Reembolso do Senado Federal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas, zipfile\n",
    "df = pandas.read_csv(\"federal-senate-2016\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26691, 10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 object\n",
      "dtypes: int64(2), object(8)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      73,6\n",
       "1    206,84\n",
       "2      6000\n",
       "3    407,48\n",
       "4       550\n",
       "Name: reimbursement_value, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '654,28'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-2401900b4e06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreimbursement_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.virtualenvs/data-analysis-senate/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/data-analysis-senate/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[1;32m   4002\u001b[0m         \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4003\u001b[0m         new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,\n\u001b[0;32m-> 4004\u001b[0;31m                                      **kwargs)\n\u001b[0m\u001b[1;32m   4005\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/data-analysis-senate/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   3455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3456\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3457\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'astype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3459\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/data-analysis-senate/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   3322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3323\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3324\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3325\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/data-analysis-senate/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m         return self._astype(dtype, copy=copy, errors=errors, values=values,\n\u001b[0;32m--> 544\u001b[0;31m                             **kwargs)\n\u001b[0m\u001b[1;32m    545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m     def _astype(self, dtype, copy=False, errors='raise', values=None,\n",
      "\u001b[0;32m~/.virtualenvs/data-analysis-senate/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, errors, values, klass, mgr, **kwargs)\u001b[0m\n\u001b[1;32m    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m                 \u001b[0;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/data-analysis-senate/lib/python3.6/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy)\u001b[0m\n\u001b[1;32m    701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    704\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '654,28'"
     ]
    }
   ],
   "source": [
    "df.reimbursement_value.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "5          40.00\n",
       "6        1218.76\n",
       "7        1761.34\n",
       "8         451.18\n",
       "9         923.30\n",
       "10        373.01\n",
       "11        552.74\n",
       "12       1276.43\n",
       "13       1469.05\n",
       "14         72.59\n",
       "15        315.60\n",
       "16       6000.00\n",
       "17        439.96\n",
       "18        550.00\n",
       "19        300.00\n",
       "20       1308.48\n",
       "21        754.25\n",
       "22        826.24\n",
       "23        862.97\n",
       "24       1280.42\n",
       "25       1655.00\n",
       "26        758.04\n",
       "27       1134.54\n",
       "28       1305.35\n",
       "29         71.10\n",
       "          ...   \n",
       "26661     145.57\n",
       "26662      50.00\n",
       "26663     176.00\n",
       "26664     100.00\n",
       "26665     271.41\n",
       "26666     195.39\n",
       "26667     160.96\n",
       "26668      90.00\n",
       "26669    1160.00\n",
       "26670    1974.00\n",
       "26671     894.60\n",
       "26672    1597.50\n",
       "26673    2440.32\n",
       "26674    1249.56\n",
       "26675     100.00\n",
       "26676     200.00\n",
       "26677     230.00\n",
       "26678     245.52\n",
       "26679     213.25\n",
       "26680     251.93\n",
       "26681     239.93\n",
       "26682     222.58\n",
       "26683     100.00\n",
       "26684      23.04\n",
       "26685     100.00\n",
       "26686     183.40\n",
       "26687      50.00\n",
       "26688    1055.41\n",
       "26689     390.18\n",
       "26690     654.28\n",
       "Name: reimbursement_value, Length: 26691, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.str.replace(\",\", \".\").astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      73,6\n",
       "1    206,84\n",
       "2      6000\n",
       "3    407,48\n",
       "4       550\n",
       "Name: reimbursement_value, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value = df.reimbursement_value.str.replace(\",\", \".\").astype(float)\n",
    "df.reimbursement_value.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>73.60</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>6000.00</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>550.00</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...                73.60  \n",
       "1  Pagamento de energia elétrica para o escritóri...               206.84  \n",
       "2  Despesa com pagamento de aluguel, utilizado pa...              6000.00  \n",
       "3  despesa com pagamento de telefonia para o escr...               407.48  \n",
       "4  Aquisição de publicação virtual de conteúdo jo...               550.00  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>reimbursement_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26691.0</td>\n",
       "      <td>26691.000000</td>\n",
       "      <td>26691.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.470908</td>\n",
       "      <td>947.484513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.257134</td>\n",
       "      <td>2733.820037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>324.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>956.865000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>256980.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year         month  reimbursement_value\n",
       "count  26691.0  26691.000000         26691.000000\n",
       "mean    2016.0      6.470908           947.484513\n",
       "std        0.0      3.257134          2733.820037\n",
       "min     2016.0      1.000000             0.010000\n",
       "25%     2016.0      4.000000           124.000000\n",
       "50%     2016.0      6.000000           324.790000\n",
       "75%     2016.0      9.000000           956.865000\n",
       "max     2016.0     12.000000        256980.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------\n",
    "\n",
    "E agora?\n",
    "====\n",
    "\n",
    "Temos um dataset minimo e em cima dele podemos criar algumas perguntas, como por exemplo:\n",
    "\n",
    "- [qual maior/menor reembolso?](#Qual-maior-e-o-menor-reembolso?)\n",
    "- [quem gastou mais durante 2016?](#Quem-gastou-mais?)\n",
    "- [como são os gastos por mês?](#como-são-os-gastos-por-mês?)\n",
    "- [quais partidos e estados gastaram mais?](#quais-partidos-e-estados-gastaram-mais?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "\n",
    "## Qual maior e o menor reembolso?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256980.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reimbursement_value.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "indice = df.reimbursement_value.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[indice]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indice = df.reimbursement_value.idxmin()\n",
    "df.loc[indice]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------\n",
    "## Quem gastou mais?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "por_pessoa = df.groupby(\"congressperson_name\")\n",
    "por_pessoa.reimbursement_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">reimbursement_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>congressperson_name</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>301970.88</td>\n",
       "      <td>2156.934857</td>\n",
       "      <td>2864.690150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALOYSIO NUNES FERREIRA</th>\n",
       "      <td>285917.60</td>\n",
       "      <td>661.846296</td>\n",
       "      <td>1357.902019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALVARO DIAS</th>\n",
       "      <td>132970.95</td>\n",
       "      <td>886.473000</td>\n",
       "      <td>435.867230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANA AMÉLIA</th>\n",
       "      <td>167090.43</td>\n",
       "      <td>420.882695</td>\n",
       "      <td>498.887655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTONIO ANASTASIA</th>\n",
       "      <td>312460.91</td>\n",
       "      <td>1144.545458</td>\n",
       "      <td>1532.358356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTÔNIO CARLOS VALADARES</th>\n",
       "      <td>265583.27</td>\n",
       "      <td>1713.440452</td>\n",
       "      <td>1650.345721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARMANDO MONTEIRO</th>\n",
       "      <td>156706.42</td>\n",
       "      <td>675.458707</td>\n",
       "      <td>1027.924855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATAÍDES OLIVEIRA</th>\n",
       "      <td>299616.21</td>\n",
       "      <td>1712.092629</td>\n",
       "      <td>3079.333122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AÉCIO NEVES</th>\n",
       "      <td>295335.85</td>\n",
       "      <td>962.006026</td>\n",
       "      <td>1854.973271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BENEDITO DE LIRA</th>\n",
       "      <td>172961.28</td>\n",
       "      <td>645.377910</td>\n",
       "      <td>856.757007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLAIRO MAGGI</th>\n",
       "      <td>108393.18</td>\n",
       "      <td>1372.065570</td>\n",
       "      <td>2252.756787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIDINHO SANTOS</th>\n",
       "      <td>232399.98</td>\n",
       "      <td>1489.743462</td>\n",
       "      <td>2054.534316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIRO NOGUEIRA</th>\n",
       "      <td>440047.81</td>\n",
       "      <td>1341.609177</td>\n",
       "      <td>1955.232902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRISTOVAM BUARQUE</th>\n",
       "      <td>107600.00</td>\n",
       "      <td>15371.428571</td>\n",
       "      <td>12115.377871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CÁSSIO CUNHA LIMA</th>\n",
       "      <td>170378.90</td>\n",
       "      <td>668.152549</td>\n",
       "      <td>1016.598787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DALIRIO BEBER</th>\n",
       "      <td>214115.91</td>\n",
       "      <td>393.595423</td>\n",
       "      <td>574.972304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAVI ALCOLUMBRE</th>\n",
       "      <td>510076.07</td>\n",
       "      <td>3835.158421</td>\n",
       "      <td>22271.242467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DECA</th>\n",
       "      <td>133912.76</td>\n",
       "      <td>1046.193438</td>\n",
       "      <td>1405.788875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DONIZETI NOGUEIRA</th>\n",
       "      <td>109265.86</td>\n",
       "      <td>600.361868</td>\n",
       "      <td>1170.831507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOUGLAS CINTRA</th>\n",
       "      <td>74002.63</td>\n",
       "      <td>685.209537</td>\n",
       "      <td>972.108468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DÁRIO BERGER</th>\n",
       "      <td>319814.09</td>\n",
       "      <td>723.561290</td>\n",
       "      <td>1038.533209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDISON LOBÃO</th>\n",
       "      <td>151556.51</td>\n",
       "      <td>3294.706739</td>\n",
       "      <td>5240.292523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO AMORIM</th>\n",
       "      <td>306258.14</td>\n",
       "      <td>648.851992</td>\n",
       "      <td>817.621372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO BRAGA</th>\n",
       "      <td>293426.22</td>\n",
       "      <td>2529.536379</td>\n",
       "      <td>2790.122521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO LOPES</th>\n",
       "      <td>110289.65</td>\n",
       "      <td>829.245489</td>\n",
       "      <td>2961.260094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELMANO FÉRRER</th>\n",
       "      <td>428961.30</td>\n",
       "      <td>1469.045548</td>\n",
       "      <td>1644.973324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO BEZERRA COELHO</th>\n",
       "      <td>435141.96</td>\n",
       "      <td>724.029884</td>\n",
       "      <td>1459.551181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>420471.01</td>\n",
       "      <td>20022.429048</td>\n",
       "      <td>7545.173689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEXA RIBEIRO</th>\n",
       "      <td>364022.34</td>\n",
       "      <td>1067.514194</td>\n",
       "      <td>2079.523837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA BEZERRA</th>\n",
       "      <td>335308.94</td>\n",
       "      <td>528.045575</td>\n",
       "      <td>685.527010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINTO ITAMARATY</th>\n",
       "      <td>85377.22</td>\n",
       "      <td>2587.188485</td>\n",
       "      <td>3408.834811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIMUNDO LIRA</th>\n",
       "      <td>174400.00</td>\n",
       "      <td>1726.732673</td>\n",
       "      <td>481.433503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RANDOLFE RODRIGUES</th>\n",
       "      <td>471847.84</td>\n",
       "      <td>1429.841939</td>\n",
       "      <td>1686.607948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGINA SOUSA</th>\n",
       "      <td>311987.13</td>\n",
       "      <td>957.015736</td>\n",
       "      <td>1845.580891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENAN CALHEIROS</th>\n",
       "      <td>67359.63</td>\n",
       "      <td>962.280429</td>\n",
       "      <td>1131.586370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RICARDO FERRAÇO</th>\n",
       "      <td>279506.91</td>\n",
       "      <td>944.280101</td>\n",
       "      <td>1568.568888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RICARDO FRANCO</th>\n",
       "      <td>75385.69</td>\n",
       "      <td>919.337683</td>\n",
       "      <td>1401.637879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO MUNIZ</th>\n",
       "      <td>188559.77</td>\n",
       "      <td>528.178627</td>\n",
       "      <td>727.286524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO REQUIÃO</th>\n",
       "      <td>309428.14</td>\n",
       "      <td>603.173762</td>\n",
       "      <td>962.259881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO ROCHA</th>\n",
       "      <td>364903.02</td>\n",
       "      <td>3145.715690</td>\n",
       "      <td>3816.869175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROMERO JUCÁ</th>\n",
       "      <td>446448.98</td>\n",
       "      <td>1023.965550</td>\n",
       "      <td>1369.313814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROMÁRIO</th>\n",
       "      <td>341345.94</td>\n",
       "      <td>1906.960559</td>\n",
       "      <td>2608.047684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RONALDO CAIADO</th>\n",
       "      <td>235825.91</td>\n",
       "      <td>407.298636</td>\n",
       "      <td>669.208603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROSE DE FREITAS</th>\n",
       "      <td>396088.44</td>\n",
       "      <td>1128.457094</td>\n",
       "      <td>4304.023370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDRA BRAGA</th>\n",
       "      <td>117107.15</td>\n",
       "      <td>2856.271951</td>\n",
       "      <td>2950.018999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMONE TEBET</th>\n",
       "      <td>200693.60</td>\n",
       "      <td>891.971556</td>\n",
       "      <td>832.343717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>463456.45</td>\n",
       "      <td>3707.651600</td>\n",
       "      <td>11757.386179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TASSO JEREISSATI</th>\n",
       "      <td>184004.32</td>\n",
       "      <td>3833.423333</td>\n",
       "      <td>5596.315142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>488678.92</td>\n",
       "      <td>833.923072</td>\n",
       "      <td>1993.159838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>415018.64</td>\n",
       "      <td>1343.102395</td>\n",
       "      <td>2122.107585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>477079.76</td>\n",
       "      <td>1716.114245</td>\n",
       "      <td>2601.589275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICENTINHO ALVES</th>\n",
       "      <td>299890.52</td>\n",
       "      <td>4052.574595</td>\n",
       "      <td>5467.742757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIRGINIO DE CARVALHO</th>\n",
       "      <td>46418.49</td>\n",
       "      <td>1365.249706</td>\n",
       "      <td>1270.231996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALDEMIR MOKA</th>\n",
       "      <td>265638.06</td>\n",
       "      <td>633.981050</td>\n",
       "      <td>821.418792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALTER PINHEIRO</th>\n",
       "      <td>78455.02</td>\n",
       "      <td>622.658889</td>\n",
       "      <td>839.184778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLINGTON FAGUNDES</th>\n",
       "      <td>415154.23</td>\n",
       "      <td>448.815384</td>\n",
       "      <td>1619.613354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WILDER MORAIS</th>\n",
       "      <td>252542.40</td>\n",
       "      <td>693.797802</td>\n",
       "      <td>1513.942453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIRLANDE DA LUZ</th>\n",
       "      <td>11298.25</td>\n",
       "      <td>1129.825000</td>\n",
       "      <td>594.058339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEZÉ PERRELLA</th>\n",
       "      <td>322433.80</td>\n",
       "      <td>810.135176</td>\n",
       "      <td>4199.936436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÂNGELA PORTELA</th>\n",
       "      <td>429605.98</td>\n",
       "      <td>2512.315673</td>\n",
       "      <td>4580.254034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         reimbursement_value                            \n",
       "                                         sum          mean           std\n",
       "congressperson_name                                                     \n",
       "ACIR GURGACZ                       301970.88   2156.934857   2864.690150\n",
       "ALOYSIO NUNES FERREIRA             285917.60    661.846296   1357.902019\n",
       "ALVARO DIAS                        132970.95    886.473000    435.867230\n",
       "ANA AMÉLIA                         167090.43    420.882695    498.887655\n",
       "ANTONIO ANASTASIA                  312460.91   1144.545458   1532.358356\n",
       "ANTÔNIO CARLOS VALADARES           265583.27   1713.440452   1650.345721\n",
       "ARMANDO MONTEIRO                   156706.42    675.458707   1027.924855\n",
       "ATAÍDES OLIVEIRA                   299616.21   1712.092629   3079.333122\n",
       "AÉCIO NEVES                        295335.85    962.006026   1854.973271\n",
       "BENEDITO DE LIRA                   172961.28    645.377910    856.757007\n",
       "BLAIRO MAGGI                       108393.18   1372.065570   2252.756787\n",
       "CIDINHO SANTOS                     232399.98   1489.743462   2054.534316\n",
       "CIRO NOGUEIRA                      440047.81   1341.609177   1955.232902\n",
       "CRISTOVAM BUARQUE                  107600.00  15371.428571  12115.377871\n",
       "CÁSSIO CUNHA LIMA                  170378.90    668.152549   1016.598787\n",
       "DALIRIO BEBER                      214115.91    393.595423    574.972304\n",
       "DAVI ALCOLUMBRE                    510076.07   3835.158421  22271.242467\n",
       "DECA                               133912.76   1046.193438   1405.788875\n",
       "DONIZETI NOGUEIRA                  109265.86    600.361868   1170.831507\n",
       "DOUGLAS CINTRA                      74002.63    685.209537    972.108468\n",
       "DÁRIO BERGER                       319814.09    723.561290   1038.533209\n",
       "EDISON LOBÃO                       151556.51   3294.706739   5240.292523\n",
       "EDUARDO AMORIM                     306258.14    648.851992    817.621372\n",
       "EDUARDO BRAGA                      293426.22   2529.536379   2790.122521\n",
       "EDUARDO LOPES                      110289.65    829.245489   2961.260094\n",
       "ELMANO FÉRRER                      428961.30   1469.045548   1644.973324\n",
       "FERNANDO BEZERRA COELHO            435141.96    724.029884   1459.551181\n",
       "FERNANDO COLLOR                    420471.01  20022.429048   7545.173689\n",
       "FLEXA RIBEIRO                      364022.34   1067.514194   2079.523837\n",
       "FÁTIMA BEZERRA                     335308.94    528.045575    685.527010\n",
       "...                                      ...           ...           ...\n",
       "PINTO ITAMARATY                     85377.22   2587.188485   3408.834811\n",
       "RAIMUNDO LIRA                      174400.00   1726.732673    481.433503\n",
       "RANDOLFE RODRIGUES                 471847.84   1429.841939   1686.607948\n",
       "REGINA SOUSA                       311987.13    957.015736   1845.580891\n",
       "RENAN CALHEIROS                     67359.63    962.280429   1131.586370\n",
       "RICARDO FERRAÇO                    279506.91    944.280101   1568.568888\n",
       "RICARDO FRANCO                      75385.69    919.337683   1401.637879\n",
       "ROBERTO MUNIZ                      188559.77    528.178627    727.286524\n",
       "ROBERTO REQUIÃO                    309428.14    603.173762    962.259881\n",
       "ROBERTO ROCHA                      364903.02   3145.715690   3816.869175\n",
       "ROMERO JUCÁ                        446448.98   1023.965550   1369.313814\n",
       "ROMÁRIO                            341345.94   1906.960559   2608.047684\n",
       "RONALDO CAIADO                     235825.91    407.298636    669.208603\n",
       "ROSE DE FREITAS                    396088.44   1128.457094   4304.023370\n",
       "SANDRA BRAGA                       117107.15   2856.271951   2950.018999\n",
       "SIMONE TEBET                       200693.60    891.971556    832.343717\n",
       "SÉRGIO PETECÃO                     463456.45   3707.651600  11757.386179\n",
       "TASSO JEREISSATI                   184004.32   3833.423333   5596.315142\n",
       "TELMÁRIO MOTA                      488678.92    833.923072   1993.159838\n",
       "VALDIR RAUPP                       415018.64   1343.102395   2122.107585\n",
       "VANESSA GRAZZIOTIN                 477079.76   1716.114245   2601.589275\n",
       "VICENTINHO ALVES                   299890.52   4052.574595   5467.742757\n",
       "VIRGINIO DE CARVALHO                46418.49   1365.249706   1270.231996\n",
       "WALDEMIR MOKA                      265638.06    633.981050    821.418792\n",
       "WALTER PINHEIRO                     78455.02    622.658889    839.184778\n",
       "WELLINGTON FAGUNDES                415154.23    448.815384   1619.613354\n",
       "WILDER MORAIS                      252542.40    693.797802   1513.942453\n",
       "WIRLANDE DA LUZ                     11298.25   1129.825000    594.058339\n",
       "ZEZÉ PERRELLA                      322433.80    810.135176   4199.936436\n",
       "ÂNGELA PORTELA                     429605.98   2512.315673   4580.254034\n",
       "\n",
       "[93 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "por_pessoa.agg({\"reimbursement_value\": [sum, numpy.mean, numpy.std]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">reimbursement_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DAVI ALCOLUMBRE</th>\n",
       "      <td>510076.07</td>\n",
       "      <td>3835.158421</td>\n",
       "      <td>22271.242467</td>\n",
       "      <td>256980.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OMAR AZIZ</th>\n",
       "      <td>390372.08</td>\n",
       "      <td>21687.337778</td>\n",
       "      <td>16216.921826</td>\n",
       "      <td>61600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRISTOVAM BUARQUE</th>\n",
       "      <td>107600.00</td>\n",
       "      <td>15371.428571</td>\n",
       "      <td>12115.377871</td>\n",
       "      <td>40000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SÉRGIO PETECÃO</th>\n",
       "      <td>463456.45</td>\n",
       "      <td>3707.651600</td>\n",
       "      <td>11757.386179</td>\n",
       "      <td>80000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FERNANDO COLLOR</th>\n",
       "      <td>420471.01</td>\n",
       "      <td>20022.429048</td>\n",
       "      <td>7545.173689</td>\n",
       "      <td>28922.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TASSO JEREISSATI</th>\n",
       "      <td>184004.32</td>\n",
       "      <td>3833.423333</td>\n",
       "      <td>5596.315142</td>\n",
       "      <td>14400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICENTINHO ALVES</th>\n",
       "      <td>299890.52</td>\n",
       "      <td>4052.574595</td>\n",
       "      <td>5467.742757</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDISON LOBÃO</th>\n",
       "      <td>151556.51</td>\n",
       "      <td>3294.706739</td>\n",
       "      <td>5240.292523</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HÉLIO JOSÉ</th>\n",
       "      <td>244198.49</td>\n",
       "      <td>1453.562440</td>\n",
       "      <td>4673.996864</td>\n",
       "      <td>35000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÂNGELA PORTELA</th>\n",
       "      <td>429605.98</td>\n",
       "      <td>2512.315673</td>\n",
       "      <td>4580.254034</td>\n",
       "      <td>28000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROSE DE FREITAS</th>\n",
       "      <td>396088.44</td>\n",
       "      <td>1128.457094</td>\n",
       "      <td>4304.023370</td>\n",
       "      <td>79680.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEZÉ PERRELLA</th>\n",
       "      <td>322433.80</td>\n",
       "      <td>810.135176</td>\n",
       "      <td>4199.936436</td>\n",
       "      <td>80000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO ROCHA</th>\n",
       "      <td>364903.02</td>\n",
       "      <td>3145.715690</td>\n",
       "      <td>3816.869175</td>\n",
       "      <td>22800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINTO ITAMARATY</th>\n",
       "      <td>85377.22</td>\n",
       "      <td>2587.188485</td>\n",
       "      <td>3408.834811</td>\n",
       "      <td>17500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATAÍDES OLIVEIRA</th>\n",
       "      <td>299616.21</td>\n",
       "      <td>1712.092629</td>\n",
       "      <td>3079.333122</td>\n",
       "      <td>17500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO LOPES</th>\n",
       "      <td>110289.65</td>\n",
       "      <td>829.245489</td>\n",
       "      <td>2961.260094</td>\n",
       "      <td>33000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDRA BRAGA</th>\n",
       "      <td>117107.15</td>\n",
       "      <td>2856.271951</td>\n",
       "      <td>2950.018999</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACIR GURGACZ</th>\n",
       "      <td>301970.88</td>\n",
       "      <td>2156.934857</td>\n",
       "      <td>2864.690150</td>\n",
       "      <td>19800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO BRAGA</th>\n",
       "      <td>293426.22</td>\n",
       "      <td>2529.536379</td>\n",
       "      <td>2790.122521</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARTA SUPLICY</th>\n",
       "      <td>291299.63</td>\n",
       "      <td>1288.936416</td>\n",
       "      <td>2699.032299</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROMÁRIO</th>\n",
       "      <td>341345.94</td>\n",
       "      <td>1906.960559</td>\n",
       "      <td>2608.047684</td>\n",
       "      <td>14900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VANESSA GRAZZIOTIN</th>\n",
       "      <td>477079.76</td>\n",
       "      <td>1716.114245</td>\n",
       "      <td>2601.589275</td>\n",
       "      <td>17000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLEISI HOFFMANN</th>\n",
       "      <td>343492.72</td>\n",
       "      <td>740.286034</td>\n",
       "      <td>2591.309074</td>\n",
       "      <td>44500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLADSON CAMELI</th>\n",
       "      <td>459977.83</td>\n",
       "      <td>1789.797004</td>\n",
       "      <td>2589.188238</td>\n",
       "      <td>19680.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLAIRO MAGGI</th>\n",
       "      <td>108393.18</td>\n",
       "      <td>1372.065570</td>\n",
       "      <td>2252.756787</td>\n",
       "      <td>7800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ AGRIPINO</th>\n",
       "      <td>306939.26</td>\n",
       "      <td>1993.112078</td>\n",
       "      <td>2130.336966</td>\n",
       "      <td>7621.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALDIR RAUPP</th>\n",
       "      <td>415018.64</td>\n",
       "      <td>1343.102395</td>\n",
       "      <td>2122.107585</td>\n",
       "      <td>20000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEXA RIBEIRO</th>\n",
       "      <td>364022.34</td>\n",
       "      <td>1067.514194</td>\n",
       "      <td>2079.523837</td>\n",
       "      <td>18000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIDINHO SANTOS</th>\n",
       "      <td>232399.98</td>\n",
       "      <td>1489.743462</td>\n",
       "      <td>2054.534316</td>\n",
       "      <td>8000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELMÁRIO MOTA</th>\n",
       "      <td>488678.92</td>\n",
       "      <td>833.923072</td>\n",
       "      <td>1993.159838</td>\n",
       "      <td>18000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASIER MARTINS</th>\n",
       "      <td>240840.73</td>\n",
       "      <td>937.123463</td>\n",
       "      <td>1176.270302</td>\n",
       "      <td>4500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DONIZETI NOGUEIRA</th>\n",
       "      <td>109265.86</td>\n",
       "      <td>600.361868</td>\n",
       "      <td>1170.831507</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENAN CALHEIROS</th>\n",
       "      <td>67359.63</td>\n",
       "      <td>962.280429</td>\n",
       "      <td>1131.586370</td>\n",
       "      <td>3907.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JORGE VIANA</th>\n",
       "      <td>363350.03</td>\n",
       "      <td>784.773283</td>\n",
       "      <td>1111.152005</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAGNO MALTA</th>\n",
       "      <td>378840.79</td>\n",
       "      <td>1353.002821</td>\n",
       "      <td>1066.415158</td>\n",
       "      <td>9420.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DÁRIO BERGER</th>\n",
       "      <td>319814.09</td>\n",
       "      <td>723.561290</td>\n",
       "      <td>1038.533209</td>\n",
       "      <td>4850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARMANDO MONTEIRO</th>\n",
       "      <td>156706.42</td>\n",
       "      <td>675.458707</td>\n",
       "      <td>1027.924855</td>\n",
       "      <td>7420.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ SERRA</th>\n",
       "      <td>114621.90</td>\n",
       "      <td>573.109500</td>\n",
       "      <td>1025.189298</td>\n",
       "      <td>13500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CÁSSIO CUNHA LIMA</th>\n",
       "      <td>170378.90</td>\n",
       "      <td>668.152549</td>\n",
       "      <td>1016.598787</td>\n",
       "      <td>7500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOUGLAS CINTRA</th>\n",
       "      <td>74002.63</td>\n",
       "      <td>685.209537</td>\n",
       "      <td>972.108468</td>\n",
       "      <td>5000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO REQUIÃO</th>\n",
       "      <td>309428.14</td>\n",
       "      <td>603.173762</td>\n",
       "      <td>962.259881</td>\n",
       "      <td>6199.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAULO BAUER</th>\n",
       "      <td>394455.84</td>\n",
       "      <td>498.679949</td>\n",
       "      <td>932.483707</td>\n",
       "      <td>5700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARIBALDI ALVES FILHO</th>\n",
       "      <td>196293.49</td>\n",
       "      <td>752.082337</td>\n",
       "      <td>924.401415</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINDBERGH FARIAS</th>\n",
       "      <td>377653.66</td>\n",
       "      <td>1082.102178</td>\n",
       "      <td>900.506884</td>\n",
       "      <td>4500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BENEDITO DE LIRA</th>\n",
       "      <td>172961.28</td>\n",
       "      <td>645.377910</td>\n",
       "      <td>856.757007</td>\n",
       "      <td>4000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALTER PINHEIRO</th>\n",
       "      <td>78455.02</td>\n",
       "      <td>622.658889</td>\n",
       "      <td>839.184778</td>\n",
       "      <td>4125.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMONE TEBET</th>\n",
       "      <td>200693.60</td>\n",
       "      <td>891.971556</td>\n",
       "      <td>832.343717</td>\n",
       "      <td>3923.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALDEMIR MOKA</th>\n",
       "      <td>265638.06</td>\n",
       "      <td>633.981050</td>\n",
       "      <td>821.418792</td>\n",
       "      <td>5000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUARDO AMORIM</th>\n",
       "      <td>306258.14</td>\n",
       "      <td>648.851992</td>\n",
       "      <td>817.621372</td>\n",
       "      <td>3950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSÉ MARANHÃO</th>\n",
       "      <td>250753.45</td>\n",
       "      <td>957.074237</td>\n",
       "      <td>741.710270</td>\n",
       "      <td>3152.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBERTO MUNIZ</th>\n",
       "      <td>188559.77</td>\n",
       "      <td>528.178627</td>\n",
       "      <td>727.286524</td>\n",
       "      <td>4125.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÁTIMA BEZERRA</th>\n",
       "      <td>335308.94</td>\n",
       "      <td>528.045575</td>\n",
       "      <td>685.527010</td>\n",
       "      <td>7680.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RONALDO CAIADO</th>\n",
       "      <td>235825.91</td>\n",
       "      <td>407.298636</td>\n",
       "      <td>669.208603</td>\n",
       "      <td>4483.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LÍDICE DA MATA</th>\n",
       "      <td>262078.34</td>\n",
       "      <td>499.196838</td>\n",
       "      <td>653.637694</td>\n",
       "      <td>3600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIRLANDE DA LUZ</th>\n",
       "      <td>11298.25</td>\n",
       "      <td>1129.825000</td>\n",
       "      <td>594.058339</td>\n",
       "      <td>2234.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DALIRIO BEBER</th>\n",
       "      <td>214115.91</td>\n",
       "      <td>393.595423</td>\n",
       "      <td>574.972304</td>\n",
       "      <td>3500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANA AMÉLIA</th>\n",
       "      <td>167090.43</td>\n",
       "      <td>420.882695</td>\n",
       "      <td>498.887655</td>\n",
       "      <td>2759.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIMUNDO LIRA</th>\n",
       "      <td>174400.00</td>\n",
       "      <td>1726.732673</td>\n",
       "      <td>481.433503</td>\n",
       "      <td>2000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALVARO DIAS</th>\n",
       "      <td>132970.95</td>\n",
       "      <td>886.473000</td>\n",
       "      <td>435.867230</td>\n",
       "      <td>2196.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARIA DO CARMO ALVES</th>\n",
       "      <td>2252.40</td>\n",
       "      <td>563.100000</td>\n",
       "      <td>143.294496</td>\n",
       "      <td>690.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      reimbursement_value                              \\\n",
       "                                      sum          mean           std   \n",
       "congressperson_name                                                     \n",
       "DAVI ALCOLUMBRE                 510076.07   3835.158421  22271.242467   \n",
       "OMAR AZIZ                       390372.08  21687.337778  16216.921826   \n",
       "CRISTOVAM BUARQUE               107600.00  15371.428571  12115.377871   \n",
       "SÉRGIO PETECÃO                  463456.45   3707.651600  11757.386179   \n",
       "FERNANDO COLLOR                 420471.01  20022.429048   7545.173689   \n",
       "TASSO JEREISSATI                184004.32   3833.423333   5596.315142   \n",
       "VICENTINHO ALVES                299890.52   4052.574595   5467.742757   \n",
       "EDISON LOBÃO                    151556.51   3294.706739   5240.292523   \n",
       "HÉLIO JOSÉ                      244198.49   1453.562440   4673.996864   \n",
       "ÂNGELA PORTELA                  429605.98   2512.315673   4580.254034   \n",
       "ROSE DE FREITAS                 396088.44   1128.457094   4304.023370   \n",
       "ZEZÉ PERRELLA                   322433.80    810.135176   4199.936436   \n",
       "ROBERTO ROCHA                   364903.02   3145.715690   3816.869175   \n",
       "PINTO ITAMARATY                  85377.22   2587.188485   3408.834811   \n",
       "ATAÍDES OLIVEIRA                299616.21   1712.092629   3079.333122   \n",
       "EDUARDO LOPES                   110289.65    829.245489   2961.260094   \n",
       "SANDRA BRAGA                    117107.15   2856.271951   2950.018999   \n",
       "ACIR GURGACZ                    301970.88   2156.934857   2864.690150   \n",
       "EDUARDO BRAGA                   293426.22   2529.536379   2790.122521   \n",
       "MARTA SUPLICY                   291299.63   1288.936416   2699.032299   \n",
       "ROMÁRIO                         341345.94   1906.960559   2608.047684   \n",
       "VANESSA GRAZZIOTIN              477079.76   1716.114245   2601.589275   \n",
       "GLEISI HOFFMANN                 343492.72    740.286034   2591.309074   \n",
       "GLADSON CAMELI                  459977.83   1789.797004   2589.188238   \n",
       "BLAIRO MAGGI                    108393.18   1372.065570   2252.756787   \n",
       "JOSÉ AGRIPINO                   306939.26   1993.112078   2130.336966   \n",
       "VALDIR RAUPP                    415018.64   1343.102395   2122.107585   \n",
       "FLEXA RIBEIRO                   364022.34   1067.514194   2079.523837   \n",
       "CIDINHO SANTOS                  232399.98   1489.743462   2054.534316   \n",
       "TELMÁRIO MOTA                   488678.92    833.923072   1993.159838   \n",
       "...                                   ...           ...           ...   \n",
       "LASIER MARTINS                  240840.73    937.123463   1176.270302   \n",
       "DONIZETI NOGUEIRA               109265.86    600.361868   1170.831507   \n",
       "RENAN CALHEIROS                  67359.63    962.280429   1131.586370   \n",
       "JORGE VIANA                     363350.03    784.773283   1111.152005   \n",
       "MAGNO MALTA                     378840.79   1353.002821   1066.415158   \n",
       "DÁRIO BERGER                    319814.09    723.561290   1038.533209   \n",
       "ARMANDO MONTEIRO                156706.42    675.458707   1027.924855   \n",
       "JOSÉ SERRA                      114621.90    573.109500   1025.189298   \n",
       "CÁSSIO CUNHA LIMA               170378.90    668.152549   1016.598787   \n",
       "DOUGLAS CINTRA                   74002.63    685.209537    972.108468   \n",
       "ROBERTO REQUIÃO                 309428.14    603.173762    962.259881   \n",
       "PAULO BAUER                     394455.84    498.679949    932.483707   \n",
       "GARIBALDI ALVES FILHO           196293.49    752.082337    924.401415   \n",
       "LINDBERGH FARIAS                377653.66   1082.102178    900.506884   \n",
       "BENEDITO DE LIRA                172961.28    645.377910    856.757007   \n",
       "WALTER PINHEIRO                  78455.02    622.658889    839.184778   \n",
       "SIMONE TEBET                    200693.60    891.971556    832.343717   \n",
       "WALDEMIR MOKA                   265638.06    633.981050    821.418792   \n",
       "EDUARDO AMORIM                  306258.14    648.851992    817.621372   \n",
       "JOSÉ MARANHÃO                   250753.45    957.074237    741.710270   \n",
       "ROBERTO MUNIZ                   188559.77    528.178627    727.286524   \n",
       "FÁTIMA BEZERRA                  335308.94    528.045575    685.527010   \n",
       "RONALDO CAIADO                  235825.91    407.298636    669.208603   \n",
       "LÍDICE DA MATA                  262078.34    499.196838    653.637694   \n",
       "WIRLANDE DA LUZ                  11298.25   1129.825000    594.058339   \n",
       "DALIRIO BEBER                   214115.91    393.595423    574.972304   \n",
       "ANA AMÉLIA                      167090.43    420.882695    498.887655   \n",
       "RAIMUNDO LIRA                   174400.00   1726.732673    481.433503   \n",
       "ALVARO DIAS                     132970.95    886.473000    435.867230   \n",
       "MARIA DO CARMO ALVES              2252.40    563.100000    143.294496   \n",
       "\n",
       "                                  \n",
       "                             max  \n",
       "congressperson_name               \n",
       "DAVI ALCOLUMBRE        256980.00  \n",
       "OMAR AZIZ               61600.00  \n",
       "CRISTOVAM BUARQUE       40000.00  \n",
       "SÉRGIO PETECÃO          80000.00  \n",
       "FERNANDO COLLOR         28922.72  \n",
       "TASSO JEREISSATI        14400.00  \n",
       "VICENTINHO ALVES        30000.00  \n",
       "EDISON LOBÃO            12000.00  \n",
       "HÉLIO JOSÉ              35000.00  \n",
       "ÂNGELA PORTELA          28000.00  \n",
       "ROSE DE FREITAS         79680.00  \n",
       "ZEZÉ PERRELLA           80000.00  \n",
       "ROBERTO ROCHA           22800.00  \n",
       "PINTO ITAMARATY         17500.00  \n",
       "ATAÍDES OLIVEIRA        17500.00  \n",
       "EDUARDO LOPES           33000.00  \n",
       "SANDRA BRAGA            12000.00  \n",
       "ACIR GURGACZ            19800.00  \n",
       "EDUARDO BRAGA           12000.00  \n",
       "MARTA SUPLICY           12000.00  \n",
       "ROMÁRIO                 14900.00  \n",
       "VANESSA GRAZZIOTIN      17000.00  \n",
       "GLEISI HOFFMANN         44500.00  \n",
       "GLADSON CAMELI          19680.23  \n",
       "BLAIRO MAGGI             7800.00  \n",
       "JOSÉ AGRIPINO            7621.85  \n",
       "VALDIR RAUPP            20000.00  \n",
       "FLEXA RIBEIRO           18000.00  \n",
       "CIDINHO SANTOS           8000.00  \n",
       "TELMÁRIO MOTA           18000.00  \n",
       "...                          ...  \n",
       "LASIER MARTINS           4500.00  \n",
       "DONIZETI NOGUEIRA        6000.00  \n",
       "RENAN CALHEIROS          3907.30  \n",
       "JORGE VIANA             10000.00  \n",
       "MAGNO MALTA              9420.00  \n",
       "DÁRIO BERGER             4850.00  \n",
       "ARMANDO MONTEIRO         7420.00  \n",
       "JOSÉ SERRA              13500.00  \n",
       "CÁSSIO CUNHA LIMA        7500.00  \n",
       "DOUGLAS CINTRA           5000.00  \n",
       "ROBERTO REQUIÃO          6199.95  \n",
       "PAULO BAUER              5700.00  \n",
       "GARIBALDI ALVES FILHO    6000.00  \n",
       "LINDBERGH FARIAS         4500.00  \n",
       "BENEDITO DE LIRA         4000.00  \n",
       "WALTER PINHEIRO          4125.22  \n",
       "SIMONE TEBET             3923.94  \n",
       "WALDEMIR MOKA            5000.00  \n",
       "EDUARDO AMORIM           3950.00  \n",
       "JOSÉ MARANHÃO            3152.00  \n",
       "ROBERTO MUNIZ            4125.22  \n",
       "FÁTIMA BEZERRA           7680.60  \n",
       "RONALDO CAIADO           4483.60  \n",
       "LÍDICE DA MATA           3600.00  \n",
       "WIRLANDE DA LUZ          2234.37  \n",
       "DALIRIO BEBER            3500.00  \n",
       "ANA AMÉLIA               2759.21  \n",
       "RAIMUNDO LIRA            2000.00  \n",
       "ALVARO DIAS              2196.25  \n",
       "MARIA DO CARMO ALVES      690.56  \n",
       "\n",
       "[93 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "por_pessoa.agg({\"reimbursement_value\": [sum, numpy.mean, numpy.std, max]}).sort_values([\n",
    "        (\"reimbursement_value\", \"std\"),\n",
    "        (\"reimbursement_value\", \"sum\")\n",
    "    ],\n",
    "    ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "expense_type\n",
       "Locomotion, lodging, food, fuels and lubricants                                                                                                         34\n",
       "National air, water and land transport                                                                                                                  55\n",
       "Publicity of parliamentary activity                                                                                                                      2\n",
       "Recruitment of consultancies, advisory services, research, technical work and other services in support of the exercise of the parliamentary mandate     1\n",
       "Rent of real estate for political office, comprising expenses concerning them                                                                           41\n",
       "Name: reimbursement_value, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gastador = df[df[\"congressperson_name\"] == \"DAVI ALCOLUMBRE\"]\n",
    "gastador.groupby(\"expense_type\").reimbursement_value.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1136d8a20>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VtfdJQwYN/F2f3s+EzlLjp7m3J0NnkLQ3AvhV6BAiIiIiItsaFXbSmJkdbGZz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\nCQo1AAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0c84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "gastador.groupby(\"expense_type\").reimbursement_value.sum().plot(kind=\"pie\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------\n",
    "## como são os gastos por mês?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1136eea20>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IeETSSuCfSxf2jwLOi4inJD0n6XDgRuBDwMV9XV4b+qr9ID8z61p/rsE0Ausk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\nZlk4wZiZWRb/H4dhUKt8sH6MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11371dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column=\"reimbursement_value\", by=\"month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113a652b0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a6d0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "por_mes = df.groupby(\"month\")\n",
    "por_mes.reimbursement_value.sum().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expense_type</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Acquisition of consumables for use in the political office, including acquisition or leasing of software, postal expenses, acquisition of publications, rental of furniture and equipment</th>\n",
       "      <td>72464.58</td>\n",
       "      <td>95622.67</td>\n",
       "      <td>86689.23</td>\n",
       "      <td>89751.24</td>\n",
       "      <td>84847.06</td>\n",
       "      <td>90025.80</td>\n",
       "      <td>100578.11</td>\n",
       "      <td>88666.88</td>\n",
       "      <td>82566.49</td>\n",
       "      <td>83382.94</td>\n",
       "      <td>85632.50</td>\n",
       "      <td>93770.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Locomotion, lodging, food, fuels and lubricants</th>\n",
       "      <td>373081.30</td>\n",
       "      <td>470910.65</td>\n",
       "      <td>526333.34</td>\n",
       "      <td>471441.48</td>\n",
       "      <td>467726.59</td>\n",
       "      <td>480589.29</td>\n",
       "      <td>622530.60</td>\n",
       "      <td>529582.56</td>\n",
       "      <td>620526.43</td>\n",
       "      <td>491455.90</td>\n",
       "      <td>415616.98</td>\n",
       "      <td>446097.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>National air, water and land transport</th>\n",
       "      <td>190543.44</td>\n",
       "      <td>451277.90</td>\n",
       "      <td>635800.52</td>\n",
       "      <td>556828.37</td>\n",
       "      <td>546786.53</td>\n",
       "      <td>603512.29</td>\n",
       "      <td>505547.72</td>\n",
       "      <td>603700.31</td>\n",
       "      <td>488860.02</td>\n",
       "      <td>568453.54</td>\n",
       "      <td>606522.27</td>\n",
       "      <td>391910.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private Security Services</th>\n",
       "      <td>52280.03</td>\n",
       "      <td>51375.12</td>\n",
       "      <td>49677.47</td>\n",
       "      <td>40010.08</td>\n",
       "      <td>44902.60</td>\n",
       "      <td>45015.33</td>\n",
       "      <td>8159.13</td>\n",
       "      <td>62586.03</td>\n",
       "      <td>32747.15</td>\n",
       "      <td>36012.36</td>\n",
       "      <td>35917.60</td>\n",
       "      <td>36186.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Publicity of parliamentary activity</th>\n",
       "      <td>173923.17</td>\n",
       "      <td>222693.49</td>\n",
       "      <td>237323.72</td>\n",
       "      <td>262815.02</td>\n",
       "      <td>268376.78</td>\n",
       "      <td>269596.00</td>\n",
       "      <td>248577.17</td>\n",
       "      <td>261390.66</td>\n",
       "      <td>251446.30</td>\n",
       "      <td>205395.21</td>\n",
       "      <td>256707.00</td>\n",
       "      <td>927221.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recruitment of consultancies, advisory services, research, technical work and other services in support of the exercise of the parliamentary mandate</th>\n",
       "      <td>213084.63</td>\n",
       "      <td>260918.32</td>\n",
       "      <td>297243.39</td>\n",
       "      <td>249704.43</td>\n",
       "      <td>305240.14</td>\n",
       "      <td>282839.14</td>\n",
       "      <td>202025.57</td>\n",
       "      <td>237189.84</td>\n",
       "      <td>199793.99</td>\n",
       "      <td>209980.66</td>\n",
       "      <td>249945.24</td>\n",
       "      <td>401176.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rent of real estate for political office, comprising expenses concerning them</th>\n",
       "      <td>387716.41</td>\n",
       "      <td>423858.67</td>\n",
       "      <td>441217.01</td>\n",
       "      <td>430179.71</td>\n",
       "      <td>425730.73</td>\n",
       "      <td>438865.16</td>\n",
       "      <td>409113.87</td>\n",
       "      <td>442999.22</td>\n",
       "      <td>410624.24</td>\n",
       "      <td>425457.97</td>\n",
       "      <td>388960.76</td>\n",
       "      <td>355473.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month                                                      1          2   \\\n",
       "expense_type                                                               \n",
       "Acquisition of consumables for use in the polit...   72464.58   95622.67   \n",
       "Locomotion, lodging, food, fuels and lubricants     373081.30  470910.65   \n",
       "National air, water and land transport              190543.44  451277.90   \n",
       "Private Security Services                            52280.03   51375.12   \n",
       "Publicity of parliamentary activity                 173923.17  222693.49   \n",
       "Recruitment of consultancies, advisory services...  213084.63  260918.32   \n",
       "Rent of real estate for political office, compr...  387716.41  423858.67   \n",
       "\n",
       "month                                                      3          4   \\\n",
       "expense_type                                                               \n",
       "Acquisition of consumables for use in the polit...   86689.23   89751.24   \n",
       "Locomotion, lodging, food, fuels and lubricants     526333.34  471441.48   \n",
       "National air, water and land transport              635800.52  556828.37   \n",
       "Private Security Services                            49677.47   40010.08   \n",
       "Publicity of parliamentary activity                 237323.72  262815.02   \n",
       "Recruitment of consultancies, advisory services...  297243.39  249704.43   \n",
       "Rent of real estate for political office, compr...  441217.01  430179.71   \n",
       "\n",
       "month                                                      5          6   \\\n",
       "expense_type                                                               \n",
       "Acquisition of consumables for use in the polit...   84847.06   90025.80   \n",
       "Locomotion, lodging, food, fuels and lubricants     467726.59  480589.29   \n",
       "National air, water and land transport              546786.53  603512.29   \n",
       "Private Security Services                            44902.60   45015.33   \n",
       "Publicity of parliamentary activity                 268376.78  269596.00   \n",
       "Recruitment of consultancies, advisory services...  305240.14  282839.14   \n",
       "Rent of real estate for political office, compr...  425730.73  438865.16   \n",
       "\n",
       "month                                                      7          8   \\\n",
       "expense_type                                                               \n",
       "Acquisition of consumables for use in the polit...  100578.11   88666.88   \n",
       "Locomotion, lodging, food, fuels and lubricants     622530.60  529582.56   \n",
       "National air, water and land transport              505547.72  603700.31   \n",
       "Private Security Services                             8159.13   62586.03   \n",
       "Publicity of parliamentary activity                 248577.17  261390.66   \n",
       "Recruitment of consultancies, advisory services...  202025.57  237189.84   \n",
       "Rent of real estate for political office, compr...  409113.87  442999.22   \n",
       "\n",
       "month                                                      9          10  \\\n",
       "expense_type                                                               \n",
       "Acquisition of consumables for use in the polit...   82566.49   83382.94   \n",
       "Locomotion, lodging, food, fuels and lubricants     620526.43  491455.90   \n",
       "National air, water and land transport              488860.02  568453.54   \n",
       "Private Security Services                            32747.15   36012.36   \n",
       "Publicity of parliamentary activity                 251446.30  205395.21   \n",
       "Recruitment of consultancies, advisory services...  199793.99  209980.66   \n",
       "Rent of real estate for political office, compr...  410624.24  425457.97   \n",
       "\n",
       "month                                                      11         12  \n",
       "expense_type                                                              \n",
       "Acquisition of consumables for use in the polit...   85632.50   93770.75  \n",
       "Locomotion, lodging, food, fuels and lubricants     415616.98  446097.87  \n",
       "National air, water and land transport              606522.27  391910.20  \n",
       "Private Security Services                            35917.60   36186.98  \n",
       "Publicity of parliamentary activity                 256707.00  927221.27  \n",
       "Recruitment of consultancies, advisory services...  249945.24  401176.41  \n",
       "Rent of real estate for political office, compr...  388960.76  355473.61  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "por_expense_type = pandas.crosstab(\n",
    "    index=df.expense_type,\n",
    "    columns=df.month,\n",
    "    values=df.reimbursement_value,\n",
    "    aggfunc=numpy.sum\n",
    ")\n",
    "por_expense_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1158827f0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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CBQAAAFCGUAEAAACUIVQAAAAAZQgVAAAAQBlCBQAAAFCGUAEAAACUIVQAAAAA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\noVABAAAAbA2FCgAAAGBr/H8109R0P2bTtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1126e7ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "por_expense_type.plot(kind=\"bar\", figsize=(18,18))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------\n",
    "## quais partidos e estados gastaram mais?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>73.60</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>6000.00</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>550.00</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...                73.60  \n",
       "1  Pagamento de energia elétrica para o escritóri...               206.84  \n",
       "2  Despesa com pagamento de aluguel, utilizado pa...              6000.00  \n",
       "3  despesa com pagamento de telefonia para o escr...               407.48  \n",
       "4  Aquisição de publicação virtual de conteúdo jo...               550.00  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores_additional_info = pandas.read_html(\"senadores.html\", encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       " 5                  NaN  Fernando Collor (Fernando Affonso Collor de Me...   \n",
       " 6                  NaN  Renan Calheiros (José Renan Vasconcelos Calhei...   \n",
       " 7                Amapá                                                NaN   \n",
       " 8                  NaN  Davi Alcolumbre (David Samuel Alcolumbre Tobelem)   \n",
       " 9                  NaN  João Capiberibe (João Alberto Rodrigues Capibe...   \n",
       " 10                 NaN  Randolfe Rodrigues (Randolph Frederich Rodrigu...   \n",
       " 11            Amazonas                                                NaN   \n",
       " 12                 NaN      Eduardo Braga (Carlos Eduardo de Sousa Braga)   \n",
       " 13                 NaN                   Omar Aziz (Omar José Abdel Aziz)   \n",
       " 14                 NaN                                 Vanessa Grazziotin   \n",
       " 15               Bahia                                                NaN   \n",
       " 16                 NaN            Lídice da Mata (Lídice da Mata e Sousa)   \n",
       " 17                 NaN    Otto Alencar (Otto Roberto Mendonça de Alencar)   \n",
       " 18                 NaN          Roberto Muniz (Roberto de Oliveira Muniz)   \n",
       " 19               Ceará                                                NaN   \n",
       " 20                 NaN       Eunício Oliveira (Eunício Lopes de Oliveira)   \n",
       " 21                 NaN              José Pimentel (José Barroso Pimentel)   \n",
       " 22                 NaN        Tasso Jereissati (Tasso Ribeiro Jereissati)   \n",
       " 23    Distrito Federal                                                NaN   \n",
       " 24                 NaN  Cristovam Buarque (Cristovam Ricardo Cavalcant...   \n",
       " 25                 NaN              Hélio José (Hélio José da Silva Lima)   \n",
       " 26                 NaN             Reguffe (José Antônio Machado Reguffe)   \n",
       " 27      Espírito Santo                                                NaN   \n",
       " 28                 NaN                  Magno Malta (Magno Pereira Malta)   \n",
       " 29                 NaN       Ricardo Ferraço (Ricardo de Rezende Ferraço)   \n",
       " ..                 ...                                                ...   \n",
       " 77                 NaN                              Garibaldi Alves Filho   \n",
       " 78                 NaN                 José Agripino (José Agripino Maia)   \n",
       " 79   Rio Grande do Sul                                                NaN   \n",
       " 80                 NaN                      Ana Amélia (Ana Amélia Lemos)   \n",
       " 81                 NaN              Lasier Martins (Lasier Costa Martins)   \n",
       " 82                 NaN                     Paulo Paim (Paulo Renato Paim)   \n",
       " 83            Rondônia                                                NaN   \n",
       " 84                 NaN                 Acir Gurgacz (Acir Marcos Gurgacz)   \n",
       " 85                 NaN                    Ivo Cassol (Ivo Narciso Cassol)   \n",
       " 86                 NaN                                       Valdir Raupp   \n",
       " 87             Roraima                                                NaN   \n",
       " 88                 NaN        Ângela Portela (Ângela Maria Gomes Portela)   \n",
       " 89                 NaN                    Romero Jucá (Romero Jucá Filho)   \n",
       " 90                 NaN          Telmário Mota (Telmário Mota de Oliveira)   \n",
       " 91      Santa Catarina                                                NaN   \n",
       " 92                 NaN                 Dalírio Beber (Dalírio José Beber)   \n",
       " 93                 NaN                  Dário Berger (Dário Elias Berger)   \n",
       " 94                 NaN                  Paulo Bauer (Paulo Roberto Bauer)   \n",
       " 95           São Paulo                                                NaN   \n",
       " 96                 NaN          Airton Sandoval (Airton Sandoval Santana)   \n",
       " 97                 NaN                                         José Serra   \n",
       " 98                 NaN  Marta Suplicy (Marta Teresa Smith de Vasconcel...   \n",
       " 99             Sergipe                                                NaN   \n",
       " 100                NaN                           Antônio Carlos Valadares   \n",
       " 101                NaN           Eduardo Amorim (Eduardo Alves do Amorim)   \n",
       " 102                NaN  Maria do Carmo Alves (Maria do Carmo do Nascim...   \n",
       " 103          Tocantins                                                NaN   \n",
       " 104                NaN             Ataídes Oliveira (Ataídes de Oliveira)   \n",
       " 105                NaN                Kátia Abreu (Kátia Regina de Abreu)   \n",
       " 106                NaN       Vicentinho Alves (Vicente Alves de Oliveira)   \n",
       " \n",
       "                Partido  Início de mandato     Fim de mandato  \\\n",
       "     Unnamed: 2_level_1 Unnamed: 3_level_1 Unnamed: 4_level_1   \n",
       " 0                   PP             2015.0             2023.0   \n",
       " 1                   PT             2011.0             2019.0   \n",
       " 2                  PSD             2011.0             2019.0   \n",
       " 3                  NaN                NaN                NaN   \n",
       " 4                   PP             2011.0             2019.0   \n",
       " 5                  PTC             2007.0             2023.0   \n",
       " 6                 PMDB             1995.0             2019.0   \n",
       " 7                  NaN                NaN                NaN   \n",
       " 8                  DEM             2015.0             2023.0   \n",
       " 9                  PSB             2011.0             2019.0   \n",
       " 10                REDE             2011.0             2019.0   \n",
       " 11                 NaN                NaN                NaN   \n",
       " 12                PMDB             2011.0             2019.0   \n",
       " 13                 PSD             2015.0             2023.0   \n",
       " 14               PCdoB             2011.0             2019.0   \n",
       " 15                 NaN                NaN                NaN   \n",
       " 16                 PSB             2011.0             2019.0   \n",
       " 17                 PSD             2015.0             2023.0   \n",
       " 18                  PP             2016.0             2019.0   \n",
       " 19                 NaN                NaN                NaN   \n",
       " 20                PMDB             2011.0             2019.0   \n",
       " 21                  PT             2011.0             2019.0   \n",
       " 22                PSDB             2015.0             2023.0   \n",
       " 23                 NaN                NaN                NaN   \n",
       " 24                 PPS             2003.0             2019.0   \n",
       " 25                PMDB             2015.0             2019.0   \n",
       " 26         Sem partido             2015.0             2023.0   \n",
       " 27                 NaN                NaN                NaN   \n",
       " 28                  PR             2003.0             2019.0   \n",
       " 29                PSDB             2011.0             2019.0   \n",
       " ..                 ...                ...                ...   \n",
       " 77                PMDB             2003.0             2019.0   \n",
       " 78                 DEM             1995.0             2019.0   \n",
       " 79                 NaN                NaN                NaN   \n",
       " 80                  PP             2011.0             2019.0   \n",
       " 81                 PSD             2015.0             2023.0   \n",
       " 82                  PT             2003.0             2019.0   \n",
       " 83                 NaN                NaN                NaN   \n",
       " 84                 PDT             2009.0             2023.0   \n",
       " 85                  PP             2011.0             2019.0   \n",
       " 86                PMDB             2011.0             2019.0   \n",
       " 87                 NaN                NaN                NaN   \n",
       " 88                 PDT             2011.0             2019.0   \n",
       " 89                PMDB             1995.0             2019.0   \n",
       " 90                 PTB             2015.0             2023.0   \n",
       " 91                 NaN                NaN                NaN   \n",
       " 92                PSDB             2015.0             2019.0   \n",
       " 93                PMDB             2015.0             2023.0   \n",
       " 94                PSDB             2011.0             2019.0   \n",
       " 95                 NaN                NaN                NaN   \n",
       " 96                PMDB             2017.0             2019.0   \n",
       " 97                PSDB             2015.0             2023.0   \n",
       " 98                PMDB             2011.0             2019.0   \n",
       " 99                 NaN                NaN                NaN   \n",
       " 100                PSB             1995.0             2019.0   \n",
       " 101               PSDB             2011.0             2019.0   \n",
       " 102                DEM             1999.0             2023.0   \n",
       " 103                NaN                NaN                NaN   \n",
       " 104               PSDB             2013.0             2019.0   \n",
       " 105               PMDB             2015.0             2023.0   \n",
       " 106                 PR             2011.0             2019.0   \n",
       " \n",
       "                                                   Obs.  \n",
       "                                     Unnamed: 5_level_1  \n",
       " 0                                                  NaN  \n",
       " 1                                                  NaN  \n",
       " 2                                                  NaN  \n",
       " 3                                                  NaN  \n",
       " 4                                                  NaN  \n",
       " 5                                                  NaN  \n",
       " 6                                                  NaN  \n",
       " 7                                                  NaN  \n",
       " 8                                                  NaN  \n",
       " 9                                                  NaN  \n",
       " 10                                                 NaN  \n",
       " 11                                                 NaN  \n",
       " 12                                                 NaN  \n",
       " 13                                                 NaN  \n",
       " 14                                                 NaN  \n",
       " 15                                                 NaN  \n",
       " 16                                                 NaN  \n",
       " 17                                                 NaN  \n",
       " 18                        Suplente de Walter Pinheiro.  \n",
       " 19                                                 NaN  \n",
       " 20                                                 NaN  \n",
       " 21                                                 NaN  \n",
       " 22                                                 NaN  \n",
       " 23                                                 NaN  \n",
       " 24                                                 NaN  \n",
       " 25                     Suplente de Rodrigo Rollemberg.  \n",
       " 26                                                 NaN  \n",
       " 27                                                 NaN  \n",
       " 28                                                 NaN  \n",
       " 29                                                 NaN  \n",
       " ..                                                 ...  \n",
       " 77                                                 NaN  \n",
       " 78                                                 NaN  \n",
       " 79                                                 NaN  \n",
       " 80                                                 NaN  \n",
       " 81                                                 NaN  \n",
       " 82                                                 NaN  \n",
       " 83                                                 NaN  \n",
       " 84                                                 NaN  \n",
       " 85                                                 NaN  \n",
       " 86                                                 NaN  \n",
       " 87                                                 NaN  \n",
       " 88                                                 NaN  \n",
       " 89                                                 NaN  \n",
       " 90                                                 NaN  \n",
       " 91                                                 NaN  \n",
       " 92              Assumiu após a morte de Luiz Henrique.  \n",
       " 93                                                 NaN  \n",
       " 94                                                 NaN  \n",
       " 95                                                 NaN  \n",
       " 96                           Suplente de Aloysio Nunes  \n",
       " 97                                                 NaN  \n",
       " 98                                                 NaN  \n",
       " 99                                                 NaN  \n",
       " 100                                                NaN  \n",
       " 101                                                NaN  \n",
       " 102                                                NaN  \n",
       " 103                                                NaN  \n",
       " 104  Asumiu definitivamente após o falecimento de J...  \n",
       " 105                                                NaN  \n",
       " 106                                                NaN  \n",
       " \n",
       " [107 rows x 6 columns]]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(senadores_additional_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(senadores_additional_info[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores_additional_info = senadores_additional_info[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\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",
       "      <th>Início de mandato</th>\n",
       "      <th>Fim de mandato</th>\n",
       "      <th>Obs.</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",
       "      <th>Unnamed: 3_level_1</th>\n",
       "      <th>Unnamed: 4_level_1</th>\n",
       "      <th>Unnamed: 5_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",
       "      <td>2015.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NaN</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",
       "      <td>2011.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</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",
       "      <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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info.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": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "del senadores_additional_info[\"Início de mandato\"]\n",
    "del senadores_additional_info[\"Fim de mandato\"]\n",
    "del senadores_additional_info[\"Obs.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gladson Cameli (Gladson de Lima Cameli)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Jorge Viana (Jorge Ney Viana Macedo Neves)</td>\n",
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       "      <td>Benedito de Lira</td>\n",
       "      <td>PP</td>\n",
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      ],
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       "  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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "    </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": [
       "    estado                                        nome            partido\n",
       "      Acre                          Unnamed: 1_level_1 Unnamed: 2_level_1\n",
       "0      NaN     Gladson Cameli (Gladson de Lima Cameli)                 PP\n",
       "1      NaN  Jorge Viana (Jorge Ney Viana Macedo Neves)                 PT\n",
       "2      NaN   Sérgio Petecão (Sérgio de Oliveira Cunha)                PSD\n",
       "3  Alagoas                                         NaN                NaN\n",
       "4      NaN                            Benedito de Lira                 PP"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info.rename(columns={\"Unnamed: 0_level_0\": \"estado\", \"Nome\": \"nome\", \"Partido\": \"partido\"}, inplace=True)\n",
    "senadores_additional_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "estado = \"Acre\"\n",
    "for i, row in senadores_additional_info.iterrows():\n",
    "    if pandas.isnull(row[\"estado\"][0]):\n",
    "        senadores_additional_info.loc[i][\"estado\"] = estado\n",
    "    else:\n",
    "        estado = row[\"estado\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>estado</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>Acre</td>\n",
       "      <td>Gladson Cameli (Gladson de Lima Cameli)</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acre</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>Acre</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>Alagoas</td>\n",
       "      <td>Benedito de Lira</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    estado                                        nome            partido\n",
       "      Acre                          Unnamed: 1_level_1 Unnamed: 2_level_1\n",
       "0     Acre     Gladson Cameli (Gladson de Lima Cameli)                 PP\n",
       "1     Acre  Jorge Viana (Jorge Ney Viana Macedo Neves)                 PT\n",
       "2     Acre   Sérgio Petecão (Sérgio de Oliveira Cunha)                PSD\n",
       "3  Alagoas                                         NaN                NaN\n",
       "4  Alagoas                            Benedito de Lira                 PP"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores_additional_info.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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 (Gladson de Lima Cameli)</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acre</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>Acre</td>\n",
       "      <td>Sérgio Petecão (Sérgio de Oliveira Cunha)</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 (Fernando Affonso Collor de Me...</td>\n",
       "      <td>PTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    estado                                               nome partido\n",
       "0     Acre            Gladson Cameli (Gladson de Lima Cameli)      PP\n",
       "1     Acre         Jorge Viana (Jorge Ney Viana Macedo Neves)      PT\n",
       "2     Acre          Sérgio Petecão (Sérgio de Oliveira Cunha)     PSD\n",
       "4  Alagoas                                   Benedito de Lira      PP\n",
       "5  Alagoas  Fernando Collor (Fernando Affonso Collor de Me...     PTC"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info.columns = ['estado', 'nome', 'partido']\n",
    "senadores_additional_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                Gladson Cameli\n",
       "1                   Jorge Viana\n",
       "2                Sérgio Petecão\n",
       "4              Benedito de Lira\n",
       "5               Fernando Collor\n",
       "6               Renan Calheiros\n",
       "8               Davi Alcolumbre\n",
       "9               João Capiberibe\n",
       "10           Randolfe Rodrigues\n",
       "12                Eduardo Braga\n",
       "13                    Omar Aziz\n",
       "14           Vanessa Grazziotin\n",
       "16               Lídice da Mata\n",
       "17                 Otto Alencar\n",
       "18                Roberto Muniz\n",
       "20             Eunício Oliveira\n",
       "21                José Pimentel\n",
       "22             Tasso Jereissati\n",
       "24            Cristovam Buarque\n",
       "25                   Hélio José\n",
       "26                      Reguffe\n",
       "28                  Magno Malta\n",
       "29              Ricardo Ferraço\n",
       "30              Rose de Freitas\n",
       "32                  Lúcia Vânia\n",
       "33               Ronaldo Caiado\n",
       "34                Wilder Morais\n",
       "36                 Edison Lobão\n",
       "37           João Alberto Souza\n",
       "38                Roberto Rocha\n",
       "                 ...           \n",
       "68                Ciro Nogueira\n",
       "69                Elmano Férrer\n",
       "70                 Regina Sousa\n",
       "72                Eduardo Lopes\n",
       "73              Lindberg Farias\n",
       "74                      Romário\n",
       "76               Fátima Bezerra\n",
       "77        Garibaldi Alves Filho\n",
       "78                José Agripino\n",
       "80                   Ana Amélia\n",
       "81               Lasier Martins\n",
       "82                   Paulo Paim\n",
       "84                 Acir Gurgacz\n",
       "85                   Ivo Cassol\n",
       "86                 Valdir Raupp\n",
       "88               Ângela Portela\n",
       "89                  Romero Jucá\n",
       "90                Telmário Mota\n",
       "92                Dalírio Beber\n",
       "93                 Dário Berger\n",
       "94                  Paulo Bauer\n",
       "96              Airton Sandoval\n",
       "97                   José Serra\n",
       "98                Marta Suplicy\n",
       "100    Antônio Carlos Valadares\n",
       "101              Eduardo Amorim\n",
       "102        Maria do Carmo Alves\n",
       "104            Ataídes Oliveira\n",
       "105                 Kátia Abreu\n",
       "106            Vicentinho Alves\n",
       "Name: nome, Length: 81, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info.nome.str.replace(r\"\\s\\(.*\\)\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores_additional_info.nome = senadores_additional_info.nome.str.replace(r\"\\s\\(.*\\)\", \"\")\n",
    "senadores_additional_info.nome = senadores_additional_info.nome.str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "estado     81\n",
       "nome       81\n",
       "partido    81\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores_additional_info.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.congressperson_name.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "suplentes = pandas.read_html(\"suplentes.html\", encoding=\"utf-8\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suplentes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nome</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Estado</th>\n",
       "      <th>Titular</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",
       "      <td>Cássio Cunha Lima</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",
       "      <td>Kátia Abreu</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",
       "      <td>Armando Monteiro</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",
       "      <td>Jader Barbalho</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",
       "      <td>Acir Gurgacz</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   \n",
       "\n",
       "             Titular  \n",
       "0  Cássio Cunha Lima  \n",
       "1        Kátia Abreu  \n",
       "2   Armando Monteiro  \n",
       "3     Jader Barbalho  \n",
       "4       Acir Gurgacz  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del suplentes[\"Unnamed: 0\"]\n",
    "del suplentes[\"Período(s) exercido(s)\"]\n",
    "del suplentes[\"Obs.\"]\n",
    "suplentes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nome</th>\n",
       "      <th>partido</th>\n",
       "      <th>estado</th>\n",
       "      <th>titular</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",
       "      <td>CÁSSIO CUNHA LIMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DONIZETI NOGUEIRA</td>\n",
       "      <td>PT</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>KÁTIA ABREU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DOUGLAS CINTRA</td>\n",
       "      <td>PTB</td>\n",
       "      <td>Pernambuco</td>\n",
       "      <td>ARMANDO MONTEIRO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FERNANDO RIBEIRO</td>\n",
       "      <td>PMDB</td>\n",
       "      <td>Pará</td>\n",
       "      <td>JADER BARBALHO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GILBERTO PISELO</td>\n",
       "      <td>PDT</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                nome partido      estado            titular\n",
       "0      DECA SOBRINHO    PSDB     Paraíba  CÁSSIO CUNHA LIMA\n",
       "1  DONIZETI NOGUEIRA      PT   Tocantins        KÁTIA ABREU\n",
       "2     DOUGLAS CINTRA     PTB  Pernambuco   ARMANDO MONTEIRO\n",
       "3   FERNANDO RIBEIRO    PMDB        Pará     JADER BARBALHO\n",
       "4    GILBERTO PISELO     PDT    Rondônia       ACIR GURGACZ"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suplentes.columns = [\"nome\", \"partido\", \"estado\", \"titular\"]\n",
    "suplentes.nome = suplentes.nome.str.replace(r\"\\s\\(.*\\)\", \"\").str.upper()\n",
    "suplentes.titular = suplentes.titular.str.replace(r\"\\s\\(.*\\)\", \"\").str.upper()\n",
    "suplentes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nome       13\n",
       "partido    13\n",
       "estado     13\n",
       "titular    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suplentes.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores = pandas.concat([senadores_additional_info, suplentes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "senadores.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>estado</th>\n",
       "      <th>nome</th>\n",
       "      <th>partido</th>\n",
       "      <th>titular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Acre</td>\n",
       "      <td>GLADSON CAMELI</td>\n",
       "      <td>PP</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Acre</td>\n",
       "      <td>JORGE VIANA</td>\n",
       "      <td>PT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Acre</td>\n",
       "      <td>SÉRGIO PETECÃO</td>\n",
       "      <td>PSD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alagoas</td>\n",
       "      <td>BENEDITO DE LIRA</td>\n",
       "      <td>PP</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Alagoas</td>\n",
       "      <td>FERNANDO COLLOR</td>\n",
       "      <td>PTC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   estado              nome partido titular\n",
       "0      0     Acre    GLADSON CAMELI      PP     NaN\n",
       "1      1     Acre       JORGE VIANA      PT     NaN\n",
       "2      2     Acre    SÉRGIO PETECÃO     PSD     NaN\n",
       "3      4  Alagoas  BENEDITO DE LIRA      PP     NaN\n",
       "4      5  Alagoas   FERNANDO COLLOR     PTC     NaN"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senadores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "del senadores[\"index\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>estado</th>\n",
       "      <th>nome</th>\n",
       "      <th>partido</th>\n",
       "      <th>titular</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>73.60</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>PDT</td>\n",
       "      <td>NaN</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",
       "      <td>Rondônia</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>PDT</td>\n",
       "      <td>NaN</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>6000.00</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>PDT</td>\n",
       "      <td>NaN</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",
       "      <td>Rondônia</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>PDT</td>\n",
       "      <td>NaN</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>550.00</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>ACIR GURGACZ</td>\n",
       "      <td>PDT</td>\n",
       "      <td>NaN</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...                73.60   \n",
       "1  Pagamento de energia elétrica para o escritóri...               206.84   \n",
       "2  Despesa com pagamento de aluguel, utilizado pa...              6000.00   \n",
       "3  despesa com pagamento de telefonia para o escr...               407.48   \n",
       "4  Aquisição de publicação virtual de conteúdo jo...               550.00   \n",
       "\n",
       "     estado          nome partido titular  \n",
       "0  Rondônia  ACIR GURGACZ     PDT     NaN  \n",
       "1  Rondônia  ACIR GURGACZ     PDT     NaN  \n",
       "2  Rondônia  ACIR GURGACZ     PDT     NaN  \n",
       "3  Rondônia  ACIR GURGACZ     PDT     NaN  \n",
       "4  Rondônia  ACIR GURGACZ     PDT     NaN  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_completo = df.merge(senadores, how=\"left\", left_on=\"congressperson_name\", right_on=\"nome\")\n",
    "df_completo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_completo[\"nome\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reimbursement_value    100.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117776358>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TeAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsA\nQNcEXgAAuibwAgDQNYEXAICuCbwAAHRN4AUAoGvbbOkNAICFrDz6pE16/JpjDp7RlgDXd0Z4AQDo\nmsALAEDXBF4AALom8AIA0DWBFwCArlmlAYBNZgUF4PrECC8AAF0TeAEA6JrACwBA1wReAAC6JvAC\nANA1gRcAgK4JvAAAdM06vMAkrMsKwHWVEV4AALom8AIA0DWBFwCArgm8AAB0zaQ1uBabOhErMRkL\nAK5rjPACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQNYEXAICu\nCbwAAHRN4AUAoGsCLwAAXRN4AQDomsALAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAFAKBrAi8A\nAF0TeAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcE\nXgAAuibwAgDQNYEXAICuCbwAAHRN4AUAoGsCLwAAXRN4AQDomsALAEDXBF4AALom8AIA0DWBFwCA\nrgm8AAB0TeAFAKBrAi8AAF0TeAEA6JrACwBA1xYdeKtq66r6UlWdOMsNAgCAKW3KCO+RSc6d1YYA\nAMAsLCrwVtWuSQ5O8tbZbg4AAExrsSO8r03yR0mumuG2AADA5DYaeKvqEUl+0Fo7YyOPe0ZVra6q\n1RdddNFkGwgAAEuxmBHeA5I8sqrWJPn7JA+qqndt+KDW2nGttVWttVW77LLLxJsJAACbZ6OBt7X2\nJ621XVtrK5M8NsknWmuPn/mWAQDABKzDCwBA17bZlAe31k5NcupMtgQAAGbACC8AAF0TeAEA6JrA\nCwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQ\nNYEXAICuCbwAAHRN4AUAoGsCLwAAXRN4AQDomsALAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAF\nAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABd22ZLbwAA\n9Grl0Sdt8s+sOebgGWwJ3LAZ4QUAoGsCLwAAXRN4AQDomsALAEDXTFoDgOuxTZ0YZ1IcN0RGeAEA\n6JrACwBA15Q0AACwrJa7FMcILwAAXRN4AQDomsALAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAF\nAKBrLjwBNwDLvcA3AFyXGOEFAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAA\ndE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQNYEXAICuCbwAAHRN4AUAoGsCLwAAXRN4\nAQDomsALAEDXBF4AALom8AIA0DWBFwCArm2zpTcAbuhWHn3SJv/MmmMOnsGWAECfjPACANA1gRcA\ngK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQNYEXAICuubQwAMAEXCr+ussI\nLwAAXRN4AQDomsALAEDXNhp4q2r7qjq9qs6qqq9W1UuWY8MAAGAKi5m09oskD2qt/aSqViT5TFV9\nqLV22oy3DQAAlmyjgbe11pL8ZLy5YvzXZrlRAAAwlUXV8FbV1lV1ZpIfJDm5tfb5BR7zjKpaXVWr\nL7rooqm3EwAANsui1uFtrV2Z5O5VddMk/1RVe7bWvrLBY45LclySrFq1yggwAMDENnWtX+v8DjZp\nlYbW2o+TnJLkYbPZHAAAmNZiVmnYZRzZTVXtkOSgJOfNesMAAGAKiylpuHWSE6pq6wwB+b2ttRNn\nu1kAADCNxazScHaSeyzDtgAAwORcaQ0AgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAA\nuibwAgDQNYEXAICuLebSwtcrK48+aZMev+aYg2e0JQAAXBcY4QUAoGsCLwAAXRN4AQDoWnc1vADX\nZZs6zyAx1wBgqYzwAgDQNSO8N1BWswAAbiiM8AIA0DWBFwCArgm8AAB0TeAFAKBrAi8AAF0TeAEA\n6JrACwBA1wReAAC6JvACANA1gRcAgK65tDAAAGutPPqkTXr8mmMOntGWTEfgBRht6k4+uX7s6AFu\n6JQ0AADQNYEXAICuCbwAAHRN4AUAoGsCLwAAXRN4AQDomsALAEDXBF4AALom8AIA0DVXWgPoTI+X\nBQVYCiO8AAB0TeAFAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCg\nawIvAABdE3gBAOjaNlt6A+jXyqNP2qTHrznm4BltCQBwQ2aEFwCArgm8AAB0TeAFAKBrAi8AAF0T\neAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAA\nuibwAgDQNYEXAICuCbwAAHRN4AUAoGsCLwAAXRN4AQDomsALAEDXBF4AALom8AIA0DWBFwCArgm8\nAAB0TeAFAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABd\nE3gBAOjaRgNvVd22qk6pqnOq6qtVdeRybBgAAExhm0U85ookL2itfbGqdkpyRlWd3Fo7Z8bbBgAA\nS7bREd7W2vdaa18cv74syblJbjPrDQMAgCksZoR3rapameQeST4/i40BuDYrjz5pkx6/5piDZ7Ql\nAFyfLHrSWlXdOMkHkjyvtXbpAt9/RlWtrqrVF1100ZTbCAAAm21RgbeqVmQIu+9urf3jQo9prR3X\nWlvVWlu1yy67TLmNAACw2RazSkMleVuSc1trr579JgEAwHQWM8J7QJInJHlQVZ05/vuNGW8XAABM\nYqOT1lprn0lSy7AtAAAwuU1apQEAuOGxQgrXdy4tDABA1wReAAC6JvACANA1gRcAgK4JvAAAdE3g\nBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQNYEXAICuCbwAAHRN4AUAoGsCLwAAXRN4AQDo\nmsALAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAFAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6JvAC\nANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQNYEXAICuCbwAAHRN\n4AUAoGsCLwAAXRN4AQDomsALAEDXttnSGwBLsfLokzbp8WuOOXhGWwIAXFcZ4QUAoGsCLwAAXRN4\nAQDomsALAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAFAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6\nJvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQNYEXAICuCbwA\nAHRN4AUAoGsCLwAAXRN4AQDomsALAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAFAKBrAi8AAF0T\neAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAA\nurbRwFtVb6+qH1TVV5ZjgwAAYEqLGeF9R5KHzXg7AABgJjYaeFtrn0ryo2XYFgAAmJwaXgAAujZZ\n4K2qZ1TV6qpafdFFF03VLAAALMlkgbe1dlxrbVVrbdUuu+wyVbMAALAkShoAAOjaYpYle0+Sf0ty\nl6q6sKqeNvvNAgCAaWyzsQe01g5fjg0BAIBZUNIAAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAF\nAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOia\nwAsAQNcEXgAAuibwAgDQNYEXAICuCbwAAHRN4AUAoGsCLwAAXRN4AQDomsALAEDXBF4AALom8AIA\n0DWBFwCArgm8AAB0TeAFAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3g\nBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQNYEXAICuCbwAAHRN4AUAoGsCLwAAXRN4AQDo\nmsALAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAFAKBrAi8AAF0TeAEA6JrACwBA1wReAAC6JvAC\nANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOiawAsAQNcEXgAAuibwAgDQNYEXAICuCbwAAHRN\n4AUAoGsCLwAAXRN4AQDomsALAEDXBF4AALom8AIA0DWBFwCArgm8AAB0TeAFAKBrAi8AAF0TeAEA\n6JrACwBA1wReAAC6JvACANA1gRcAgK4JvAAAdE3gBQCgawIvAABdE3gBAOjaogJvVT2sqr5WVRdU\n1dGz3igAAJjKRgNvVW2d5A1JHp5kjySHV9Ues94wAACYwmJGeO+V5ILW2jdba5cn+fskj5rtZgEA\nwDQWE3hvk+Q7825fON4HAADXedVau/YHVP1Okoe11o4Ybz8hyb1ba8/Z4HHPSPKM8eZdknxtE7bj\nFkl+uAmP3xw99NHD76CP6077+rhu9dHD76CP6077+rhu9dHD73Bd7eP2rbVdNvagbRbR0HeT3Hbe\n7V3H+9bTWjsuyXGL3rx5qmp1a23V5vzsDamPHn4HfVx32tfHdauPHn4HfVx32tfHdauPHn6H63sf\niylp+EKSO1XVblW1bZLHJvng1BsCAACzsNER3tbaFVX1nCQfSbJ1kre31r468y0DAIAJLKakIa21\nf03yrzPcjs0qhbgB9tHD76CP6077+rhu9dHD76CP6077+rhu9dHD73C97mOjk9YAAOD6zKWFAQDo\nmsALAEDXFlXDC9dVVXXItX2/tfaPE/a1IsmzkjxgvOuTSd7cWvvlVH30oqoemXnPU2vtXyZqd9n+\n3hv0u1WSG7fWLp1F+2w5VXXX1tp5y9TX/ZLcqbV2fFXtkuE19a3l6HtKVbVPkvuPNz/dWjtrS27P\ndVVVHZzk15JsP3dfa+2lW26LNk9V3SrJfuPN01trP5i4/eck+dvW2qVV9ZYk90jyJ621j0/az5as\n4a2qG7XWfrYM/cz0w6qqbpn1X9DfnkU/szTrN+as2q+q46/l26219tSl9jGvr7cmWZHkhPGuJyS5\ncu6iLBP1cYckF7bWflFVBybZO8OO4MdT9TH2M7MdWFX9VYZLkr97vOvwJF9orf3vCdpezr/33yX5\nvSRXZlie8VeSvK619oqp+ujJ9fXDvaoeleG9/JQkBy30mCkOpKrqRUlWJblLa+3OVfU/kryvtXbA\nBG1flmShD/PK8L74laX2Ma+vI5M8Pcncc/LbSY5rrR07VR8b9He9/HytqjcnuVGSX0/y1iS/k2Ff\n+7SJ+6kkj0uye2vtpVV1uyS/2lo7faL2H5PkFUlOzfB6un+So1pr75+i/bGPs1tre1fVQ5I8O8mL\nMqwIds+p+kiStNaW/V+S+yY5J8m3x9v7JHnjxH38XYYPqB3Hvi7M8Edaaru3mff1I5NckOSyJGuS\nXJXkqxP+Dn89/g4rknw8yUVJHj+Dv8ebk/xthktIvyjJl5O8bYltHpzk1vPaf3eS85O8eIr2t8S/\nJGct5r4l9nFmhjMvd0zy9Qw7mn+duI/HJPn3DMH9b5N8K8nvTNj+2Um2mnd76yRnb+m/3+b8Lcb/\nH5fkVeP7cMm/R4aD77mv90+yetyHXJ4hXF868e+xS5JXZlhp5xNz/yZod1nf4xkuevRP437wB0k+\nkGTXidreK8mjkxy/wL+3T/V6yhAYvjTvvuvj++LsJDvOu73jLH6P8fP1/CQ/HfdRk36+jn3cKcn7\nM2SEb879m+p52uD/G2cYDZ/6eXpTkjckOXe8fbMMAwxTtX9WklvOu73LDD73zhr/f22SQ8evvzRl\nH621LVbS8JokD814AYvW2llV9YBr/5FNtkcbhscfl+RDSY5OckaGALEUD6yqgzIchbwsyX2SvLe1\n9utV9etJHr/E9ud7SGvtj6rqtzME6kOSfCrJuybsI0nu24ajq7Nbay+pqldleM6W4ltJ3lZVL57X\n/qdaay+uqldO0P7VLMMI05VVdYfW2jfG/nbPEFCmdFUb1r7+7STHttaOraovTdzHnybZr42juuOp\n1Y9l2PFP5aZJfjR+fZMJ200D+qmRAAAgAElEQVSSVNULF7p/4r/3irGM5beS/N/W2i+raopTYo8f\nR/delOT/ZgjUb07y4CRPTHLnCfqY791J/iFDQP29JE/KEBqXarnf48dnGMh49Hj78eN9C47KborW\n2pczhPT3LbWta3F5a63NvYaqasepOxhH966mTTsqWll/v3fleN/U/jzDAeHHWmv3mMHnazK8fl6U\nIZP8eoZR/qnmNv33+P/Pxvf7xUluPVHb8927tbbv3OdEa+2/xouETWWrtv4ZwIsz/fyvs6rqXzPs\n+/53Vd04C5+xWJItVsPbWvvOMBK/1tTBYSYfVq21v6uq8zN8eFzeWrto7CettVOq6rVL7WOeub/P\nwRlOfV2ywXM2lcnfmK21c6rqNzMcQc+1f8UYri5davsbuqbTR1P2keSoJKdU1Tcz7OBvn2SyU+ij\nX1bV4RlCyW+O962YuI9Z78D+KsmXquqUDM/TAzIccE7pp/O+3j7JI5KcO3Efb8lwoHlWkk9V1e0z\nvHaXpLX25qo6NEPQTWvta1W1orV2ZZLjxw+uP1lqP/Ps3Fp7W1Ud2Vr7ZJJPVtUXltrocr/Hk+zS\nWptf0vKOqnrelB3M+EDqvWN94k2r6ukZ9h1vnaDd+U6a9/X2SXZL8rUMAwFTOT7J56vqn8bbv5Xk\nbRO2P+eXrbWLq2qrqtpqBp+vSbJDa+3jVVWttX9P8uKqOiPJgq+DTXRiVd00wyDbFzMEuKn/3snw\nmbH12P7cAMZVE7b/4ar6SJL3jLcPy/TXZXhKknsmuaC19rOqukWSSUs/ki0XeL9TVfdN0saweGSu\nJx9WSdJa+0KSL1TV741HIp+vqndmGNH672v/6U1yYlWdN7b5rPGF/PMJ25/fz+RvzPED/Lyqmmv/\ndRlOh1Wm30HOYpR6Q5/J8OF+l/H21yZuPxne+L+X5C9aa9+qqt2SvHPiPma6A2utvaeqTs26GuE/\nbq3951Ttj328av7tcUTxIxP38fokr59317+Po0xTtP2BJKmqZ4yjMedV1V9mGHndeoo+5pmbVPm9\n8SzIfyS5+RQNL/N7/OKqenzWvW4Pz3CwNqWZHUi11l45nh28NMM+5IWttZOnaHteH3vNv11V+2Y4\nGzllH68e39/3G+96Smtt6rNQSfLj8fP1U0neXVU/yPp/nyn8ooY5PufXMHHquxlKD5astfbn45cf\nqKoTk2zfWrtkirY38PoMpT63qqq/yDDY82dTNd5aO2o8QJ+rNT+utfZP1/Yzm9HHlVX17SR3rKqZ\n5dItMmltTO+vy3AKr5J8NMmRrbWpd14b9rtNa+2KCdvbMUMYnSsav1mSd7fWfjhhHzdPcsn4grhR\nkl+ZOjxs0N92mfCNOe5M9m+tfW68vW2Go+pJ3/hVdXpr7V5VdVqG0o+LM9R73XHCPr7YWtt3Y/dd\nH2ywA/v0lDuwqjogQ/3rT8eAsm+GyV7/PlUfC/Q5V7e25L93VT2+tfauqvrDhb7fWnv1UvuY19ft\nk3w/wyj+8zPsQ97UWvv6hH08Ismnk9w2ybEZ5gW8pLX2wYnaX673+O0zbP99MhyUfy7JH0x8un7D\nPrdL8pHW2oETtPXy1tofb+y+qVXVlzcMwktoa+sM+9W7TtHeRvraMcMAz9zn600yfL5OlhOqar8M\nBzQ3zVBCcZMkf91aO22i9u+bZGXmDS621v52irY36OeuSf7nePMTrbWpBxBnajzYf3yS87LubH9r\nrf3GlP0s+wjv+IZ5QmvtccvQ19VqOpNMVuM3fqD/aoYZ6f+V5KNTht3R/0jy4Kqa/ztM8oapqge1\n1j5RCyz1VFVpE8xMbq1dVVVvyLDMSFprl2eYnDO1f1lglPpvpmh4/BvfJskOVXWPrKtX+5UMZRRL\nbX+r1tpV49d3ylASsEfWr0Xefan9zDeOMH5gyjbneVOSfWpYuugPM4z0/W2SB07VQVV9OetqvLbO\nMJFiqvf2XG3lThO1d43mHQT8PBPumzbo48Txy0sylPxM3f6yvMfH5+qRU7e7ETfKMFluCgcl2TDc\nPnyB+zbbBgdpW2U42PyPqdofB16+VlW3m+WBxtjX/NHcE67xgUvrY6605ycZzq5NZjzre4cMkxXX\nhrhM9Pm9gRtl2A+2JDtM0WCtW/mjsn497eQrfyQ5NMmdW2uzOIO91rIH3vEN87sZisRnZjlqOqvq\niAy1Pp/I8CI4tqpe2lp7+0TtvyjJgRnCz79m2Dl+JtO9YR6YYdt/c4HvtaxbdmapPj6OKP5jm8Ep\nhXGE6eNtWLprFqePHprkyRk++OaP7l2WZMlLbSV5blVdNr5ujs8QfF6Y4Yj3KZloQkhV3aq19v2q\nuk+G02B3S7Jthh3lTyfcgV0xTs55VJI3jPWjU9djPWJ+f0m+P9XZm9baW8b/XzJFe9dmHA1/cYZ6\n8PmjQJMd4NQwufJ1GUZGr0ryb0me31r75lR9ZMbv8SSpqhMynAn88Xj7Zkle1aZdim7yA6mqelaG\nsoI7VNXZ8761U5LPLqXtBcw/SLsiQ03v1Ae2N0vy1ao6PfNKDFprkxyMVNVnWmv3q6svtTaLJdZW\nZZjEu+H7b+8Jml+VYfL8TE+j11B3/ugMf+fKMA/gfa21ly2l3dbazA/45/lWpi/lupotVdLwmgyn\n8P4h679hvjhhH3Prus39f+MkH2qt3X+jP7z4Pr6WoXb04vH2zkk+11q7y7X/5KLb/3KGJdu+1Frb\np4a1U9/VWlvyrOTlNO64dsywA547RTX1jutLrbV7TNXeNfRx6Fzt5cTtbpXhdPb3MwSRe1bVp+de\nq1V1RptgPcKqek9r7fCqWp3ksRlmpK/KuDJAa22SiVJV9ckkH84Q1h+QYQmps6Y4rTqW9fyyjRf7\nqKq7JPmNJGumrisbz6o8LVdf+WPKgHVehr/9GZk3cXfi07anZVi2aK729bFJnttau/eEfWyR9/jU\n7/uxbGLOJAdSVXWTDCHxr7L+5M3LWms/Wvinlm48IPjx1IGrqhY8U9OGCZHXK+Nn+FEZVuhYO9Fr\nivKrqnpfhpKb7y21rY3087Uk+8yNjlbVDhlKyqbKITNf+WN8rvbOsFrQL+b1sWBZ2ebaUpPW7j7+\nP//IuSV50IR9LMeSIBdnGOWbc1mmnUTx3+Ppwiuq6lcyBIfbTth+krV1aofm6rVGk5xiXaYjxZmP\nMGWY3Pe7mfh5GssZXlVVleTZYwD+blU9I8MkpkkmUWTea6e1dkFVbd1mszLAYUl+N8nTWmv/Oe4w\np7pYw4czhNDzq+qOGUYr353kEVV179balKtBvDNDTdlDM+yrHpfpJ9de0lqbfIm+DdyotTZ/4uO7\nquqoKTtYpvf4VlV1s9bafyVr5zdM+hk2F3Rq3cUO/sdY3rXZH+5tWF3nJ0nuMUWQWsg4yvfe1tp5\n4/78Qxk+Z6+oqt9trX1sqr6WM9iOJZC3yvr72ylLKS5qE9WyL+AWSc4ZR8Lnh7ipy3L+I8Nrda4c\nYLsMk++mshwrf3x4/DdTWyTwttYmryNbwEIrD0xS0znPBRlWaPh/Y/uPSnL2XB1VW/rkltXj7/A3\nGUaAfpLhA35q/y9Dfd8ZmffGnNI42nCnrD9S9qkJu3hmhnrRK6tqbiLh1HVGM32exjKAIzOU4vxh\nhnWeb5bpass+M/7/s3Fi0ZlV9ddJvpdplyW7LMMktSur6s5J7pp1o4tLdbPW2vnj109K8p7W2nPH\n3+eMTLv82R1ba4+uqke11k6o4cprn56w/WRY5u4VGcqH5n8oLvls1xgIk+RDVXV0kr/PsJ+axbJC\ny/Eef1WSfxtHgypDmdpfTNh+argk9qsyzJ34QYZT3edmiR/uy1D7eliGSVfJ8L7YKkM5xp0z1L8u\nOfAuZ6nB2N9zM6yR+/2sG31tGUYCp/KiGq6g+fGs//6bopzvxRO0sRiXZCgxOTnD83NQktOr6vVJ\n0lr7g6U0vuGZuZrNyh+zWNbuarZUScNfZpgJOb8W6wWttcmW0tigv0lXHpjX7ouu7ftT1gBW1coM\nKzScvZGHbk7bX2mt7Tl1u/PaPyLD0nO7Zijg3z/Jv7XWphzRn7lZP0/LpdatDLBthtPpN8lwpcML\nJmr/jAyXn7xZhhrFL2RYs3rJE1XnSpTGrz+b5BWttX8eb5/VWttnqX3M62tu5Y9PZdjB/2eGS4NO\nWV97ygJ3tyneG1X1raybdLJQH1P+HsvyHq+qPbLuTOAnWmvnTNz+WWP7613soE1wOdjxdXSPDHNJ\nJq19nV/aUVUfyDCB+i3j7evrSjIXZLiowsxWb6qqd2U4IP9q5oXqKcuWZq2qnnRt32+tTT7hryZa\n+WNemd2XssCFJqZ+3W6pwLtQLdakb8oa1vd9VoYawmS4DvRb5mr/ri+q6ja5ekH9lKMmqarjMlzV\n68tTtjuv/S9nWJP1tNba3WtYQuUvW2tXWx1iCX3MLV2zW2vtz6vqthkuezrZRMVZPk9V9drW2vOq\n6l+y8Bt/uWenb7a59/I4QrNDa+2vpwqj4wfUf2Y4ZXd0hr/3z8YzIZ+cOPAekWEiyF5J3pGhtOT/\nzAWJ67LxPfa1GZb3bNjfcrzHl6OWcHVrbdUYfO8xlpRN9dqdWe3rWKd9RIYD2a8luWdr7Vvj985r\nEy8jVlX3S3Kn1trxNSwzutNcfxP2cUqSg5ZaQ72RPr42Va3rvDaXdSR81mrhlT92bq09dIK2d22t\nXVhVd1jo+228qulUtlQN79ZVtV1r7RfJ2iLr7Sbu400ZJsa9cbz9hPG+I6bqoIYLQfxRrj6pZZJR\njap6eYZTVedk/WVNJgm8tW5G8jZJnlLDFcR+kXVvzKlOHf28tfbzqsr4dz+vhslGU3pjhiP0B2U4\ntfeTDBN19ru2H9pE90vy5HHkbOrnaa7G8pUTtHWtavYrA1QNK0E8LuuuljNVycTTM4wkrsxw6e2f\njffvkQmfuxrqqC8d60U/lWTSZeE26GsWl8S+S5KXVdVTcg2X3p3otO2c5XiPn5R1AWKHzKaWcGYX\nO2itfbKGicdz+6TT2/pXPFyKIzNcGnyXJK+ZF3Z/I8mkF4UYz2yuyvAaOz7DmaJ3Zd263lP5ZpJT\nq+qkrF9uMNk62Ek+V1V7THmmoLV2v/H/mda1V9V7W2uPqfVXFpm/HVN9fs9s5Y/W2oXjl09rra23\n4tFYCTDFKkjr2txCI7x/nGEprOMzhIYnJ/lga+2vJ+zjakflMzjl+dEMK038r8y7Rn2baCHxGmZf\n7j13YDC1Wn9G8tW0iSZY1HAJyqckeV6GQPpfSVa0CReVnjeqOP/U3tR/7wWfr6mep3n9bJuh9i4Z\nRukmPStRM14ZYBzJekGSz7bWXl7DsljPW2ot2XKbG+2bcR8LLp840Sn0vTKcrl3ofTbpadvleI8v\n0Oe+SZ7dWptyEGNmFzuoqsdkmFNy6tj+/ZMc1Vp7/1LbXk5VdWaG0owvztvXnj1hwJrrZ8GSwYlL\nBc/NsFbupIMYtQwX6KiqW7fWvrdcn0uztNAZ/qk/v5MtFHiTpKoeluFKay3DpRZ/tbX2+xO2/8Uk\nj54bEh8/dN8/cdnEGW1YQmp+XeEXWmuTjCpW1Ycy/A4/maK9RfQ3NzM5yeSzYef6eGCGD5EPt2GB\n+qna/XyS+2a42ta+4+j7RzcsnZmor5k9T1V1YIZJJmsy7Hxvm+RJU5axVNXn24RLUl1LPzdOkuV6\n/U6tqo5J8sNcffnEyZaSqmVYPnG5zeo9fg19TXYVsVkbyyQOmhvVHfdRH5v6Q33Wal1t+9wgw44Z\n6rUnDbzz+pvZfmSWYbGGyezPncXn6HKa5ZnsqnpmhsHCO2c4WzNnpwwHVIcttY/5tlRJQzLUGrUM\nCyZ/K9Mvjn1UhhnQ38wQHG6fia+kkhleo370swwz6TecQTrpSFnNaGbyBn3Mr/naJcOVy6as+Zq7\nnvgtawbXE0+W53ka239Ia+1rY593zrDCwRTr8M4d7M1sZYCxn70yXBzl5sPNuijJE1trX52i/WU0\nt7OdfyDeMm15w9xSQrNcPnFWZROpYa3i30tyxwxrmb5tiprUa+hrZlcRW6a6y602KGG4ONOujrJc\n3ltVb0ly06p6epKnZsIVkGq88mRV7Zmh1Ovm4/0/zET7kar6ldbapVl/WdGpzfQCHXMWeM0mw8oN\nqzMsBrDUC8y8O8NB/yMy70z2Etuc894MK2QstEb1VOU+ay3rCO/44X34+G9u5OR/tdau9dT6Evrb\nLkOdUTKcGp60NKBmf436BWdftolnXdYMZyaP7a+t+Wqt3Xn8YH9fa23Smq9adz3xynDltUnXTJ31\n8zT2cbVTg1OdLqyFVwSY0yasPf9ckj9trZ0y3j4wwwSm+07R/gZ9Xd9Hkf9Phn3H/8xQc96S/E1r\n7YUT9jHLsol/yHDg/+kMV4L899bakUtt9xr6mn+K+4oMZ0E+0GZ8OdKpjAeZe2fdEn2HJTl7qhK4\n5VRVByV5SIZ97UdaaydP1O4tk/xFa+3ps9yPVNWJrbVH1MIrmbQ2wXyGWqYLdFTVnye5MMnfZfg9\nHpuhTOOLSZ7VWjtwie3P9Ez2vH4qQw36/Hklk10WO1n+wHtVhh3j09q4BFJVfXOKF9cCfS00O/iS\nJF+exZHDrMy6nnPsY2Yzk8f2Z1bzVevWGl3QxKefZ/o8jX28PcPku3eNdz0+w8jQ9WmZnOWon19v\nFDnDiMOTWmtfmbCPufWQb9dae0ZV3SnDQduJE7W/VZL9W2ufG2/PavnEmZVNzC8pqKptMgTpmS6B\nVcNFeFprbbLRuQ1Gqs9O8vY2g9UBarg4ztyB/qfb9FcH3HB1ok8mefMsPjdmoapekOT81toHl2M/\n0oNreJ7ObMNqKUt+vqrqtNba/lX1kQxnUv8jQ3nogisrbGYfz8ow2fzirL883B5T9ZEsf0nDIRmO\nPk6pqg9nWAh9oTUip/C0DNeOnxvVOjDDJJ3dquqlbf0rD22WqtotyXNz9StvTXVN8QOzQT1nVU1a\nzzma2czk0eWttVZVLVk7MWQqZ2TdEfrtMkyWqSQ3TfLtJLtN2Nesn6dk+LD6/Qyvq2Q4QHzjNT98\n09WMr6yX5JvjyOXce+zxGWZcT+ktSf5wg9Gf4zLUcU/l+Ayvr7k2v5vhcsyTBN7xgOkNGQ4GM56B\nmsUE1VmWTawNUq21K4ZBmtmoqlUZ/iY7jbcvyTB4snqC5k/IupHq38hQ/jH5SHUbLk0++eXJ51mO\n1YkOSfLyJLfMsK+dsuzj1Un+NMkHszz7kdSMLphSVftnOHtztwwrWWyd5KcTPU/z/ayGCZFzkx9/\nJ+ve81OMaL6shstjvyDrzmQ/f4J253tBkru11qYqlVhYa23Z/2W45vrvJvmXDIHhTRnqFqfs4yNJ\nbjXv9q3G+26e5CsT9XFWkj/IcKrwgXP/JvwdzsgwojR3+85JzpjR32OrDOHnSePvtPOE7f+vDAHl\nmxmWlfq3DMX8U/4Of5PkN+bdfniGdZevF89Thqv0/f6826dnqHH+ZpLfmfj3+HCGcqI/yrCjeUGG\nWq+p2r9ZhpGAL46v4ddmuELalL/DWYu5b4l9rB7//9IM+3hlhoOPmrLdDfr4PxkOAA/NsIbx95K8\ndKK2r8ww6XiuHvKKeV9fOvHvcXaS+8+7fb8MJQFTtP3leV9vk+Fs1FTbPfc+XujfNyZ+jpbjfXFB\nhnAy+Wt1g37m70e+mOR1M9iPHJGh9vy/MgyO/XeGC5pM0fbqDGcMvpQh7D4lyV/N4HnaPUOW+mGG\nM13/Mva7Q5L7TdD+LZbhb31qkq1n3c+WurTwTzPUm/zdeHT16CR/nOSjE3Zz29ba9+fd/sF434+q\naqrTOz9vrb1+orYWsqKNk5eSpLX29fGU1aTGv0eSXFXDmocXt/FVuBRVdccMBx2vHGu+Ls1QU/2h\nTH9p0/1ba0+fu9Fa+1ANl82dzNzzNL5mL81w4DTVVYD+KMPZjznbZpioduMMo1pTLl20a2vtYRO2\nt1YNy/H8aZv9EmTLMfpzeQ1rhM+dmbhDph+Bnbsk9hVVNbccVmsTjQKNZRMfb8NVLT9QVSdmwrKJ\n1trWU7SzSFe21tZe2rm19pmqmqrsYJYj1RsubbdVksdkGAiYdI3cDJdWv0Nbf3WiKzfyM5vq+23i\n+RELacMa2LPejxyZdRdM+fVxHshfTtV4a+2Cqtq6tXZlkuNruKLYn0zV/ri/fVRr7Tev4SGfuYb7\nF9P2byZ5e4Z905VJHtPG8qsZuCDJJ8b90/yJ1JPmqy25SkOStS/q48Z/Uzp1fPLeN94+dLxvxyQ/\nnqiP140TKT6aGcx2T7K6hut8z9VzPi7DUeMkxlMuxyT5UYb6mXcmuUWSrarqia21Dy+xi9dmfHO3\nYVLDyWO/e43fu6Y36eb4j6r6s6z/XE01g/vEJEe31r5SVbfOMNqwOsnuVfU3rbXXTtDNtq2178y7\n/Zk21B//aOISkGRYbH2vNoMrxrXWrqxhRY5Ze2qSl2RYaSIZTkVPXef8ogyj4betqndnqL188pQd\ntBkvTt+Wr2xiZmrd6iKfrGF1gPdkOAg5LMPI0BT2qapL57pMssN4e8kHIHMHxePBxxMyrCB0ZpKD\n28SXRs7yrE60uobJiv+c9T/3JrmQSV3D1Sbn9TPlKgezvGDKz8Y5OGeOgy/fy8Srcoz728OTvGbK\ndkd/keGMynlVde8kf53hLPYsfG/8N9Or0G2xdXhnbZzxN3+CwGczzOid7Beuqr/KsAP7RtYvtJ5q\ntvt2Geo55wLEp5O8oU20tmVVrc5wJZObZDjgeHhr7bTxKPc9bYlr2Na1zOSsidfPHCevvSjDZI25\nq9G9tE0waa2qvtpa+7Xx6/+d5K6ttSdW1U4ZLq4wxeS7C1prd7yG732jTTtB4JwMp7xmccW4VNWb\nMiw7976svxzPlFf2WhZVtXOS/TM8R6e11n44gz5mUkM4r/1XZigj+scp93/LpZZpdZFZGc/KPTVD\n3eNnkhzTxknbM+pv1qsTHb/A3a1NNLG21q1ucEiSX826QYzDM4wuT1Y/WjO8YEoNa/x+P8PZuudn\n+Jx9Q5v4crlV9ZoMddsbrhe+pIG32uBiEBvevj7qNvAuh6q6IMkeUwXQBdp/QpJ/bvNmI1fVI9p0\ns8TPbK3dffz63Nba3eZ9b+0Vy5bQ/vmttTtdw/euMeAtsc8d55VoTNXm/Ofp4xmWjfr7Db+3xD7e\nneTU1trfbHD/M5Mc2Fo7fKl9zGtzplfmmeUH4nKM/oynCXdo41Jn45mQbcdvf6lNuzrAERlOq+6a\nYdRv/wyL+E8W4mpYp3PHDPW1k5dNcO2q6sIMz/1rM0ykXc8UB4K18KpEk/ax3GqBKx0udN+E/U16\nwZSqOrK19rqN3TdBPwsdEC75QHB83c6/jPMfzr/dJrzEc1XdIsNckg3XCn/IVH0k14GShllZphmS\nX8kwGWRWy5wdm+QFVXX4vJqpl2aiWeJZNyqdDMX6801xJLS6qp6+QIg7IsNkpslU1X0zrDF64yS3\nq6p9kjyztfbsCZr/TlU9N8Nah/tmOM2dsb5zqprq5+f/t3fmYZJVVbb/rSpAEC2GFmykARnEAYRS\nwBH7CTg0ggrIYDEjPsWW0eeMAzihOKCCOIHMoKKC0igNIpOiDKVgATKJ8gBR0QZFQERY/cc+UXkz\nKzJryHMjMiL37/vyy7w3Ms45mRkZd9999l4Lzpa0K1EyAVHD+zhgu0pzACOBrcY4xlUcv/YWapNP\nlc9dsz+V5vgE8T/dqQE/nfhfX47429TUTW21hhDaL5voFZJWBPZkQXWRqW5Z/UPi/XTj8tHEjJTl\nTIZOediqhKrIhcSNzRbA5ZXmAOZLuO3LgsFJ7ZKi5SWt42KcoFBFqlbepTH2v65vmLIX0WjXZO8u\n5yaF7S1qjtfgqxRFlHGOa3IqYRy1PbGrvRfRYFuVoQ14gWOIJqAziaaBPRnRs63FisCNkq5ipJbJ\ntl9bafzfEG8s35J0mO0zqSvj1qlba9asUY5rBEIHA2dJ2o2RAHdT4gZk+wrjNzkKeCUhZ4PtayX9\n+8RPWWT2JW40XgbsUhqAILJx3bKZi41DG/pFkrZkxLntXNs/qjF+E7XkGCdpA2BdF+OVstW2Qnn4\nmBq17Z2LkqRPj8n0nFNKdGqwFRGEdviL7deUMqnLxnnOktJmDeF82i6b6BHfB35GdNU/tpDvnTLY\n3rsHc+wDIOl8Ytfx7nK8GnBi5elOAW4k3m8/RPRLtNHEdgjRd9OsR35zrcFL/etNktZ0XXv4OYQK\n1dqSmiZUTyT6Zaoi6cnETfJTbG8t6VnAC20fP5lxbR9eZYGLxiq2vyzprbYvlPQj4IrakwxzwNt6\nhyRRM9pBwEsY3Wk/WWz752Wr5YxSOF6tK9otd1g7VDJepHAk27CcbiWIK/PdodHd1VW6k0swul+X\n8xcxovNchfK7aeX30+DDRLA+yjGuwrgfJywiO7ySkMR6PPAB6maq28z+zPBo04F3Qfwzqji7VeTO\nkrk8G7hA0r1AldKSDuOVTRA1i4PEsrbftvBvm9as0Ql2C38g9Mlrsp7tnSS91vZJkk6n/o0gts9T\nmL08o5y6sXY9Mu3Y/15ONGA9iUgsdLifkNarzYlE4uXQcnwzUc87qYC3x3RUUn4v6ZVEw/m/1J5k\nmAPeXnRIXiLpOcTd3E5ERvZLFae4u8zzp/Ii+AQjgePA0EZg2IU7SlmDS5PIQbSTdRgGHrH9Z0kz\nFL71F0mqoTSxmkfL1vzVIbTfqUWuSbfsz5sqjb2MpCd2anVtnw+gEF+vWgJiu7PTcVipxVuBUjJT\nkdbLJnrEKZL+L1HS1VQHqJ41G2AuVDhiNe2Lf1h5jk5wcp+kDYmt51Urz9FhE0ZKWDaWhO2TK47/\n/opjAfNLxm4njK96wZNsf1NSRxGpIyM2SHysvL++nbBYn0UojlRlmAPePYhs6P7ExXENQrVh0kha\nn6gZnEOIPX+DaACsWhQWu7EAAB7gSURBVEtje5vG148RL4DqL4IhYT+iNmp1whHrfKIWKFmQthzj\nRtV32X5B47DqBbHl7M9XgW9I2q+z1Vka/b5I1IlPGo22sp0HHN9CDWGHnpRN9IB/AJ8kMlmdHgMT\nwvsJYHt/SdszYi38FVe2Lwa+Ukpk3k+UkD2B2MGpiqRTgHWJXYlOAGfCUrwKJWn1ZEZKmK4sO3qT\npkd9RAAPKNRkOnrhLwCq2pO3TacMjmIu09Y8qdKwBEh6jNjC2ddFXkbSbbarvvFKWoXYTn0Wo2vv\nBm0rMplCKHR9HyJ2PHYjsoqneZImGiVD+W7bV4w5/wJCiumlkxm/l0jaj5Ds65RJ/I34Gb5Yafxv\nMGJluzVwu+3qVrZlrtakl3pJyeY/zy1Iw/WCsvP0FkaC0UuAL9muZYQ0VEj6FVGP3FqQorDk/SSh\n59wpS3yH7Ukb/ZSeggX6iGzXLKvs6FQfTez+XgesQrhzVimfKJnXwxgJRC8hJD+rBdWSjifcPu8r\nxysBR7phJlVlnmELeCXNY3yFAdse2yW7JHNsR7yQX0xsP34dOM722pMde8w85xPZ47cT2aC9gHts\n1+wSH2gknd+RLpH0HttHLOw5k5jr34g3ls2J19hlwEG272xrztqUzuQfttHZK+l5xOv1REYrTexF\nNPtdWXvOtlFoLeOKUmRl3Pk61JKWIjJLrWtcqrL0Ui8p74fb2X6w32tZEhQmQksDJ5VTexDucW/s\n36oWHUkT1U8/TOjRn192I2vMdyZw4Jia5KpIuhZ4eSerW5JMP6wUJ1xte1NJv3TROFcFuc/G+JsB\nd9j+fXkPeTOxi30D8IFapT6Svk0E0s3X7ca2J5TCW8w5Fvi9qAXd32Esadi2yzkRJQ1V7qxsn01I\nSC0PvJbInKyqENw/q1PzV4F/sX28QrvvEsJp6KpKYw8LqzS+3onRTVO1OYGQqNqpHO9ezr28xTmr\nUjqTH5O0Qs079DL2laWxcn9GHMmuJ2yfa0mG9ZTagW6DNq1sgZ6XTfSCB4iejIsYXcM71WXJOmw2\nJpD6UQm4BoWJJKlWItRN3kDYJtfgScANpaGs+feu6bQ2Y0wJw5+p1+vTdh/RlwnlIAgpukOBA4DZ\nhJHUjpXmWdd2sxz0cEnXVBq7w4zmNalkeGtJfs5n6AJeN8TzuzSUfbvyXA8QAdDp5Q+0E1GCUCvg\n7VwU75a0DdG5uHKlsYeFXm5RrGK7KUN2oqSDezh/Lf4GzJN0AaM7kycdOJSLR/V6vm4o5NXmbw/b\nPqcX81aiNSvbBicxumziWUQD26BydvkYVB6VtK6L05akdaikJNNEoQ++pu2bao7rRZCpklRTheCw\nimONx3ldmvx+UGnsPYgAt3ofUWFmI4u7C1Gv/W3g25UD0ockbW77xwCSXsyCuv2T5bPAT0upl4ib\npiMnfsriM4wlDd0ayt5uu6u71FRG0rbExWoNYit9FnB4o8B72iPpPqL5qlN/NUpbtGY2QOGydgIj\nb45zgH1sb1Vrjl4gaa9u522f1O38VERh6/084LRyag5wle339m9VU4t+lU0k3ZG0FfH+0VQW2aeo\n2NSa49WEOcsytteWNJuot6yZFe0ZbTWUjZljB6JMDeCyyTb5lbKIVWzfMOb8BsAfbd8zmfEb410H\nzC47RDcCb3LR1pZ0ne0qik4KE6eTiVIoEVrCe9uuujshaSOix8DARbVqkEfNMYQBb08aypKpgUZ8\n17tScwu3dOofTcjNmNBbPMD2HbXmSBaNkkma3akXLLXJv+jUylWaYyei1vV+Se8jXPY+4goGGr1g\nbA1cGzVxvaSochzBgk28A/PeLulxQEch46aKyiKd8ecSQcPFnZrI5o3PINFmQ9kEc84A5tg+baHf\nPP4YXweO9RhjF0kvAd5ie9dJLrMz3qHAq4jE3prAc21b0nrASbZfXGOexnyzAGz/dWHfu4Tjb0Ds\n2Jm48bi+9hxDV9JAWI6+HrhIUqehrH6BXA9QiOkfwIJWmgN5t94GPa5J/Lexv/uyvTNQAW+vAgcV\nkwbbf6s5boMVGXEuWmGib1xC3m/7TEmbE7VynySkyZ7fwlxt0IuyiV5yAmH2cxRhmbsPlbXV26Bk\nELuxnkJXtprtL6Gx/ZcxNeGDmtU6lKh7HtVQBtRQUJhFyFauTkirXVCO3w5cy8jO0ZKw3thgF8D2\nZaXPpwq2P1p2HVcjmgU7f+cZRNxQjVJSuQGwbOe1ZftDFcffH/hPwl5YwDclfcH2sbXmgCEMeHvY\nUNYLzibcUs5hgKw0h5ijiSzfws5NdVoNHCQ9m9gCWzkOdQ+wl+3ras1BBOy/KA1MIjID7644PozU\nV25D1MedK+kjledoDbfspNgHlnPYjqr0ahxWMpo9qRmfBK8un1clmosuJF6zWxC7RDUD3usl7QrM\nLDe2B5Y5qqGWrGy70GZD2SmEPN9PgTcSEoQiVEAmW/86UXNf1UYs2z/rcu7mGmNL2t/2MZK+RLhl\nbkHokO8I1FbceRMhOfi3MvfHiNdtBryLQg8aynrB321/vt+LmO5IeiFxoVpFo6V5ZlHR6rmHtB04\nfBl4W6c2UdJLia7hF1UaH9tnSLqYkfq+d9n+fa3xC3dJ+jKhwvGJsh095TOKQ8zDZcv5lpIRuosw\nPZjS2N4H5suqPctFZkvSaoSEX00OIDKjDxO9Bv9NWInX5ER6Y2XbZkPZOo369uMIBYU1bf+9wti3\nSnqV7e83T0ramqjfHhReDhwDvMj2Rgp5tcMlfZp6f4cOIoxlOjxCCzvzQxvwNrF9L3HB/Uq/17KY\nfE7SB4kgvSnLMhA1hP2gpW30ZYgL61KMvnv/K/WkX3pJ24HD8s1GHNsXlx2XapRSkmtsf0/S7sA7\nJX2uqdJSgZ2B/wA+Zfu+EqCk02H/OIjINB1IBHFbEhrPg8IaHq0p+wei9rIaDo3iQxkJRtugJ1a2\ntt8xpqGspmtcUxbwUUl3Vgp2IXaVzy01yHPLuU2J3o9usqlTlZXK587v5UFJTyEy7avVmEDSUrb/\nSWTcr1Bo/gJsz4jubzWGrmltmCid6HsQgt6dkgY7ndYWYOw2OlB9G13SWpUDqr6gECz/FVED+2Gi\n/vXIbttjSzj+WYTxxCnl1O7AJra3rzF+meOXwMbARkS26XhgZ9sTNjEu4VyrMrrW+f/XniMZfiQd\nAzyN0RnLW21Put5S0jlMUKtbWa3mYkJe6wLbz1U4KX6ijf+9MfNOuqGsMdajjEgyClgOeJBK9e1l\nN2hXwv0MQo/89IpBdetIepbtGyS9nyjd2wr4AvE6+6rtSe8INhtpFcZFTbWM6p4DGfBOYSTdSmyB\nDZQjUj+QdDlw6Jht9I/ZnvQ2uqTP2j54vItKNhGOppQQHc5oR7rDXGwjK83x83Kx/QBwl8OgpaoK\ngULn99PAU4A/Etm4G21vUGuOZNFRSE6+g5DzajbxDkwCQNL2jGhHX1orY9lQq9kB+Ffg1HI8B/iD\n7UNqzFPmatvKdsKGMtuvrTFPsviUQH5ZVzItUkXnuUWaLwPeqYukswltverag8OGpGs9xg6y27kl\nHHsT23M1jgRaj5UilhhJE+o31wrcJe1k+8yFnZvkHJcQtt77EAHEH4mLYTX5JYUL1paE1ehzJG0B\n7G5731pzJItO+Xt8idgmnr+FbnvuuE+aZqjY2S7sXIV5liLk1UTIqz2ykKcsztjfZaShbCui2U+E\njXtth69kHNSwLi7HexKZ/duJBMakrYsl3Ql8ZrzHbY/72JIwLWp4B5gVgRsVdsKdGl7nHW5Xbitb\nL81t9CoNAiXYnUncfOxWY8w+8UJCQu0M4Arak+t7DzA2uO12bjLsQmwZ7uvwkl+TkA2rySO2/yxp\nhqQZti+S9NnKcySLzj9tV5N1GlKWl7SO7dtgvrRllfp5jS+vtr7qyqu12VCWLDrzrYsl/Tvwcepb\nF88k+kd6Ih2bAe/U5oONrzvC26/v01qmOm8gttG/w8g2+j61Bi+NDWtJWmaAS0z+lei8nUMEi+cC\nZ7iSwHfpQn4VsLqkprrILOCfNeZocD/wufJ3WR94BiO1kbW4rzRBXgqcJumPNKyYk55zjqSOVmez\niXfSmaYh4hDgYklNN7c3Vxp7rLzaj8pxbXm1NhvKkkWnF9bFd7uinu/CyJKGKY6k5xDByU7Ab4Dv\n2D66v6uaevRoG/1k4JlEXdn8wKf2tksvKLVYc4is6OG2j6kw5sbE3f+HGC1xdj9hFXnvZOdozDWX\nuAFcCfgJcBXwj5oZ+KIs8XcicNiNaO47zfafa82RLDqSftPltD1YTmvLEdnKm1qc43HEDSBEzXlt\nN7fziYbgUfJqtl9ZafxWG8p6hQbcGVA9sC7udQ1vZninICVjNad8/InQOJTtLfq6sKlNL7bRf10+\nZjCxuPiUpVwMtyFeW08FPk9kzCaNw1v9Wkmn16zpGwfZflDSvoSN55GlxrMaDi3vDtUlcpJFp3To\n7277J/1ey5Ii6dXApwiZw7UlzQY+1ELT6yaMuHNuXMoNTq44fqvyah4ew5SBdAZscAZwiaQ/AQ8R\nu6YorIurNK0RNdo9IzO8UxBJjxEvrn1t31rO3TYod4a9pLGNvjNxY9BhFqFw8bxJjj8HuNr2LZMZ\nZypQMtQbAt8Hvl5Tsm3MPK1nNiT9grCiPIr4P7le0rzKTWs7AJ9gpGlmoDJMw0avs0G1KbsSWwIX\nd36OFl6zpwDrAtcw0thn2wdWnKM1ebVhQtJc25s0/8adc/1e26JSJOc61sUPlHPrA0/wAPoBZIZ3\narIDUat7kaTzgK/To6LuAeR3wNXAaxgR+YbYRq8hxXM58AVJHyec+gZZlmx3YqvwIOBAaf5LqnYg\n14vMxsFEBv+sEuyuA1y0kOcsLkcCr7b9q8rjJkvGhZJeR5R1DWKm5hHbf2n838EE2rlLyKbEjX5r\nvx/b+4+RV6tpCDFMDKQzYBO3aF3cDzLDO4UpNYSvJbaftySMFc6yPUj2yD1BI44tbYw9A1iPcdxl\nBkWWrFf0MrOhdpz1OmP/xPaLa4+bLBmS7icUBx4ltlgHKuMu6XjgQuDdhLzTgcDStverOMeZwIFj\nSg6SPqAFDX5mEQY/V/R1YdOYDHgHhCLmvxOwi+2e1r1MZSR90/bOkubRPfu6UeX5liEaQkzoTw6q\nYkNrKExANge+RXRy3wV83PbTK87RzVlvz1qKE2WOzxHKFmczWhWgVjd6Mo2Q9HjC8vcVxGv2v4EP\n11QgkHQR0Th6JaNfs4OyCzU09KKROlk8MuBNBhpJq9m+W9Ja3R53RStgSdsQwve/Ji5YawNvtv2D\nWnMMA10yG1Wti8scrTnrNeY4octp235DrTmSRUdRC7AbsLbtD0taA1jN9pV9XtqUQQNujjNMqIvz\nY7dzSe/IgDcZeIopxA/bVrEo0izbNhoJ1wXOtf2MiZ+Z1EYtOuslUxNJXwQeA7a0/cyy63W+7c36\nvLQJ0TiW5B0GMftadrrWL4dVndYGnbYbqZMlJ5vWkoGniJM/JmkFV/L4Hof7O8Fu4TaiOS6hd9bF\nhdac9SS9s8icHU33MplqHe/JYvF8288tCh3YvrcEXlOdT5XPOxAlMqeW4zmEpFc1Slf90YRe+DKE\nk9UDNeucy27KScBviZ2uNSTt1dFoTVpvpE6WkAx4k2Hhb8A8SRcw2hSiZnBytaTvA98kAqGdgKuK\nfFXWdvbOuhi6O+vVKjXoqDJcXWm8pA6PlN0cA0hahcj4Tmk65QSSPm1708ZD50iq/Ro7hlD4OZNQ\nbNiTkUxsLT4NvKJjnlFkqs4g9H+nPT3WI08Wgwx4k2HhOyxobVm7XmdZIiPTqZO7h3ABenWZa7oH\nvK1aF3coQc+hbWVabZ9TPqfZxNSiY5KyqqSPAjsC7+vvkhaL5SWtY/s2AElrE6oTVbF9q6SZth8F\nTigZ8fdUnGJpN5zibN8saemK4w8Lz5N0GGHvvBQjqiKpp98nMuBNhoUVbX+ueULSQTUnsL1PzfGG\njXKBPQ84TyPWxRdLqmJd3JxH0ua1xhtLj0szkkXE9mnFvGErInjYbsA0kg8h/h9uI9a/FvDmynM8\nWMo8rpF0JHA39TWwr5Z0HCOlGbuRuyHdOJ74m89lxAQk6SPZtJYMBeN0xFZ1ZioXkI8QGqDnARsB\nh9g+dcInTiO6WBd/D/ia7bsqz/NFYHVi67ZZwjLpLLuke5igNCM73nuLpGWB/Qgt7HnA8W1pbrdN\n+f/oNLneaPvhib5/CcZfi9iFWoYItlYgrLdvnfCJizfH44C3EtKDEOVEx9b+WQYdSVfYfn6/15GM\nkAFvMtAU699diTffyxoPzQIeralZLOka27OLy9C2wNuAS1MZIOiVdXGZqzXJsFIy0SnN2IiWSjOS\nRUPSN4BHiP/vrYHf2j64v6taMiS9iLgRnL+7avvkSmPPBE62vVuN8ZLJUdw5ZxKlbk1N5IGz5B0W\nMuBNBpqS0VgbOIJwMOpwP/DLmpkgSdfZ3rBs533L9nkphTWCpMcYybY231gGyhGrSaM045NA1dKM\nZNEY49i3FHDlIGqZSjoFWBe4hpEtbtesRZf0Y0K2rbohTq9NfgadYgIyFtvesueLSYCs4U0GnGIs\ncbuklwEP2X6sdA0/g9j+rMl/FS3eh4C3lC7xai5Jg47t2rWCCyBpA2Bd298rx0cR27YAx9TKnnQp\nzeg0TCW9Z36nu+1/hv/EQLIpocPaZpbpNuAnpQ69WerzmQpjd3oitq0w1tDTti58svhkhjcZCkoz\ny0uAlYCfAFcB/6i9vSdpZeAvpXFqeeCJtn9fc45kfIqI/xG2Ly/HNwDvBx4PvM72dhXm6FlpRrJw\nJD3KSPAmQhnlQQZs50DSmcCBtu9ucY4Pdjtv+/AW55wBzLF9WltzDCrFnXMDQuEHANsf6t+KpjcZ\n8CZDQadpTdIBwHLFOOAa27MrjP1O20eWr0d5oUv6mO33TnaOZNGQdHVTy1TSz2y/oHz9Y9uTVm8Y\nxtKMpP+ULe7ZwJWMrukcCNUPSbOIZrXViWbUC4D9gf8HXGv7tX1c3pRD0peIG/EtgOMIGb0rbe/b\n14VNYzLgTYaCojX5n8BRwL62r2/W/k1y7PkKEGPVINIbvbdIusn208d57GbbtUX2k6QKkv5Pt/M1\nVD+KTN86nQY4Sd8CVi4Pf8T2jyrM8V3gXuCnhDTcqsRN4EG2r5ns+MOGpF/a3qjx+QnAD2y/pN9r\nm65kDW8yLBxMiKufVYLddYBuTQNLgsb5uttx0i6/k/R821c0TxZL1d/1aU1JslBalrM7HDigcfx0\nYG/C2OK9wKQDXiKg7jQPHkdo/K5pO/sYuvNQ+fygpKcAfwZW6+N6pj0Z8CZDQbmYXNI4vg2o1f3s\ncb7udpy0y7uAb0g6Eeg0qG0C7AXs0q9FJcnCKDdlRwPPJHRyZwIPVCqRmWX7hsbxLbbnlnmPqDA+\njG4efFTSnRnsTsh/SVqRUHj5OXGt+Gp/lzS9yZKGZKCR9FnbB5dmpm5SOZOuj2s0zTQbZijHy9pO\nW80eImlVonZwg3LqeuALtv/Qv1UlycRIuhp4PWGWsimwJ7C+7Unb/kq6xfbTxnnsVtvrVZhjKJoH\n+0FRfVnW9l/6vZbpTGZ4k0HnlPL5U21NYHtmW2Mni4/tPwIf6Pc6kmRxsX2rpJnFhvuE0nsw6YAX\nuFHSNrbPbZ6UtC1wU4Xx831wEZG0GXBHR71H0p7A6wj5zMNs/09fFziNyQxvMjQUXVxs39PvtSRJ\nkjSRdCnwMqJj//dEDezeNYxrJK1HOAJezuhSnxcB29q+ebJzJIuGpJ8DL7P9P5L+Hfg6UV89G3im\n7R37usBpTOtC8UnSNpIOk/QnIpNxs6R7JGUGMEmSqcQexDV3f6I0YA0i8zdpbN9K2GBfRhilPBW4\nFNgog92eM7ORxd0F+Irtb9t+PzDp0pJkyckMbzLQSHobsDXwJtu/KefWAb4InGf7qH6uL2mXIvWD\n7b/1ey1JMh6SZgIn1zbCSaYekq4DZhdXwBuJa9Olncdsb9jfFU5fMsObDDp7EC4/v+mcKAoNuxNN\nIckQIunZpf7xeuAGSXMl5YUkmZKUmt21JC3T77UkrXMGcEnRLX6IyLp3yk6yaa2PZNNaMugsbftP\nY0/avkdSqicML18G3mb7IgBJLwW+QtQsJslU5DbgJ5K+x4jaAbY/078lJbWx/VFJFxKau+d7ZBt9\nBqO1kpMekwFvMuj8YwkfSwab5TvBLoDtiyUt388FJclC+HX5mAE8sa1JSha54zh4k+1HJvr+pD62\nf9blXNZS95ms4U0GmjHakKMeIjVyhxZJZxHd6B1Zut2BTWxv379VJUl/KTsdJwG/Jd4D1wD26tSQ\nJsl0JgPeJEkGDkkrEXaqmxOGI5cBh9m+r68LS5IxSNqcsOU9uRx/C1i5PPwR2zVsfztzzQV2tX1T\nOV4fOMP2JrXmSJJBJUsakiQZRF5me5R1tKSdCBerJJlKHM7o2s2nA3sDywPvBaoFvERPw3yjCds3\nZy9DkgSp0pAkySDSzZ2qhmNVktRmlu0bGse32J5bygxq1/JeLek4SS8tH18Frq48R5IMJJnhTZJk\nYJC0NfAqYHVJn288NAv4Z39WlSQTsmLzwPYOjcMnV57rLcBbgc7ux2XAsZXnSJKBJAPeJEkGid8R\nGavXAHMb5+8HDunLipJkYm6UtI3tc5snJW1LuENWw/bDwGfKR5IkDbJpLUmSgUPS0im3lAwCxXDg\nXOByQlkEYBNCM3rbGnJVkr5pe2dJ84gmzlHY3miycyTJoJMBb5IkA4ekpwFHAM8Clu2ct71O3xaV\nJOMg6XHAbsAG5dT1wOm2/15p/NVs3y1prW6P2769xjxJMshkSUOSJIPICcAHgaOALYB9yCbcZIpS\nSg2+1uL4d5fPowJbSTOAOUAGvMm0Jy8QSZIMIsvZvpDYpbrd9mHANn1eU5L0BUmzJL1H0jGSXqHg\nAMLOeOd+ry9JpgKZ4U2SZBB5uGSvbpG0P3AX8IQ+rylJ+sUpwL3AT4E3Evq+ArazfU0/F5YkU4Ws\n4U2SZOCQtBnwK0Ly6cPACsCR3Tzsk2SqIGkZYP1yeFOtxktJ82w/u3w9E7gbWLNWjXCSDAMZ8CZJ\nkiRJy0h6KXAS8Fsi+7oGsFcxoJjs2D+3/dzxjpMkyYA3SZIBQtL3Jnrc9mt6tZYkWRwkzQV27Vj/\nSlofOMP2JhXGfhR4oHMILAc8WL627VmTnSNJBp2s4U2SZJB4IXAHcAZwBXFBT5JBYOlOsAtg+2ZJ\nS9cY2PbMGuMkyTCTGd4kSQaGUp/4ckJqaSNC0P8M29f3dWFJshAkfQ14DDi1nNoNmGn7Df1bVZJM\nHzLgTZJkICli/nOATwKH2z6mz0tKknEpr9e3ApuXU5cBxxaN3iRJWiYD3iRJBooSOGxDBLtPBb4H\nfM32Xf1cV5IkSTJ1yYA3SZKBQdLJwIbA94Gv276uz0tKkgmR9E3bO0uaByxwwbW9UR+WlSTTjgx4\nkyQZGCQ9xkg3evPNK7vRkymJpNVs3y1prW6Pj7UDTpKkHTLgTZIkSZIeU5wC59g+rd9rSZLpwIx+\nLyBJkiRJhhVJsyS9R9Ixkl6h4ADgNmDnfq8vSaYLmeFNkiRJkpaQ9F3gXuCnwFbAqkQJzkG2r+nn\n2pJkOpEBb5IkSZK0hKR5tp9dvp4J3A2safvv/V1ZkkwvsqQhSZIkSdrjkc4Xth8F7sxgN0l6T2Z4\nkyRJkqQlJD3KiLKIgOWAB0llkSTpKRnwJkmSJEmSJENNljQkSZIkSZIkQ00GvEmSJEmSJMlQkwFv\nkiRJkiRJMtRkwJskSdJjJO0t6SmL+ZynSrqurTUlSZIMMxnwJkmS9J69gcUKeJMkSZIlJwPeJEmS\nSkjaXdKVkq6R9GVJMyWdKOk6SfMkHSJpR2BT4LTyfctJ+oCkq8r3fUWSynibSLpW0rXAWxvzLCvp\nhDLmLyRt0acfOUmSZCDIgDdJkqQCkp4J7AK82PZs4FHgfcDqtjcsblsn2P4WcDWwm+3Zth8CjrG9\nme0NCZ3WbcuwJwAH2N54zHRvJTRcnw3MAU6StGzrP2SSJMmAkgFvkiRJHbYCNgGuknRNOV4ZWEfS\n0ZL+A/jrOM/dQtIVkuYBWwIbSFoRWNH2peV7Tml8/+bAqQC2bwRuB9av/hMlSZIMCRnwJkmS1EHA\nSSVrO9v2020fBGwMXAzsBxy3wJMiM3sssGPJ2H4VyGxtkiRJRTLgTZIkqcOFwI6SVgWQtLKktYAZ\ntr9NlDc8t3zv/cATy9ed4PZPkp4A7Ahg+z7gPkmbl8d3a8x1WedY0vrAmsBNrfxUSZIkQ8BS/V5A\nkiTJMGD7BknvA86XNAN4BHgbcFY5BnhP+Xwi8CVJDwEvJLK61wG/B65qDLsP8DVJBs5vnD8W+GIp\ngfgnsLfth9v5yZIkSQYf2e73GpIkSZIkSZKkNbKkIUmSJEmSJBlqMuBNkiRJkiRJhpoMeJMkSZIk\nSZKhJgPeJEmSJEmSZKjJgDdJkiRJkiQZajLgTZIkSZIkSYaaDHiTJEmSJEmSoSYD3iRJkiRJkmSo\n+V/x2u912iRS8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11792c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "por_estado = df_completo.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(figsize=(12,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117d83f28>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IY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\n/wM3bXCIe7MyXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e52c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "por_partido = df_completo.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(figsize=(12,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1185dc9b0>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WsOzb2pIfT7b+KbnTWPJsSfK/Jrldkvt298O6+8Ik98vq82w/dJCDHKYtw485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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118629400>"
      ]
     },
     "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(figsize=(12,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118783518>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MOXuGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWG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\nqfRbgWcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1187b2320>"
      ]
     },
     "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(figsize=(12,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% de senadores</th>\n",
       "      <th>% de gastos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partido</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>6.024096</td>\n",
       "      <td>4.868811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCdoB</th>\n",
       "      <td>1.204819</td>\n",
       "      <td>2.054714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDT</th>\n",
       "      <td>4.819277</td>\n",
       "      <td>3.639673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMDB</th>\n",
       "      <td>26.506024</td>\n",
       "      <td>23.452885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PODE</th>\n",
       "      <td>1.204819</td>\n",
       "      <td>1.470128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PP</th>\n",
       "      <td>8.433735</td>\n",
       "      <td>9.029590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPS</th>\n",
       "      <td>1.204819</td>\n",
       "      <td>0.463418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <td>4.819277</td>\n",
       "      <td>5.712120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRB</th>\n",
       "      <td>1.204819</td>\n",
       "      <td>0.475002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSB</th>\n",
       "      <td>7.228916</td>\n",
       "      <td>8.973503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSC</th>\n",
       "      <td>1.204819</td>\n",
       "      <td>0.199918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSD</th>\n",
       "      <td>6.024096</td>\n",
       "      <td>7.361149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSDB</th>\n",
       "      <td>13.253012</td>\n",
       "      <td>11.665048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>10.843373</td>\n",
       "      <td>13.692653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTB</th>\n",
       "      <td>3.614458</td>\n",
       "      <td>3.098300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTC</th>\n",
       "      <td>1.204819</td>\n",
       "      <td>1.810908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REDE</th>\n",
       "      <td>1.204819</td>\n",
       "      <td>2.032181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         % de senadores  % de gastos\n",
       "partido                             \n",
       "DEM            6.024096     4.868811\n",
       "PCdoB          1.204819     2.054714\n",
       "PDT            4.819277     3.639673\n",
       "PMDB          26.506024    23.452885\n",
       "PODE           1.204819     1.470128\n",
       "PP             8.433735     9.029590\n",
       "PPS            1.204819     0.463418\n",
       "PR             4.819277     5.712120\n",
       "PRB            1.204819     0.475002\n",
       "PSB            7.228916     8.973503\n",
       "PSC            1.204819     0.199918\n",
       "PSD            6.024096     7.361149\n",
       "PSDB          13.253012    11.665048\n",
       "PT            10.843373    13.692653\n",
       "PTB            3.614458     3.098300\n",
       "PTC            1.204819     1.810908\n",
       "REDE           1.204819     2.032181"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf"
   ]
  },
  {
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