\n",
       "      <td>7.55</td>\n",
       "      <td>260.2150</td>\n",
       "      <td>661.970</td>\n",
       "      <td>1505.3800</td>\n",
       "      <td>8000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIRO NOGUEIRA</th>\n",
       "      <td>328.0</td>\n",
       "      <td>1341.609177</td>\n",
       "      <td>1955.232902</td>\n",
       "      <td>45.00</td>\n",
       "      <td>212.2550</td>\n",
       "      <td>605.390</td>\n",
       "      <td>1440.0000</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRISTOVAM BUARQUE</th>\n",
       "      <td>7.0</td>\n",
       "      <td>15371.428571</td>\n",
       "      <td>12115.377871</td>\n",
       "      <td>2100.00</td>\n",
       "      <td>10250.0000</td>\n",
       "      <td>15000.000</td>\n",
       "      <td>15000.0000</td>\n",
       "      <td>40000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CÁSSIO CUNHA LIMA</th>\n",
       "      <td>255.0</td>\n",
       "      <td>668.152549</td>\n",
       "      <td>1016.598787</td>\n",
       "      <td>10.64</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>227.920</td>\n",
       "      <td>852.3650</td>\n",
       "      <td>7500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DALIRIO BEBER</th>\n",
       "      <td>544.0</td>\n",
       "      <td>393.595423</td>\n",
       "      <td>574.972304</td>\n",
       "      <td>3.00</td>\n",
       "      <td>83.7125</td>\n",
       "      <td>158.875</td>\n",
       "      <td>464.1000</td>\n",
       "      <td>3500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAVI ALCOLUMBRE</th>\n",
       "      <td>133.0</td>\n",
       "      <td>3835.158421</td>\n",
       "      <td>22271.242467</td>\n",
       "      <td>170.00</td>\n",
       "      <td>453.7300</td>\n",
       "      <td>842.580</td>\n",
       "      <td>2100.0000</td>\n",
       "      <td>256980.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DECA</th>\n",
       "      <td>128.0</td>\n",
       "      <td>1046.193438</td>\n",
       "      <td>1405.788875</td>\n",
       "      <td>9.00</td>\n",
       "      <td>151.2025</td>\n",
       "      <td>340.075</td>\n",
       "      <td>1752.0100</td>\n",
       "      <td>7125.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DONIZETI NOGUEIRA</th>\n",
       "      <td>182.0</td>\n",
       "      <td>600.361868</td>\n",
       "      <td>1170.831507</td>\n",
       "      <td>25.00</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>144.075</td>\n",
       "      <td>400.5000</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOUGLAS CINTRA</th>\n",
       "      <td>108.0</td>\n",
       "      <td>685.209537</td>\n",
       "      <td>972.108468</td>\n",
       "      <td>22.87</td>\n",
       "      <td>173.8125</td>\n",
       "      <td>348.755</td>\n",
       "      <td>811.8900</td>\n",
       "      <td>5000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DÁRIO BERGER</th>\n",
       "      <td>442.0</td>\n",
       "      <td>723.561290</td>\n",
       "      <td>1038.533209</td>\n",
       "      <td>10.00</td>\n",
       "      <td>179.9950</td>\n",
       "      <td>319.820</td>\n",
       "      <td>732.5250</td>\n",
       "      <td>4850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDISON LOBÃO</th>\n",
       "      <td>46.0</td>\n",
       "      <td>3294.706739</td>\n",
       "      <td>5240.292523</td>\n",
       "      <td>58.43</td>\n",
       "      <td>67.4375</td>\n",
       "      <td>174.740</td>\n",
       "      <td>9602.7450</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO AMORIM</th>\n",
       "      <td>472.0</td>\n",
       "      <td>648.851992</td>\n",
       "      <td>817.621372</td>\n",
       "      <td>40.00</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>272.730</td>\n",
       "      <td>891.7750</td>\n",
       "      <td>3950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO BRAGA</th>\n",
       "      <td>116.0</td>\n",
       "      <td>2529.536379</td>\n",
       "      <td>2790.122521</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1072.8000</td>\n",
       "      <td>1520.605</td>\n",
       "      <td>2688.6500</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO LOPES</th>\n",
       "      <td>133.0</td>\n",
       "      <td>829.245489</td>\n",
       "      <td>2961.260094</td>\n",
       "      <td>40.49</td>\n",
       "      <td>104.2500</td>\n",
       "      <td>193.720</td>\n",
       "      <td>668.6800</td>\n",
       "      <td>33000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELMANO FÉRRER</th>\n",
       "      <td>292.0</td>\n",
       "      <td>1469.045548</td>\n",
       "      <td>1644.973324</td>\n",
       "      <td>5.00</td>\n",
       "      <td>315.1800</td>\n",
       "      <td>823.460</td>\n",
       "      <td>2398.7175</td>\n",
       "      <td>6500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO BEZERRA COELHO</th>\n",
       "      <td>601.0</td>\n",
       "      <td>724.029884</td>\n",
       "      <td>1459.551181</td>\n",
       "      <td>0.01</td>\n",
       "      <td>142.4800</td>\n",
       "      <td>213.110</td>\n",
       "      <td>627.4400</td>\n",
       "      <td>15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>21.0</td>\n",
       "      <td>20022.429048</td>\n",
       "      <td>7545.173689</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>12000.0000</td>\n",
       "      <td>23056.200</td>\n",
       "      <td>27655.0700</td>\n",
       "      <td>28922.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEXA RIBEIRO</th>\n",
       "      <td>341.0</td>\n",
       "      <td>1067.514194</td>\n",
       "      <td>2079.523837</td>\n",
       "      <td>35.00</td>\n",
       "      <td>125.3200</td>\n",
       "      <td>540.000</td>\n",
       "      <td>1117.1900</td>\n",
       "      <td>18000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA BEZERRA</th>\n",
       "      <td>635.0</td>\n",
       "      <td>528.045575</td>\n",
       "      <td>685.527010</td>\n",
       "      <td>30.00</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>200.000</td>\n",
       "      <td>703.8900</td>\n",
       "      <td>7680.60</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINTO ITAMARATY</th>\n",
       "      <td>33.0</td>\n",
       "      <td>2587.188485</td>\n",
       "      <td>3408.834811</td>\n",
       "      <td>60.69</td>\n",
       "      <td>545.8500</td>\n",
       "      <td>1336.690</td>\n",
       "      <td>3500.0000</td>\n",
       "      <td>17500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIMUNDO LIRA</th>\n",
       "      <td>101.0</td>\n",
       "      <td>1726.732673</td>\n",
       "      <td>481.433503</td>\n",
       "      <td>800.00</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>2000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RANDOLFE RODRIGUES</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1429.841939</td>\n",
       "      <td>1686.607948</td>\n",
       "      <td>6.24</td>\n",
       "      <td>629.2200</td>\n",
       "      <td>940.000</td>\n",
       "      <td>1470.9375</td>\n",
       "      <td>11306.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGINA SOUSA</th>\n",
       "      <td>326.0</td>\n",
       "      <td>957.015736</td>\n",
       "      <td>1845.580891</td>\n",
       "      <td>16.00</td>\n",
       "      <td>68.0050</td>\n",
       "      <td>220.040</td>\n",
       "      <td>1174.6650</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENAN CALHEIROS</th>\n",
       "      <td>70.0</td>\n",
       "      <td>962.280429</td>\n",
       "      <td>1131.586370</td>\n",
       "      <td>25.59</td>\n",
       "      <td>144.8900</td>\n",
       "      <td>282.400</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>3907.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RICARDO FERRAÇO</th>\n",
       "      <td>296.0</td>\n",
       "      <td>944.280101</td>\n",
       "      <td>1568.568888</td>\n",
       "      <td>18.70</td>\n",
       "      <td>280.5325</td>\n",
       "      <td>423.635</td>\n",
       "      <td>973.9725</td>\n",
       "      <td>11500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RICARDO FRANCO</th>\n",
       "      <td>82.0</td>\n",
       "      <td>919.337683</td>\n",
       "      <td>1401.637879</td>\n",
       "      <td>40.00</td>\n",
       "      <td>119.6325</td>\n",
       "      <td>371.120</td>\n",
       "      <td>654.8450</td>\n",
       "      <td>4800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO MUNIZ</th>\n",
       "      <td>357.0</td>\n",
       "      <td>528.178627</td>\n",
       "      <td>727.286524</td>\n",
       "      <td>5.45</td>\n",
       "      <td>88.0000</td>\n",
       "      <td>263.650</td>\n",
       "      <td>696.5700</td>\n",
       "      <td>4125.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO REQUIÃO</th>\n",
       "      <td>513.0</td>\n",
       "      <td>603.173762</td>\n",
       "      <td>962.259881</td>\n",
       "      <td>2.95</td>\n",
       "      <td>145.2200</td>\n",
       "      <td>227.550</td>\n",
       "      <td>661.3900</td>\n",
       "      <td>6199.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO ROCHA</th>\n",
       "      <td>116.0</td>\n",
       "      <td>3145.715690</td>\n",
       "      <td>3816.869175</td>\n",
       "      <td>10.00</td>\n",
       "      <td>200.6550</td>\n",
       "      <td>1465.575</td>\n",
       "      <td>5500.0000</td>\n",
       "      <td>22800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROMERO JUCÁ</th>\n",
       "      <td>436.0</td>\n",
       "      <td>1023.965550</td>\n",
       "      <td>1369.313814</td>\n",
       "      <td>5.30</td>\n",
       "      <td>98.1700</td>\n",
       "      <td>396.250</td>\n",
       "      <td>1323.9975</td>\n",
       "      <td>10600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROMÁRIO</th>\n",
       "      <td>179.0</td>\n",
       "      <td>1906.960559</td>\n",
       "      <td>2608.047684</td>\n",
       "      <td>88.80</td>\n",
       "      <td>694.0000</td>\n",
       "      <td>1089.320</td>\n",
       "      <td>1745.8400</td>\n",
       "      <td>14900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RONALDO CAIADO</th>\n",
       "      <td>579.0</td>\n",
       "      <td>407.298636</td>\n",
       "      <td>669.208603</td>\n",
       "      <td>14.00</td>\n",
       "      <td>140.3800</td>\n",
       "      <td>199.610</td>\n",
       "      <td>329.7650</td>\n",
       "      <td>4483.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROSE DE FREITAS</th>\n",
       "      <td>351.0</td>\n",
       "      <td>1128.457094</td>\n",
       "      <td>4304.023370</td>\n",
       "      <td>8.50</td>\n",
       "      <td>171.0000</td>\n",
       "      <td>801.780</td>\n",
       "      <td>1202.7750</td>\n",
       "      <td>79680.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDRA BRAGA</th>\n",
       "      <td>41.0</td>\n",
       "      <td>2856.271951</td>\n",
       "      <td>2950.018999</td>\n",
       "      <td>566.83</td>\n",
       "      <td>1406.3600</td>\n",
       "      <td>1858.620</td>\n",
       "      <td>3484.0000</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMONE TEBET</th>\n",
       "      <td>225.0</td>\n",
       "      <td>891.971556</td>\n",
       "      <td>832.343717</td>\n",
       "      <td>33.10</td>\n",
       "      <td>475.5100</td>\n",
       "      <td>665.790</td>\n",
       "      <td>1157.7300</td>\n",
       "      <td>3923.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>125.0</td>\n",
       "      <td>3707.651600</td>\n",
       "      <td>11757.386179</td>\n",
       "      <td>72.93</td>\n",
       "      <td>356.9600</td>\n",
       "      <td>936.790</td>\n",
       "      <td>2402.8800</td>\n",
       "      <td>80000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TASSO JEREISSATI</th>\n",
       "      <td>48.0</td>\n",
       "      <td>3833.423333</td>\n",
       "      <td>5596.315142</td>\n",
       "      <td>0.30</td>\n",
       "      <td>672.2975</td>\n",
       "      <td>872.355</td>\n",
       "      <td>1583.3625</td>\n",
       "      <td>14400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>586.0</td>\n",
       "      <td>833.923072</td>\n",
       "      <td>1993.159838</td>\n",
       "      <td>0.01</td>\n",
       "      <td>60.1000</td>\n",
       "      <td>174.615</td>\n",
       "      <td>604.3850</td>\n",
       "      <td>18000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>309.0</td>\n",
       "      <td>1343.102395</td>\n",
       "      <td>2122.107585</td>\n",
       "      <td>24.88</td>\n",
       "      <td>162.0000</td>\n",
       "      <td>500.000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>20000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>278.0</td>\n",
       "      <td>1716.114245</td>\n",
       "      <td>2601.589275</td>\n",
       "      <td>40.00</td>\n",
       "      <td>450.0000</td>\n",
       "      <td>851.875</td>\n",
       "      <td>1808.0350</td>\n",
       "      <td>17000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICENTINHO ALVES</th>\n",
       "      <td>74.0</td>\n",
       "      <td>4052.574595</td>\n",
       "      <td>5467.742757</td>\n",
       "      <td>274.64</td>\n",
       "      <td>481.9825</td>\n",
       "      <td>1100.000</td>\n",
       "      <td>9320.5250</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRGINIO DE CARVALHO</th>\n",
       "      <td>34.0</td>\n",
       "      <td>1365.249706</td>\n",
       "      <td>1270.231996</td>\n",
       "      <td>55.53</td>\n",
       "      <td>533.2600</td>\n",
       "      <td>1000.000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>5200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALDEMIR MOKA</th>\n",
       "      <td>419.0</td>\n",
       "      <td>633.981050</td>\n",
       "      <td>821.418792</td>\n",
       "      <td>50.00</td>\n",
       "      <td>146.2450</td>\n",
       "      <td>370.760</td>\n",
       "      <td>702.6150</td>\n",
       "      <td>5000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALTER PINHEIRO</th>\n",
       "      <td>126.0</td>\n",
       "      <td>622.658889</td>\n",
       "      <td>839.184778</td>\n",
       "      <td>5.00</td>\n",
       "      <td>156.4900</td>\n",
       "      <td>401.640</td>\n",
       "      <td>636.3975</td>\n",
       "      <td>4125.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLINGTON FAGUNDES</th>\n",
       "      <td>925.0</td>\n",
       "      <td>448.815384</td>\n",
       "      <td>1619.613354</td>\n",
       "      <td>8.00</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>100.000</td>\n",
       "      <td>458.4600</td>\n",
       "      <td>40000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WILDER MORAIS</th>\n",
       "      <td>364.0</td>\n",
       "      <td>693.797802</td>\n",
       "      <td>1513.942453</td>\n",
       "      <td>2.65</td>\n",
       "      <td>87.3775</td>\n",
       "      <td>151.510</td>\n",
       "      <td>358.3500</td>\n",
       "      <td>10693.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIRLANDE DA LUZ</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1129.825000</td>\n",
       "      <td>594.058339</td>\n",
       "      <td>24.33</td>\n",
       "      <td>1048.1925</td>\n",
       "      <td>1160.110</td>\n",
       "      <td>1362.0175</td>\n",
       "      <td>2234.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEZÉ PERRELLA</th>\n",
       "      <td>398.0</td>\n",
       "      <td>810.135176</td>\n",
       "      <td>4199.936436</td>\n",
       "      <td>16.47</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>200.000</td>\n",
       "      <td>599.0225</td>\n",
       "      <td>80000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÂNGELA PORTELA</th>\n",
       "      <td>171.0</td>\n",
       "      <td>2512.315673</td>\n",
       "      <td>4580.254034</td>\n",
       "      <td>64.21</td>\n",
       "      <td>422.6300</td>\n",
       "      <td>730.000</td>\n",
       "      <td>2400.0000</td>\n",
       "      <td>28000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count          mean           std       min  \\\n",
       "congressperson_name                                                     \n",
       "ACIR GURGACZ              140.0   2156.934857   2864.690150     40.00   \n",
       "ALOYSIO NUNES FERREIRA    432.0    661.846296   1357.902019      5.55   \n",
       "ALVARO DIAS               150.0    886.473000    435.867230    178.00   \n",
       "ANA AMÉLIA                397.0    420.882695    498.887655      8.00   \n",
       "ANTONIO ANASTASIA         273.0   1144.545458   1532.358356     22.00   \n",
       "ANTÔNIO CARLOS VALADARES  155.0   1713.440452   1650.345721    250.83   \n",
       "ARMANDO MONTEIRO          232.0    675.458707   1027.924855      4.08   \n",
       "ATAÍDES OLIVEIRA          175.0   1712.092629   3079.333122     85.00   \n",
       "AÉCIO NEVES               307.0    962.006026   1854.973271     15.00   \n",
       "BENEDITO DE LIRA          268.0    645.377910    856.757007     16.73   \n",
       "BLAIRO MAGGI               79.0   1372.065570   2252.756787      8.80   \n",
       "CIDINHO SANTOS            156.0   1489.743462   2054.534316      7.55   \n",
       "CIRO NOGUEIRA             328.0   1341.609177   1955.232902     45.00   \n",
       "CRISTOVAM BUARQUE           7.0  15371.428571  12115.377871   2100.00   \n",
       "CÁSSIO CUNHA LIMA         255.0    668.152549   1016.598787     10.64   \n",
       "DALIRIO BEBER             544.0    393.595423    574.972304      3.00   \n",
       "DAVI ALCOLUMBRE           133.0   3835.158421  22271.242467    170.00   \n",
       "DECA                      128.0   1046.193438   1405.788875      9.00   \n",
       "DONIZETI NOGUEIRA         182.0    600.361868   1170.831507     25.00   \n",
       "DOUGLAS CINTRA            108.0    685.209537    972.108468     22.87   \n",
       "DÁRIO BERGER              442.0    723.561290   1038.533209     10.00   \n",
       "EDISON LOBÃO               46.0   3294.706739   5240.292523     58.43   \n",
       "EDUARDO AMORIM            472.0    648.851992    817.621372     40.00   \n",
       "EDUARDO BRAGA             116.0   2529.536379   2790.122521     30.00   \n",
       "EDUARDO LOPES             133.0    829.245489   2961.260094     40.49   \n",
       "ELMANO FÉRRER             292.0   1469.045548   1644.973324      5.00   \n",
       "FERNANDO BEZERRA COELHO   601.0    724.029884   1459.551181      0.01   \n",
       "FERNANDO COLLOR            21.0  20022.429048   7545.173689  10000.00   \n",
       "FLEXA RIBEIRO             341.0   1067.514194   2079.523837     35.00   \n",
       "FÁTIMA BEZERRA            635.0    528.045575    685.527010     30.00   \n",
       "...                         ...           ...           ...       ...   \n",
       "PINTO ITAMARATY            33.0   2587.188485   3408.834811     60.69   \n",
       "RAIMUNDO LIRA             101.0   1726.732673    481.433503    800.00   \n",
       "RANDOLFE RODRIGUES        330.0   1429.841939   1686.607948      6.24   \n",
       "REGINA SOUSA              326.0    957.015736   1845.580891     16.00   \n",
       "RENAN CALHEIROS            70.0    962.280429   1131.586370     25.59   \n",
       "RICARDO FERRAÇO           296.0    944.280101   1568.568888     18.70   \n",
       "RICARDO FRANCO             82.0    919.337683   1401.637879     40.00   \n",
       "ROBERTO MUNIZ             357.0    528.178627    727.286524      5.45   \n",
       "ROBERTO REQUIÃO           513.0    603.173762    962.259881      2.95   \n",
       "ROBERTO ROCHA             116.0   3145.715690   3816.869175     10.00   \n",
       "ROMERO JUCÁ               436.0   1023.965550   1369.313814      5.30   \n",
       "ROMÁRIO                   179.0   1906.960559   2608.047684     88.80   \n",
       "RONALDO CAIADO            579.0    407.298636    669.208603     14.00   \n",
       "ROSE DE FREITAS           351.0   1128.457094   4304.023370      8.50   \n",
       "SANDRA BRAGA               41.0   2856.271951   2950.018999    566.83   \n",
       "SIMONE TEBET              225.0    891.971556    832.343717     33.10   \n",
       "SÉRGIO PETECÃO            125.0   3707.651600  11757.386179     72.93   \n",
       "TASSO JEREISSATI           48.0   3833.423333   5596.315142      0.30   \n",
       "TELMÁRIO MOTA             586.0    833.923072   1993.159838      0.01   \n",
       "VALDIR RAUPP              309.0   1343.102395   2122.107585     24.88   \n",
       "VANESSA GRAZZIOTIN        278.0   1716.114245   2601.589275     40.00   \n",
       "VICENTINHO ALVES           74.0   4052.574595   5467.742757    274.64   \n",
       "VIRGINIO DE CARVALHO       34.0   1365.249706   1270.231996     55.53   \n",
       "WALDEMIR MOKA             419.0    633.981050    821.418792     50.00   \n",
       "WALTER PINHEIRO           126.0    622.658889    839.184778      5.00   \n",
       "WELLINGTON FAGUNDES       925.0    448.815384   1619.613354      8.00   \n",
       "WILDER MORAIS             364.0    693.797802   1513.942453      2.65   \n",
       "WIRLANDE DA LUZ            10.0   1129.825000    594.058339     24.33   \n",
       "ZEZÉ PERRELLA             398.0    810.135176   4199.936436     16.47   \n",
       "ÂNGELA PORTELA            171.0   2512.315673   4580.254034     64.21   \n",
       "\n",
       "                                 25%        50%         75%        max  \n",
       "congressperson_name                                                     \n",
       "ACIR GURGACZ                591.3275   1547.590   2084.8775   19800.00  \n",
       "ALOYSIO NUNES FERREIRA      104.4000    178.485    777.6025    8266.00  \n",
       "ALVARO DIAS                 541.0450    795.505   1120.8400    2196.25  \n",
       "ANA AMÉLIA                   72.4900    195.000    628.8600    2759.21  \n",
       "ANTONIO ANASTASIA           362.9800    633.940   1017.0000    6613.31  \n",
       "ANTÔNIO CARLOS VALADARES    577.5350   1045.000   2316.6500    5900.00  \n",
       "ARMANDO MONTEIRO            169.9225    412.800    812.5225    7420.00  \n",
       "ATAÍDES OLIVEIRA            793.4100    979.400   1500.0000   17500.00  \n",
       "AÉCIO NEVES                  72.5000    326.600    900.0000    9000.00  \n",
       "BENEDITO DE LIRA            173.2675    247.790    776.2950    4000.00  \n",
       "BLAIRO MAGGI                230.3750    455.630    900.0000    7800.00  \n",
       "CIDINHO SANTOS              260.2150    661.970   1505.3800    8000.00  \n",
       "CIRO NOGUEIRA               212.2550    605.390   1440.0000   12000.00  \n",
       "CRISTOVAM BUARQUE         10250.0000  15000.000  15000.0000   40000.00  \n",
       "CÁSSIO CUNHA LIMA           150.0000    227.920    852.3650    7500.00  \n",
       "DALIRIO BEBER                83.7125    158.875    464.1000    3500.00  \n",
       "DAVI ALCOLUMBRE             453.7300    842.580   2100.0000  256980.00  \n",
       "DECA                        151.2025    340.075   1752.0100    7125.00  \n",
       "DONIZETI NOGUEIRA           100.0000    144.075    400.5000    6000.00  \n",
       "DOUGLAS CINTRA              173.8125    348.755    811.8900    5000.00  \n",
       "DÁRIO BERGER                179.9950    319.820    732.5250    4850.00  \n",
       "EDISON LOBÃO                 67.4375    174.740   9602.7450   12000.00  \n",
       "EDUARDO AMORIM              150.0000    272.730    891.7750    3950.00  \n",
       "EDUARDO BRAGA              1072.8000   1520.605   2688.6500   12000.00  \n",
       "EDUARDO LOPES               104.2500    193.720    668.6800   33000.00  \n",
       "ELMANO FÉRRER               315.1800    823.460   2398.7175    6500.00  \n",
       "FERNANDO BEZERRA COELHO     142.4800    213.110    627.4400   15000.00  \n",
       "FERNANDO COLLOR           12000.0000  23056.200  27655.0700   28922.72  \n",
       "FLEXA RIBEIRO               125.3200    540.000   1117.1900   18000.00  \n",
       "FÁTIMA BEZERRA              120.0000    200.000    703.8900    7680.60  \n",
       "...                              ...        ...         ...        ...  \n",
       "PINTO ITAMARATY             545.8500   1336.690   3500.0000   17500.00  \n",
       "RAIMUNDO LIRA              2000.0000   2000.000   2000.0000    2000.00  \n",
       "RANDOLFE RODRIGUES          629.2200    940.000   1470.9375   11306.91  \n",
       "REGINA SOUSA                 68.0050    220.040   1174.6650   10000.00  \n",
       "RENAN CALHEIROS             144.8900    282.400   2000.0000    3907.30  \n",
       "RICARDO FERRAÇO             280.5325    423.635    973.9725   11500.00  \n",
       "RICARDO FRANCO              119.6325    371.120    654.8450    4800.00  \n",
       "ROBERTO MUNIZ                88.0000    263.650    696.5700    4125.22  \n",
       "ROBERTO REQUIÃO             145.2200    227.550    661.3900    6199.95  \n",
       "ROBERTO ROCHA               200.6550   1465.575   5500.0000   22800.00  \n",
       "ROMERO JUCÁ                  98.1700    396.250   1323.9975   10600.00  \n",
       "ROMÁRIO                     694.0000   1089.320   1745.8400   14900.00  \n",
       "RONALDO CAIADO              140.3800    199.610    329.7650    4483.60  \n",
       "ROSE DE FREITAS             171.0000    801.780   1202.7750   79680.00  \n",
       "SANDRA BRAGA               1406.3600   1858.620   3484.0000   12000.00  \n",
       "SIMONE TEBET                475.5100    665.790   1157.7300    3923.94  \n",
       "SÉRGIO PETECÃO              356.9600    936.790   2402.8800   80000.00  \n",
       "TASSO JEREISSATI            672.2975    872.355   1583.3625   14400.00  \n",
       "TELMÁRIO MOTA                60.1000    174.615    604.3850   18000.00  \n",
       "VALDIR RAUPP                162.0000    500.000   2000.0000   20000.00  \n",
       "VANESSA GRAZZIOTIN          450.0000    851.875   1808.0350   17000.00  \n",
       "VICENTINHO ALVES            481.9825   1100.000   9320.5250   30000.00  \n",
       "VIRGINIO DE CARVALHO        533.2600   1000.000   2000.0000    5200.00  \n",
       "WALDEMIR MOKA               146.2450    370.760    702.6150    5000.00  \n",
       "WALTER PINHEIRO             156.4900    401.640    636.3975    4125.22  \n",
       "WELLINGTON FAGUNDES          20.0000    100.000    458.4600   40000.00  \n",
       "WILDER MORAIS                87.3775    151.510    358.3500   10693.80  \n",
       "WIRLANDE DA LUZ            1048.1925   1160.110   1362.0175    2234.37  \n",
       "ZEZÉ PERRELLA               100.0000    200.000    599.0225   80000.00  \n",
       "ÂNGELA PORTELA              422.6300    730.000   2400.0000   28000.00  \n",
       "\n",
       "[93 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bysenator.reimbursement_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.SeriesGroupBy object at 0x7fb5785357b8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtrar senador por nome\n",
    "bysenator.congressperson_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kpl/jxo2Duts6PV+SNH78eCUlJWnz5s1q0qSJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56b018358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# valores gastos por senador\n",
    "s = bysenator.reimbursement_value.sum()\n",
    "s.sort_values(ascending=False).plot.bar(figsize=(18,18))\n",
    "pyplot.savefig(\"/dados/gasto_senadores.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------\n",
    "Esses graficos nos ajudam a ver quais meses nossos senadores gastaram mais e quais senadores gastaram mais/menos.\n",
    "\n",
    "Visualmente podemos identificar senadores com gastos altissimos e alguns com poquissimo gasto.\n",
    "\n",
    "Vamos investigar esses casos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "congressperson_name\n",
       "ACIR GURGACZ                 19760.00\n",
       "ALOYSIO NUNES FERREIRA        8260.45\n",
       "ALVARO DIAS                   2018.25\n",
       "ANA AMÉLIA                    2751.21\n",
       "ANTONIO ANASTASIA             6591.31\n",
       "ANTÔNIO CARLOS VALADARES      5649.17\n",
       "ARMANDO MONTEIRO              7415.92\n",
       "ATAÍDES OLIVEIRA             17415.00\n",
       "AÉCIO NEVES                   8985.00\n",
       "BENEDITO DE LIRA              3983.27\n",
       "BLAIRO MAGGI                  7791.20\n",
       "CIDINHO SANTOS                7992.45\n",
       "CIRO NOGUEIRA                11955.00\n",
       "CRISTOVAM BUARQUE            37900.00\n",
       "CÁSSIO CUNHA LIMA             7489.36\n",
       "DALIRIO BEBER                 3497.00\n",
       "DAVI ALCOLUMBRE             256810.00\n",
       "DECA                          7116.00\n",
       "DONIZETI NOGUEIRA             5975.00\n",
       "DOUGLAS CINTRA                4977.13\n",
       "DÁRIO BERGER                  4840.00\n",
       "EDISON LOBÃO                 11941.57\n",
       "EDUARDO AMORIM                3910.00\n",
       "EDUARDO BRAGA                11970.00\n",
       "EDUARDO LOPES                32959.51\n",
       "ELMANO FÉRRER                 6495.00\n",
       "FERNANDO BEZERRA COELHO      14999.99\n",
       "FERNANDO COLLOR              18922.72\n",
       "FLEXA RIBEIRO                17965.00\n",
       "FÁTIMA BEZERRA                7650.60\n",
       "                              ...    \n",
       "PINTO ITAMARATY              17439.31\n",
       "RAIMUNDO LIRA                 1200.00\n",
       "RANDOLFE RODRIGUES           11300.67\n",
       "REGINA SOUSA                  9984.00\n",
       "RENAN CALHEIROS               3881.71\n",
       "RICARDO FERRAÇO              11481.30\n",
       "RICARDO FRANCO                4760.00\n",
       "ROBERTO MUNIZ                 4119.77\n",
       "ROBERTO REQUIÃO               6197.00\n",
       "ROBERTO ROCHA                22790.00\n",
       "ROMERO JUCÁ                  10594.70\n",
       "ROMÁRIO                      14811.20\n",
       "RONALDO CAIADO                4469.60\n",
       "ROSE DE FREITAS              79671.50\n",
       "SANDRA BRAGA                 11433.17\n",
       "SIMONE TEBET                  3890.84\n",
       "SÉRGIO PETECÃO               79927.07\n",
       "TASSO JEREISSATI             14399.70\n",
       "TELMÁRIO MOTA                17999.99\n",
       "VALDIR RAUPP                 19975.12\n",
       "VANESSA GRAZZIOTIN           16960.00\n",
       "VICENTINHO ALVES             29725.36\n",
       "VIRGINIO DE CARVALHO          5144.47\n",
       "WALDEMIR MOKA                 4950.00\n",
       "WALTER PINHEIRO               4120.22\n",
       "WELLINGTON FAGUNDES          39992.00\n",
       "WILDER MORAIS                10691.15\n",
       "WIRLANDE DA LUZ               2210.04\n",
       "ZEZÉ PERRELLA                79983.53\n",
       "ÂNGELA PORTELA               27935.79\n",
       "Name: reimbursement_value, Length: 93, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# podemos aplicar uma função para agregar os valores de uma coluna,\n",
    "# nesse caso, vamos aplicar uma função para ver o range de valores que nossos senadores pediram de reembolso.\n",
    "def max_min(array):\n",
    "    return array.max() - array.min()\n",
    "bysenator.reimbursement_value.agg(max_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agora vamos tirar os dados que estão distorcendo nossa distribuição.\n",
    "clean_outliers = df[(df[\"reimbursement_value\"] < df.reimbursement_value.quantile(0.995))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26535.000000\n",
       "mean       826.170997\n",
       "std       1374.499845\n",
       "min          0.010000\n",
       "25%        122.485000\n",
       "50%        319.260000\n",
       "75%        936.655000\n",
       "max      11944.160000\n",
       "Name: reimbursement_value, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_outliers.reimbursement_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     26691.000000\n",
       "mean        947.484513\n",
       "std        2733.820037\n",
       "min           0.010000\n",
       "25%         124.000000\n",
       "50%         324.790000\n",
       "75%         956.865000\n",
       "max      256980.000000\n",
       "Name: reimbursement_value, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56adabb00>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nt5OZmQmtVotdu3YhLi4OM2fOxFNPPYWAgADs3LkTM2bMQFxcHPbt22fyvDfeeAPDhg3D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56add1be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vamos ver se os gastos estão mais proximos na distribuição agrupada por mês.\n",
    "clean_outliers.boxplot(column=\"reimbursement_value\", by=\"month\", figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------\n",
    "Percentis são valores que agrupam os valores de uma distribuição,\n",
    "\n",
    "ex: percentil 25 com valor 124, significa que 25% dos dados são iguais ou menores que 124.\n",
    "\n",
    "Os percentis mais comuns são 25, 50 (mediana), 75. A diferença entre o percentil 25 e 75 é conhecido como interquartil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.56539657562475"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# em qual percentil um valor está\n",
    "def percentil_rank(values, value):\n",
    "    count = 0\n",
    "    for v in values:\n",
    "        if v <= value:\n",
    "            count += 1\n",
    "    return 100 * count / len(values)\n",
    "\n",
    "percentil_rank(df.reimbursement_value, 12000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "Vamos ver os efeitos da limpeza de outliers nos percentis da distribuição."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v6MKOZ8OGDdmwYUOSZMWKFZk6deppu61XXmmc9L6nso4JEzpO+dhzUUdHh/uqAHOowyzq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\nIoQZAEAR/w+MVxRgxHqwVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56a942c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_percentis(dist, top=10000):\n",
    "    colors = [\"black\", \"yellow\", \"green\", \"purple\", \"blue\"]\n",
    "    percentis = [0.005, 0.25, 0.5, 0.75, 0.995]\n",
    "    for color, percentil in zip(colors, percentis):\n",
    "        pyplot.vlines(dist.quantile(percentil), # Plot black line at mean\n",
    "              ymin=0,\n",
    "              ymax=top,\n",
    "              color=color)\n",
    "\n",
    "df.reimbursement_value.hist(figsize=(10,10), bins=50)\n",
    "plot_percentis(df.reimbursement_value, 25000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XnOiyAADGrRO+YjZv3rwsXrw43/rWt9LQ0JAPf/jD6ejoyKWXXpr169fnRz/6Uc4777ws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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56a955748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clean_outliers.reimbursement_value.hist(figsize=(10,10), bins=50)\n",
    "plot_percentis(clean_outliers.reimbursement_value, 12000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------\n",
    "\n",
    "Limpamos os outliers e vimos que a distribuição ficou mais comportada.\n",
    "Porém, nesse caso, não queremos descarta-los e sim analisa-los para saber o porque gastaram tanto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers = df[df[\"reimbursement_value\"] >= df.reimbursement_value.quantile(0.995)]\n",
    "outliers.reimbursement_value.describe()\n",
    "clean_outliers.reimbursement_value.count() + outliers.reimbursement_value.count() == df.reimbursement_value.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "reembolsos_senador = pandas.crosstab(index=outliers[\"congressperson_name\"], columns=\"qtd_reembolsos\")\n",
    "reembolsos_cnpj_cpf = pandas.crosstab(index=outliers[\"cnpj_cpf\"], columns=\"qtd_reembolsos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>qtd_reembolsos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>congressperson_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÂNGELA PORTELA</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDISON LOBÃO</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TASSO JEREISSATI</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OMAR AZIZ</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO BRAGA</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATAÍDES OLIVEIRA</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICENTINHO ALVES</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRISTOVAM BUARQUE</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLADSON CAMELI</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HÉLIO JOSÉ</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ MEDEIROS</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO ROCHA</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEZÉ PERRELLA</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLINGTON FAGUNDES</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDRA BRAGA</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROMÁRIO</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACIR GURGACZ</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LÚCIA VÂNIA</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOÃO CAPIBERIBE</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLEISI HOFFMANN</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARCELO CRIVELLA</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARTA SUPLICY</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO PAIM</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINTO ITAMARATY</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROSE DE FREITAS</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ SERRA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEXA RIBEIRO</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO BEZERRA COELHO</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO LOPES</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAVI ALCOLUMBRE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIRO NOGUEIRA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0                    qtd_reembolsos\n",
       "congressperson_name                    \n",
       "FERNANDO COLLOR                      20\n",
       "ÂNGELA PORTELA                       13\n",
       "EDISON LOBÃO                         12\n",
       "TASSO JEREISSATI                     11\n",
       "OMAR AZIZ                            11\n",
       "EDUARDO BRAGA                         7\n",
       "ATAÍDES OLIVEIRA                      6\n",
       "VICENTINHO ALVES                      5\n",
       "CRISTOVAM BUARQUE                     5\n",
       "GLADSON CAMELI                        5\n",
       "HÉLIO JOSÉ                            5\n",
       "JOSÉ MEDEIROS                         5\n",
       "ROBERTO ROCHA                         5\n",
       "ZEZÉ PERRELLA                         5\n",
       "TELMÁRIO MOTA                         4\n",
       "WELLINGTON FAGUNDES                   3\n",
       "SÉRGIO PETECÃO                        3\n",
       "SANDRA BRAGA                          3\n",
       "ROMÁRIO                               3\n",
       "ACIR GURGACZ                          3\n",
       "LÚCIA VÂNIA                           3\n",
       "JOÃO CAPIBERIBE                       2\n",
       "GLEISI HOFFMANN                       2\n",
       "VANESSA GRAZZIOTIN                    2\n",
       "MARCELO CRIVELLA                      2\n",
       "MARTA SUPLICY                         1\n",
       "PAULO PAIM                            1\n",
       "PINTO ITAMARATY                       1\n",
       "ROSE DE FREITAS                       1\n",
       "JOSÉ SERRA                            1\n",
       "FLEXA RIBEIRO                         1\n",
       "FERNANDO BEZERRA COELHO               1\n",
       "VALDIR RAUPP                          1\n",
       "EDUARDO LOPES                         1\n",
       "DAVI ALCOLUMBRE                       1\n",
       "CIRO NOGUEIRA                         1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reembolsos_senador.sort_values(by=\"qtd_reembolsos\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "col_0               qtd_reembolsos\n",
       "cnpj_cpf                          \n",
       "04.850.792/0001-44              12\n",
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       "08.988.587/0001-09               3\n",
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       "091.592.231-20                   3\n",
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       "21.793.072/0001-03               1\n",
       "22.079.440/0001-19               1\n",
       "73.289.340/0001-58               1\n",
       "24.930.645/0001-91               1\n",
       "19.572.509/0001-37               1\n",
       "00.433.623/0001-58               1\n",
       "17.256.653/0001-39               1\n",
       "05.763.587/0001-04               1\n",
       "03.003.930/0001-97               1\n",
       "03.942.303/0001-11               1\n",
       "04.144.804/0001-15               1\n",
       "04.453.641/0001-52               1\n",
       "04.527.033/0001-45               1\n",
       "04.953.008/0001-23               1\n",
       "05.461.408/0001-84               1\n",
       "07.053.387/0001-93               1\n",
       "14.003.690/0001-47               1\n",
       "08.154.750/0001-20               1\n",
       "09.192.078/0001-20               1\n",
       "11.591.334/0001-01               1\n",
       "12.974.269/0001-58               1\n",
       "01.098.474/0002-61               1\n",
       "13.768.347/0001-20               1\n",
       "13.850.055/0001-32               1\n",
       "86.863.412/0001-70               1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reembolsos_cnpj_cpf.sort_values(by=\"qtd_reembolsos\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "Agora vamos analisar o gasto por partido e estado dos senadores.\n",
    "\n",
    "Nós não temos essa informação no dado inicial, mas podemos pega-la em outro arquivo e\n",
    "fazer um merge entre os dois."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "senadores = pandas.read_html(\"senadores.html\", encoding = 'utf8')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sérgio Petecão (Sérgio de Oliveira Cunha)</td>\n",
       "      <td>PSD</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Benedito de Lira</td>\n",
       "      <td>PP</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0_level_0                                        Nome  \\\n",
       "                Acre                          Unnamed: 1_level_1   \n",
       "0                NaN     Gladson Cameli (Gladson de Lima Cameli)   \n",
       "1                NaN  Jorge Viana (Jorge Ney Viana Macedo Neves)   \n",
       "2                NaN   Sérgio Petecão (Sérgio de Oliveira Cunha)   \n",
       "3            Alagoas                                         NaN   \n",
       "4                NaN                            Benedito de Lira   \n",
       "\n",
       "             Partido  Início de mandato     Fim de mandato               Obs.  \n",
       "  Unnamed: 2_level_1 Unnamed: 3_level_1 Unnamed: 4_level_1 Unnamed: 5_level_1  \n",
       "0                 PP             2015.0             2023.0                NaN  \n",
       "1                 PT             2011.0             2019.0                NaN  \n",
       "2                PSD             2011.0             2019.0                NaN  \n",
       "3                NaN                NaN                NaN                NaN  \n",
       "4                 PP             2011.0             2019.0                NaN  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------\n",
    "Nossos dados estão com varios problemas, precisamos trata-los antes de fazer o merge com o nosso dataframe principal.\n",
    "\n",
    "Vamos então:\n",
    "* excluir colunas que nao fazem sentido\n",
    "* renomear as colunas\n",
    "* normalizar a coluna de estado\n",
    "* tratar o nome dos senadores pois esse será nosso ponto de merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107, 6)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del senadores[\"Início de mandato\"]\n",
    "del senadores[\"Fim de mandato\"]\n",
    "del senadores[\"Obs.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
       "      <th>Nome</th>\n",
       "      <th>Partido</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Acre</th>\n",
       "      <th>Unnamed: 1_level_1</th>\n",
       "      <th>Unnamed: 2_level_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gladson Cameli (Gladson de Lima Cameli)</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Jorge Viana (Jorge Ney Viana Macedo Neves)</td>\n",
       "      <td>PT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sérgio Petecão (Sérgio de Oliveira Cunha)</td>\n",
       "      <td>PSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Benedito de Lira</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0_level_0                                        Nome  \\\n",
       "                Acre                          Unnamed: 1_level_1   \n",
       "0                NaN     Gladson Cameli (Gladson de Lima Cameli)   \n",
       "1                NaN  Jorge Viana (Jorge Ney Viana Macedo Neves)   \n",
       "2                NaN   Sérgio Petecão (Sérgio de Oliveira Cunha)   \n",
       "3            Alagoas                                         NaN   \n",
       "4                NaN                            Benedito de Lira   \n",
       "\n",
       "             Partido  \n",
       "  Unnamed: 2_level_1  \n",
       "0                 PP  \n",
       "1                 PT  \n",
       "2                PSD  \n",
       "3                NaN  \n",
       "4                 PP  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "senadores.rename(columns={\"Unnamed: 0_level_0\": \"estado\", \"Nome\": \"nome\", \"Partido\": \"partido\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "estado = \"Acre\"\n",
    "for i, row in senadores.iterrows():\n",
    "    if pandas.isnull(row[\"estado\"][0]):\n",
    "        senadores.loc[i][\"estado\"] = estado\n",
    "    else:\n",
    "        estado = row[\"estado\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores.columns = ['estado', 'nome', 'partido']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores.nome = senadores.nome.str.replace(r\"\\s\\(.*\\)\", \"\").str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>estado</th>\n",
       "      <th>nome</th>\n",
       "      <th>partido</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acre</td>\n",
       "      <td>GLADSON CAMELI</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acre</td>\n",
       "      <td>JORGE VIANA</td>\n",
       "      <td>PT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acre</td>\n",
       "      <td>SÉRGIO PETECÃO</td>\n",
       "      <td>PSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>BENEDITO DE LIRA</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>PTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    estado              nome partido\n",
       "0     Acre    GLADSON CAMELI      PP\n",
       "1     Acre       JORGE VIANA      PT\n",
       "2     Acre    SÉRGIO PETECÃO     PSD\n",
       "4  Alagoas  BENEDITO DE LIRA      PP\n",
       "5  Alagoas   FERNANDO COLLOR     PTC"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------\n",
    "Agora com os dados tratados podemos fazer nosso merge e começar as analises."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Nome</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Estado</th>\n",
       "      <th>Titular</th>\n",
       "      <th>Período(s) exercido(s)</th>\n",
       "      <th>Obs.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Deca Sobrinho (José Gonzaga Sobrinho)</td>\n",
       "      <td>PSDB</td>\n",
       "      <td>Paraíba</td>\n",
       "      <td>Cássio Cunha Lima</td>\n",
       "      <td>2015 - 2016</td>\n",
       "      <td>Primeiro suplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Donizeti Nogueira (Divino Donizeti Borges Nogu...</td>\n",
       "      <td>PT</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Kátia Abreu</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>Primeiro suplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Douglas Cintra (Douglas Mauricio Ramos Cintra)</td>\n",
       "      <td>PTB</td>\n",
       "      <td>Pernambuco</td>\n",
       "      <td>Armando Monteiro</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>Primeiro suplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Fernando Ribeiro (Fernando de Castro Ribeiro)</td>\n",
       "      <td>PMDB</td>\n",
       "      <td>Pará</td>\n",
       "      <td>Jader Barbalho</td>\n",
       "      <td>2015</td>\n",
       "      <td>Primeiro suplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gilberto Piselo (Gilberto Piselo do Nascimento)</td>\n",
       "      <td>PDT</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Acir Gurgacz</td>\n",
       "      <td>2016</td>\n",
       "      <td>Primeiro suplente</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                               Nome Partido  \\\n",
       "0         NaN              Deca Sobrinho (José Gonzaga Sobrinho)    PSDB   \n",
       "1         NaN  Donizeti Nogueira (Divino Donizeti Borges Nogu...      PT   \n",
       "2         NaN     Douglas Cintra (Douglas Mauricio Ramos Cintra)     PTB   \n",
       "3         NaN      Fernando Ribeiro (Fernando de Castro Ribeiro)    PMDB   \n",
       "4         NaN    Gilberto Piselo (Gilberto Piselo do Nascimento)     PDT   \n",
       "\n",
       "       Estado            Titular Período(s) exercido(s)               Obs.  \n",
       "0     Paraíba  Cássio Cunha Lima            2015 - 2016  Primeiro suplente  \n",
       "1   Tocantins        Kátia Abreu              2015-2016  Primeiro suplente  \n",
       "2  Pernambuco   Armando Monteiro              2015-2016  Primeiro suplente  \n",
       "3        Pará     Jader Barbalho                   2015  Primeiro suplente  \n",
       "4    Rondônia       Acir Gurgacz                   2016  Primeiro suplente  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suplentes = pandas.read_html(\"suplentes.html\", encoding='utf-8')[0]\n",
    "suplentes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nome</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Estado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Deca Sobrinho (José Gonzaga Sobrinho)</td>\n",
       "      <td>PSDB</td>\n",
       "      <td>Paraíba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Donizeti Nogueira (Divino Donizeti Borges Nogu...</td>\n",
       "      <td>PT</td>\n",
       "      <td>Tocantins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Douglas Cintra (Douglas Mauricio Ramos Cintra)</td>\n",
       "      <td>PTB</td>\n",
       "      <td>Pernambuco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fernando Ribeiro (Fernando de Castro Ribeiro)</td>\n",
       "      <td>PMDB</td>\n",
       "      <td>Pará</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gilberto Piselo (Gilberto Piselo do Nascimento)</td>\n",
       "      <td>PDT</td>\n",
       "      <td>Rondônia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Nome Partido      Estado\n",
       "0              Deca Sobrinho (José Gonzaga Sobrinho)    PSDB     Paraíba\n",
       "1  Donizeti Nogueira (Divino Donizeti Borges Nogu...      PT   Tocantins\n",
       "2     Douglas Cintra (Douglas Mauricio Ramos Cintra)     PTB  Pernambuco\n",
       "3      Fernando Ribeiro (Fernando de Castro Ribeiro)    PMDB        Pará\n",
       "4    Gilberto Piselo (Gilberto Piselo do Nascimento)     PDT    Rondônia"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del suplentes[\"Unnamed: 0\"]\n",
    "del suplentes[\"Titular\"]\n",
    "del suplentes[\"Período(s) exercido(s)\"]\n",
    "del suplentes[\"Obs.\"]\n",
    "suplentes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nome</th>\n",
       "      <th>partido</th>\n",
       "      <th>estado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DECA SOBRINHO</td>\n",
       "      <td>PSDB</td>\n",
       "      <td>Paraíba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DONIZETI NOGUEIRA</td>\n",
       "      <td>PT</td>\n",
       "      <td>Tocantins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DOUGLAS CINTRA</td>\n",
       "      <td>PTB</td>\n",
       "      <td>Pernambuco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FERNANDO RIBEIRO</td>\n",
       "      <td>PMDB</td>\n",
       "      <td>Pará</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GILBERTO PISELO</td>\n",
       "      <td>PDT</td>\n",
       "      <td>Rondônia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                nome partido      estado\n",
       "0      DECA SOBRINHO    PSDB     Paraíba\n",
       "1  DONIZETI NOGUEIRA      PT   Tocantins\n",
       "2     DOUGLAS CINTRA     PTB  Pernambuco\n",
       "3   FERNANDO RIBEIRO    PMDB        Pará\n",
       "4    GILBERTO PISELO     PDT    Rondônia"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suplentes.columns = ['nome', 'partido', \"estado\"]\n",
    "suplentes.nome = suplentes.nome.str.replace(r\"\\s\\(.*\\)\", \"\").str.upper()\n",
    "suplentes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores = pandas.concat([senadores, suplentes])\n",
    "senadores = senadores.reset_index(drop=True)\n",
    "len(senadores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26691 entries, 0 to 26690\n",
      "Data columns (total 13 columns):\n",
      "year                   26691 non-null int64\n",
      "month                  26691 non-null int64\n",
      "congressperson_name    26691 non-null object\n",
      "expense_type           26691 non-null object\n",
      "cnpj_cpf               26691 non-null object\n",
      "supplier               26691 non-null object\n",
      "document_id            26691 non-null object\n",
      "date                   26691 non-null object\n",
      "expense_details        26691 non-null object\n",
      "reimbursement_value    26691 non-null float64\n",
      "estado                 23865 non-null object\n",
      "nome                   23865 non-null object\n",
      "partido                23865 non-null object\n",
      "dtypes: float64(1), int64(2), object(10)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df = df.merge(senadores, how=\"left\", left_on=\"congressperson_name\", right_on=\"nome\")\n",
    "df.info()\n",
    "# dist plot de dois anos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.document_id.fillna(0, inplace=True)\n",
    "df.expense_details.fillna(\"0\", inplace=True)\n",
    "del df[\"nome\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56a554860>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VSoXi4mK0bdsWAwcONEfdCCGE6GEwcNvY2GDOnDlQKBRQqVT45ptv0Lx5czRs2NAc9SOE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\nsHATEekMCzcRkc6wcBMR6QwLNxGRzvwPuzF4qofzl/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb56a595080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "por_estado = df.groupby(\"estado\")\n",
    "gasto_estado = por_estado.agg({\"reimbursement_value\": \"sum\"})\n",
    "gasto_estado_percent = gasto_estado.apply(lambda x: 100 * x / float(x.sum()))\n",
    "gasto_estado_percent.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56802a780>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZwCNGjFBwcLDat2/vuu/SpUvq3bu32rRpo969e+vy5ctubSQAAN4mzwDu1KmTPvnkk2z3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\nGAAAAwhgAAAMIIABADCAAAYAwID/D9umAxepw2f8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb568050668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "por_partido = df.groupby(\"partido\")\n",
    "gasto_partido = por_partido.agg({\"reimbursement_value\": \"sum\"})\n",
    "gasto_partido_percent = gasto_partido.apply(lambda x: 100 * x / float(x.sum()))\n",
    "gasto_partido_percent.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb563cd5f28>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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UNcHUNkdXsdIz1d3JJFPryFQdEzcF+1QAz5kzRzt37tSiRYs8fYCPHj2ql19+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\nFhDAAABYQAADAGABAQwAgAUEMAAAFvw/eNPiRrT+DsoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5680c2710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "partidos = por_partido.congressperson_name.unique()\n",
    "qtd_partido = partidos.aggregate(lambda x: len(x))\n",
    "qtd_partido_percent = qtd_partido.apply(lambda x: 100 * x / float(qtd_partido.sum()))\n",
    "qtd_partido_percent.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb563691ba8>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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JCSpfvrwkKSgoSAkJCR5pIAAAvijXc8CZLl26pMGDB2vkyJEKCAjIMs/lcsnlcuW6jDJl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\npC01pKzfZ7//brP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spa1q1ar605/+pN27d0uSoqOj1bFjR7Vv314xMTFZekKrUqWK7r//\n/hta/vbt29WsWTPddtttkqRu3bq553377bdq2rSpypcvL0l64okn9PXXX9uyXgA4BA0UODt27NDS\npUv10UcfqWzZslq5cqWWLVvmnl+yZEmDrQNwo9gDBrzY8uXLJUlHjhzR3r17Va9ePV24cEEBAQH6\nwx/+oOTkZPdjchIQEKDExMRs5zVq1EhffPGF+9D2J5984p734IMP6osvvlB8fLwkadmyZXr44Yft\nWC0AYg8Y8GppaWkKCwvTb7/95r4A6s9//rNWrFih1q1bq0yZMmrQoIF++OGHHJfx+OOPa8SIEVqz\nZo37IqxMtWrVUr9+/fTXv/5VAQEBCgkJcc+rUaOGXnzxRfXp00eSVKlSJY0fP95zKwsUMnRFCXip\nmjVrateuXSpVqpTppgDwAA5BAwBgAHvAAAAYwB4wAAAGEMAAABhAAAMAYAABDACAAQQwAAAGEMAA\nABjwfwyUUZAEIBzbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5636a42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gasto por partido / quantidade de senadores em cada partido.\n",
    "qtd_partido_df = qtd_partido_percent.to_frame()\n",
    "ndf = pandas.concat([qtd_partido_df, gasto_partido_percent], axis=1)\n",
    "ndf = ndf.rename(columns={\"congressperson_name\": \"% de senadores\", \"reimbursement_value\": \"% de gastos\"})\n",
    "ndf.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------\n",
    "\n",
    "## Proximos passos\n",
    "Com essas tecnicas podemos fazer uma analise dos dados do senado e tirar algumas conclusões.\n",
    "\n",
    "Agora podemos pegar os dados dos anos anteriores e compara-los com os desse ano. :)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}