prof_09_organizacao_dados.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Organização dos Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidy Data\n",
    "\n",
    "Algumas formas de organização dos dados são melhores para uma pessoa analisar outras são mais interessantes para a análise computacional. Hadley Wickham, no artigo [Tidy Data](http://vita.had.co.nz/papers/tidy-data.html), publicado no Journal of Statistical Software, aborda o assunto de forma simples, exemplificando situações onde os dados precisam ser reorganizados para facilitar sua análise. Wickham afirma que:\n",
    "\n",
    "> \"Tidy Data é uma maneira padrão de mapear o significado de um conjunto de dados para sua estrutura. Um conjunto de dados é bagunçado ou organizado, dependendo de como linhas, colunas e tabelas são combinadas com observações, variáveis e tipos.\"\n",
    "\n",
    "Em sua abordagem, são definidos três pilares que devem reger a organização de um dataset:\n",
    "\n",
    "* cada variável forma uma coluna\n",
    "* cada observação forma uma linha\n",
    "* cada unidade observacional forma uma tabela\n",
    "\n",
    "O artigo descreve, ainda, os cinco problemas mais comuns encontrados nos datasets e quais técnicas utilizar para reorganizar os dados para que os pilares citados acima sejam respeitados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dívida e PIB por UF\n",
    "\n",
    "## Datasets\n",
    "\n",
    "### Dívida dos Estados por Ano\n",
    "\n",
    "Este conjunto de dados abrange o estoque da dívida dos Governos estaduais e DF, segregado por administração direta e administração indireta custeada pelo Tesouro estadual.\n",
    "\n",
    "#### Arquivos:\n",
    "\n",
    "* dados/de_relatorio_estoque_final.csv\n",
    "* de_metadados_relatorio_estoque_final.pdf\n",
    "\n",
    "#### Origem:\n",
    "\n",
    "http://dados.gov.br/dataset/estoque-da-divida-de-estados-e-do-distrito-federal/resource/f9a981c0-6281-43b2-9238-5270eacd4a77?inner_span=True\n",
    "\n",
    "\n",
    "### PIB dos Estados por Ano\n",
    "\n",
    "PIB Estadual a preços de mercado corrente.\n",
    "\n",
    "#### Arquivos:\n",
    "\n",
    "* dados/de_ipeadata_pib_estados_preco_de_mercado_corrente.csv\n",
    "\n",
    "#### Origem:\n",
    "\n",
    "http://www.ipeadata.gov.br/\n",
    "\n",
    "http://www.ipeadata.gov.br/SelNivelR.aspx?serid=1540855420&module=R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prática\n",
    "\n",
    "## Exercício 01\n",
    "\n",
    "Realize a leitura de ambos os datasets e aplique os princípios do Tidy Data para criar um único dataset que deverá conter as informações de PIB e Dívida dos estados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "pd.options.display.max_rows = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_divida = pd.read_csv(\n",
    "        'dados/de_relatorio_estoque_final.csv',\n",
    "        sep=';',\n",
    "        header=2,\n",
    "        encoding='ansi',\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DISCRIMINAÇÃO de credores', 'ANO', 'AC', 'AL', 'AM', 'AP', 'BA', 'CE',\n",
       "       'DF', 'ES', 'GO', 'MA', 'MG', 'MS', 'MT', 'PA', 'PB', 'PE', 'PI', 'PR',\n",
       "       'RJ', 'RN', 'RO', 'RR', 'RS', 'SC', 'SE', 'SP', 'TO', 'Unnamed: 29',\n",
       "       'Unnamed: 30', 'Unnamed: 31', 'Unnamed: 32', 'Unnamed: 33',\n",
       "       'Unnamed: 34', 'Unnamed: 35', 'Unnamed: 36', 'Unnamed: 37',\n",
       "       'Unnamed: 38', 'Unnamed: 39', 'Unnamed: 40', 'Unnamed: 41',\n",
       "       'Unnamed: 42'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divida.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ANO', 'AC', 'AL', 'AM', 'AP', 'BA', 'CE', 'DF', 'ES', 'GO', 'MA', 'MG',\n",
       "       'MS', 'MT', 'PA', 'PB', 'PE', 'PI', 'PR', 'RJ', 'RN', 'RO', 'RR', 'RS',\n",
       "       'SC', 'SE', 'SP', 'TO'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divida_01.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def converter_numero(v):\n",
    "    try:\n",
    "        if type(v) == float or type(v) == int:\n",
    "            return v\n",
    "        else:\n",
    "            return float(str(v).replace('.', '').replace(',', '.'))\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>AC</th>\n",
       "      <th>AL</th>\n",
       "      <th>AM</th>\n",
       "      <th>AP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CE</th>\n",
       "      <th>DF</th>\n",
       "      <th>ES</th>\n",
       "      <th>GO</th>\n",
       "      <th>...</th>\n",
       "      <th>PR</th>\n",
       "      <th>RJ</th>\n",
       "      <th>RN</th>\n",
       "      <th>RO</th>\n",
       "      <th>RR</th>\n",
       "      <th>RS</th>\n",
       "      <th>SC</th>\n",
       "      <th>SE</th>\n",
       "      <th>SP</th>\n",
       "      <th>TO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996</td>\n",
       "      <td>496.167.400,00</td>\n",
       "      <td>1.336.063.226,80</td>\n",
       "      <td>1.339.077.760,60</td>\n",
       "      <td>72.350.000,00</td>\n",
       "      <td>5.051.360.926,00</td>\n",
       "      <td>1.914.695.155,20</td>\n",
       "      <td>914.624.000,00</td>\n",
       "      <td>970.495.656,60</td>\n",
       "      <td>5.339.870.576,02</td>\n",
       "      <td>...</td>\n",
       "      <td>2.714.774.210,60</td>\n",
       "      <td>8.858.624.026,00</td>\n",
       "      <td>553.568.706,80</td>\n",
       "      <td>513.126.727,00</td>\n",
       "      <td>78.600.206,03</td>\n",
       "      <td>9.215.137.103,10</td>\n",
       "      <td>3.746.107.567,80</td>\n",
       "      <td>975.204.018,20</td>\n",
       "      <td>50.922.619.418,00</td>\n",
       "      <td>203.010.099,40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997</td>\n",
       "      <td>518.193.236,05</td>\n",
       "      <td>1.915.425.521,96</td>\n",
       "      <td>1.365.492.378,88</td>\n",
       "      <td>75.450.000,00</td>\n",
       "      <td>5.185.699.897,24</td>\n",
       "      <td>1.756.071.263,21</td>\n",
       "      <td>826.197.000,00</td>\n",
       "      <td>1.217.145.609,78</td>\n",
       "      <td>6.175.958.952,42</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625.403.635,71</td>\n",
       "      <td>13.910.376.235,99</td>\n",
       "      <td>641.498.749,51</td>\n",
       "      <td>277.554.042,59</td>\n",
       "      <td>76.633.323,70</td>\n",
       "      <td>10.780.340.119,46</td>\n",
       "      <td>4.070.235.316,62</td>\n",
       "      <td>783.147.525,32</td>\n",
       "      <td>52.285.477.029,44</td>\n",
       "      <td>307.877.996,53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>545.164.412,53</td>\n",
       "      <td>2.503.891.740,97</td>\n",
       "      <td>1.616.159.702,15</td>\n",
       "      <td>84.395.000,00</td>\n",
       "      <td>7.023.265.633,39</td>\n",
       "      <td>2.219.451.876,51</td>\n",
       "      <td>1.244.621.000,00</td>\n",
       "      <td>2.067.171.833,68</td>\n",
       "      <td>6.889.195.785,08</td>\n",
       "      <td>...</td>\n",
       "      <td>2.411.360.364,66</td>\n",
       "      <td>21.576.954.717,14</td>\n",
       "      <td>650.025.215,91</td>\n",
       "      <td>1.851.440.159,15</td>\n",
       "      <td>88.169.352,47</td>\n",
       "      <td>13.171.120.879,55</td>\n",
       "      <td>4.326.034.350,44</td>\n",
       "      <td>765.651.182,23</td>\n",
       "      <td>54.703.524.607,59</td>\n",
       "      <td>233.243.000,00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>791.895.413,30</td>\n",
       "      <td>3.151.235.387,49</td>\n",
       "      <td>2.087.434.948,96</td>\n",
       "      <td>101.031.000,00</td>\n",
       "      <td>8.483.442.271,78</td>\n",
       "      <td>3.325.264.649,38</td>\n",
       "      <td>1.340.334.920,00</td>\n",
       "      <td>2.520.877.423,89</td>\n",
       "      <td>7.567.903.214,40</td>\n",
       "      <td>...</td>\n",
       "      <td>9.316.582.805,75</td>\n",
       "      <td>23.385.175.202,19</td>\n",
       "      <td>897.169.830,00</td>\n",
       "      <td>1.974.119.859,76</td>\n",
       "      <td>161.056.000,00</td>\n",
       "      <td>15.984.646.267,96</td>\n",
       "      <td>5.817.251.599,32</td>\n",
       "      <td>983.207.013,23</td>\n",
       "      <td>62.402.882.751,56</td>\n",
       "      <td>480.541.000,00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>715.259.628,70</td>\n",
       "      <td>3.073.980.980,23</td>\n",
       "      <td>1.920.488.632,83</td>\n",
       "      <td>113.377.000,00</td>\n",
       "      <td>9.127.751.634,22</td>\n",
       "      <td>3.530.759.336,26</td>\n",
       "      <td>1.163.374.333,41</td>\n",
       "      <td>2.660.989.863,49</td>\n",
       "      <td>8.428.106.917,85</td>\n",
       "      <td>...</td>\n",
       "      <td>8.492.432.806,54</td>\n",
       "      <td>26.150.524.078,35</td>\n",
       "      <td>1.081.343.938,76</td>\n",
       "      <td>1.872.237.809,20</td>\n",
       "      <td>168.276.778,68</td>\n",
       "      <td>17.974.267.004,73</td>\n",
       "      <td>6.165.272.861,73</td>\n",
       "      <td>1.051.001.297,00</td>\n",
       "      <td>69.543.959.783,36</td>\n",
       "      <td>554.977.901,93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ANO              AC                AL                AM              AP  \\\n",
       "0  1996  496.167.400,00  1.336.063.226,80  1.339.077.760,60   72.350.000,00   \n",
       "1  1997  518.193.236,05  1.915.425.521,96  1.365.492.378,88   75.450.000,00   \n",
       "2  1998  545.164.412,53  2.503.891.740,97  1.616.159.702,15   84.395.000,00   \n",
       "3  1999  791.895.413,30  3.151.235.387,49  2.087.434.948,96  101.031.000,00   \n",
       "4  2000  715.259.628,70  3.073.980.980,23  1.920.488.632,83  113.377.000,00   \n",
       "\n",
       "                 BA                CE                DF                ES  \\\n",
       "0  5.051.360.926,00  1.914.695.155,20    914.624.000,00    970.495.656,60   \n",
       "1  5.185.699.897,24  1.756.071.263,21    826.197.000,00  1.217.145.609,78   \n",
       "2  7.023.265.633,39  2.219.451.876,51  1.244.621.000,00  2.067.171.833,68   \n",
       "3  8.483.442.271,78  3.325.264.649,38  1.340.334.920,00  2.520.877.423,89   \n",
       "4  9.127.751.634,22  3.530.759.336,26  1.163.374.333,41  2.660.989.863,49   \n",
       "\n",
       "                 GO       ...                      PR                 RJ  \\\n",
       "0  5.339.870.576,02       ...        2.714.774.210,60   8.858.624.026,00   \n",
       "1  6.175.958.952,42       ...        2.625.403.635,71  13.910.376.235,99   \n",
       "2  6.889.195.785,08       ...        2.411.360.364,66  21.576.954.717,14   \n",
       "3  7.567.903.214,40       ...        9.316.582.805,75  23.385.175.202,19   \n",
       "4  8.428.106.917,85       ...        8.492.432.806,54  26.150.524.078,35   \n",
       "\n",
       "                 RN                RO              RR                 RS  \\\n",
       "0    553.568.706,80    513.126.727,00   78.600.206,03   9.215.137.103,10   \n",
       "1    641.498.749,51    277.554.042,59   76.633.323,70  10.780.340.119,46   \n",
       "2    650.025.215,91  1.851.440.159,15   88.169.352,47  13.171.120.879,55   \n",
       "3    897.169.830,00  1.974.119.859,76  161.056.000,00  15.984.646.267,96   \n",
       "4  1.081.343.938,76  1.872.237.809,20  168.276.778,68  17.974.267.004,73   \n",
       "\n",
       "                 SC                SE                 SP              TO  \n",
       "0  3.746.107.567,80    975.204.018,20  50.922.619.418,00  203.010.099,40  \n",
       "1  4.070.235.316,62    783.147.525,32  52.285.477.029,44  307.877.996,53  \n",
       "2  4.326.034.350,44    765.651.182,23  54.703.524.607,59  233.243.000,00  \n",
       "3  5.817.251.599,32    983.207.013,23  62.402.882.751,56  480.541.000,00  \n",
       "4  6.165.272.861,73  1.051.001.297,00  69.543.959.783,36  554.977.901,93  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divida.head()\n",
    "df_divida_01 = df_divida.loc[\n",
    "    df_divida['DISCRIMINAÇÃO de credores'] == 'DÍVITA TOTAL (Adm. Direta e Indireta)', 'ANO':'TO'\n",
    "].copy()\n",
    "df_divida_01 = df_divida_01.reset_index(drop=True)\n",
    "df_divida_01.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>UF</th>\n",
       "      <th>Dívida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996</td>\n",
       "      <td>AC</td>\n",
       "      <td>496.167.400,00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997</td>\n",
       "      <td>AC</td>\n",
       "      <td>518.193.236,05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>AC</td>\n",
       "      <td>545.164.412,53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>AC</td>\n",
       "      <td>791.895.413,30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>AC</td>\n",
       "      <td>715.259.628,70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ANO  UF          Dívida\n",
       "0  1996  AC  496.167.400,00\n",
       "1  1997  AC  518.193.236,05\n",
       "2  1998  AC  545.164.412,53\n",
       "3  1999  AC  791.895.413,30\n",
       "4  2000  AC  715.259.628,70"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divida_02 = df_divida_01.copy()\n",
    "df_divida_02 = pd.melt(\n",
    "    df_divida_01, \n",
    "    id_vars='ANO', \n",
    "    var_name='UF', \n",
    "    value_name='Dívida')\n",
    "df_divida_02.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 567 entries, 0 to 566\n",
      "Data columns (total 3 columns):\n",
      "ANO       567 non-null int64\n",
      "UF        567 non-null object\n",
      "Dívida    567 non-null float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 13.4+ KB\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>UF</th>\n",
       "      <th>Dívida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997</td>\n",
       "      <td>AC</td>\n",
       "      <td>5.181932e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>AC</td>\n",
       "      <td>5.451644e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.918954e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.152596e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ANO  UF        Dívida\n",
       "0  1996  AC  4.961674e+08\n",
       "1  1997  AC  5.181932e+08\n",
       "2  1998  AC  5.451644e+08\n",
       "3  1999  AC  7.918954e+08\n",
       "4  2000  AC  7.152596e+08"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divida_02['ANO'] = df_divida_02['ANO'].apply(lambda x: int(x))\n",
    "df_divida_02['Dívida'] = df_divida_02['Dívida'].apply(converter_numero)\n",
    "df_divida_02.info()\n",
    "df_divida_02.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sigla</th>\n",
       "      <th>Código</th>\n",
       "      <th>Estado</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>Unnamed: 26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AC</td>\n",
       "      <td>12</td>\n",
       "      <td>Acre</td>\n",
       "      <td>15,99639309</td>\n",
       "      <td>80,80820123</td>\n",
       "      <td>928,0680459</td>\n",
       "      <td>20105,63289</td>\n",
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       "      <td>1237096,725</td>\n",
       "      <td>1459645,547</td>\n",
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       "      <td>4482918,968</td>\n",
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       "      <td>5760499,894</td>\n",
       "      <td>6730107,014</td>\n",
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       "      <td>8476514,711</td>\n",
       "      <td>8794361,637</td>\n",
       "      <td>9629238,676</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>27</td>\n",
       "      <td>Alagoas</td>\n",
       "      <td>81,60226627</td>\n",
       "      <td>428,9000432</td>\n",
       "      <td>4630,854661</td>\n",
       "      <td>93464,35972</td>\n",
       "      <td>2386299,951</td>\n",
       "      <td>4656515,994</td>\n",
       "      <td>5369180,034</td>\n",
       "      <td>...</td>\n",
       "      <td>12890508,99</td>\n",
       "      <td>14139343,77</td>\n",
       "      <td>15748040,09</td>\n",
       "      <td>17793223,18</td>\n",
       "      <td>19476857,52</td>\n",
       "      <td>21234950,63</td>\n",
       "      <td>24574808,01</td>\n",
       "      <td>28540303,89</td>\n",
       "      <td>29544708,36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AM</td>\n",
       "      <td>13</td>\n",
       "      <td>Amazonas</td>\n",
       "      <td>210,0455169</td>\n",
       "      <td>1015,38191</td>\n",
       "      <td>10138,26866</td>\n",
       "      <td>266842,2454</td>\n",
       "      <td>5878452,277</td>\n",
       "      <td>10069421,18</td>\n",
       "      <td>12495041,42</td>\n",
       "      <td>...</td>\n",
       "      <td>30313729,31</td>\n",
       "      <td>33352132,25</td>\n",
       "      <td>39156909,68</td>\n",
       "      <td>42023210,12</td>\n",
       "      <td>46822561,04</td>\n",
       "      <td>49614250,55</td>\n",
       "      <td>59779291,83</td>\n",
       "      <td>64555403,73</td>\n",
       "      <td>64119835,58</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AP</td>\n",
       "      <td>16</td>\n",
       "      <td>Amapá</td>\n",
       "      <td>18,59140125</td>\n",
       "      <td>96,91090206</td>\n",
       "      <td>1125,864717</td>\n",
       "      <td>23048,07566</td>\n",
       "      <td>624582,2361</td>\n",
       "      <td>1506572,834</td>\n",
       "      <td>1819439,914</td>\n",
       "      <td>...</td>\n",
       "      <td>3846125,342</td>\n",
       "      <td>4361254,714</td>\n",
       "      <td>5260017,965</td>\n",
       "      <td>6022130,33</td>\n",
       "      <td>6764832,739</td>\n",
       "      <td>7404389,145</td>\n",
       "      <td>8265964,663</td>\n",
       "      <td>8968031,818</td>\n",
       "      <td>10419539,4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA</td>\n",
       "      <td>29</td>\n",
       "      <td>Bahia</td>\n",
       "      <td>518,9828251</td>\n",
       "      <td>2652,432271</td>\n",
       "      <td>28114,11123</td>\n",
       "      <td>611119,1179</td>\n",
       "      <td>14972456,45</td>\n",
       "      <td>26427274,23</td>\n",
       "      <td>32430856,07</td>\n",
       "      <td>...</td>\n",
       "      <td>79083213,15</td>\n",
       "      <td>90919322,15</td>\n",
       "      <td>96520719,24</td>\n",
       "      <td>109651822,4</td>\n",
       "      <td>121507036,5</td>\n",
       "      <td>137074670,7</td>\n",
       "      <td>154340457,6</td>\n",
       "      <td>159868615,1</td>\n",
       "      <td>167727375,1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sigla  Código    Estado         1990         1991         1992         1993  \\\n",
       "0    AC      12      Acre  15,99639309  80,80820123  928,0680459  20105,63289   \n",
       "1    AL      27   Alagoas  81,60226627  428,9000432  4630,854661  93464,35972   \n",
       "2    AM      13  Amazonas  210,0455169   1015,38191  10138,26866  266842,2454   \n",
       "3    AP      16     Amapá  18,59140125  96,91090206  1125,864717  23048,07566   \n",
       "4    BA      29     Bahia  518,9828251  2652,432271  28114,11123  611119,1179   \n",
       "\n",
       "          1994         1995         1996     ...             2004  \\\n",
       "0  548356,3313  1237096,725  1459645,547     ...      3940314,681   \n",
       "1  2386299,951  4656515,994  5369180,034     ...      12890508,99   \n",
       "2  5878452,277  10069421,18  12495041,42     ...      30313729,31   \n",
       "3  624582,2361  1506572,834  1819439,914     ...      3846125,342   \n",
       "4  14972456,45  26427274,23  32430856,07     ...      79083213,15   \n",
       "\n",
       "          2005         2006         2007         2008         2009  \\\n",
       "0  4482918,968  4834621,202  5760499,894  6730107,014  7386436,282   \n",
       "1  14139343,77  15748040,09  17793223,18  19476857,52  21234950,63   \n",
       "2  33352132,25  39156909,68  42023210,12  46822561,04  49614250,55   \n",
       "3  4361254,714  5260017,965   6022130,33  6764832,739  7404389,145   \n",
       "4  90919322,15  96520719,24  109651822,4  121507036,5  137074670,7   \n",
       "\n",
       "          2010         2011         2012 Unnamed: 26  \n",
       "0  8476514,711  8794361,637  9629238,676         NaN  \n",
       "1  24574808,01  28540303,89  29544708,36         NaN  \n",
       "2  59779291,83  64555403,73  64119835,58         NaN  \n",
       "3  8265964,663  8968031,818   10419539,4         NaN  \n",
       "4  154340457,6  159868615,1  167727375,1         NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pib = pd.read_csv(\n",
    "    'dados/de_ipeadata_pib_estados_preco_de_mercado_corrente.csv',\n",
    "    sep=';',\n",
    "    header=1,\n",
    ")\n",
    "df_pib.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sigla</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>...</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
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       "      <th>2012</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AC</td>\n",
       "      <td>15.996393</td>\n",
       "      <td>80.808201</td>\n",
       "      <td>928.068046</td>\n",
       "      <td>20105.63289</td>\n",
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       "      <td>8.794362e+06</td>\n",
       "      <td>9.629239e+06</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>81.602266</td>\n",
       "      <td>428.900043</td>\n",
       "      <td>4630.854661</td>\n",
       "      <td>93464.35972</td>\n",
       "      <td>2.386300e+06</td>\n",
       "      <td>4.656516e+06</td>\n",
       "      <td>5.369180e+06</td>\n",
       "      <td>6.214731e+06</td>\n",
       "      <td>6.676333e+06</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.413934e+07</td>\n",
       "      <td>1.574804e+07</td>\n",
       "      <td>1.779322e+07</td>\n",
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       "      <td>2.123495e+07</td>\n",
       "      <td>2.457481e+07</td>\n",
       "      <td>2.854030e+07</td>\n",
       "      <td>2.954471e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AM</td>\n",
       "      <td>210.045517</td>\n",
       "      <td>1015.381910</td>\n",
       "      <td>10138.268660</td>\n",
       "      <td>266842.24540</td>\n",
       "      <td>5.878452e+06</td>\n",
       "      <td>1.006942e+07</td>\n",
       "      <td>1.249504e+07</td>\n",
       "      <td>1.257225e+07</td>\n",
       "      <td>1.319851e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>2.497717e+07</td>\n",
       "      <td>3.031373e+07</td>\n",
       "      <td>3.335213e+07</td>\n",
       "      <td>3.915691e+07</td>\n",
       "      <td>4.202321e+07</td>\n",
       "      <td>4.682256e+07</td>\n",
       "      <td>4.961425e+07</td>\n",
       "      <td>5.977929e+07</td>\n",
       "      <td>6.455540e+07</td>\n",
       "      <td>6.411984e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AP</td>\n",
       "      <td>18.591401</td>\n",
       "      <td>96.910902</td>\n",
       "      <td>1125.864717</td>\n",
       "      <td>23048.07566</td>\n",
       "      <td>6.245822e+05</td>\n",
       "      <td>1.506573e+06</td>\n",
       "      <td>1.819440e+06</td>\n",
       "      <td>2.030050e+06</td>\n",
       "      <td>2.038323e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>3.434107e+06</td>\n",
       "      <td>3.846125e+06</td>\n",
       "      <td>4.361255e+06</td>\n",
       "      <td>5.260018e+06</td>\n",
       "      <td>6.022130e+06</td>\n",
       "      <td>6.764833e+06</td>\n",
       "      <td>7.404389e+06</td>\n",
       "      <td>8.265965e+06</td>\n",
       "      <td>8.968032e+06</td>\n",
       "      <td>1.041954e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA</td>\n",
       "      <td>518.982825</td>\n",
       "      <td>2652.432271</td>\n",
       "      <td>28114.111230</td>\n",
       "      <td>611119.11790</td>\n",
       "      <td>1.497246e+07</td>\n",
       "      <td>2.642727e+07</td>\n",
       "      <td>3.243086e+07</td>\n",
       "      <td>3.652810e+07</td>\n",
       "      <td>3.783813e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>6.814694e+07</td>\n",
       "      <td>7.908321e+07</td>\n",
       "      <td>9.091932e+07</td>\n",
       "      <td>9.652072e+07</td>\n",
       "      <td>1.096518e+08</td>\n",
       "      <td>1.215070e+08</td>\n",
       "      <td>1.370747e+08</td>\n",
       "      <td>1.543405e+08</td>\n",
       "      <td>1.598686e+08</td>\n",
       "      <td>1.677274e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sigla        1990         1991          1992          1993          1994  \\\n",
       "0    AC   15.996393    80.808201    928.068046   20105.63289  5.483563e+05   \n",
       "1    AL   81.602266   428.900043   4630.854661   93464.35972  2.386300e+06   \n",
       "2    AM  210.045517  1015.381910  10138.268660  266842.24540  5.878452e+06   \n",
       "3    AP   18.591401    96.910902   1125.864717   23048.07566  6.245822e+05   \n",
       "4    BA  518.982825  2652.432271  28114.111230  611119.11790  1.497246e+07   \n",
       "\n",
       "           1995          1996          1997          1998      ...       \\\n",
       "0  1.237097e+06  1.459646e+06  1.555307e+06  1.694329e+06      ...        \n",
       "1  4.656516e+06  5.369180e+06  6.214731e+06  6.676333e+06      ...        \n",
       "2  1.006942e+07  1.249504e+07  1.257225e+07  1.319851e+07      ...        \n",
       "3  1.506573e+06  1.819440e+06  2.030050e+06  2.038323e+06      ...        \n",
       "4  2.642727e+07  3.243086e+07  3.652810e+07  3.783813e+07      ...        \n",
       "\n",
       "           2003          2004          2005          2006          2007  \\\n",
       "0  3.304771e+06  3.940315e+06  4.482919e+06  4.834621e+06  5.760500e+06   \n",
       "1  1.120951e+07  1.289051e+07  1.413934e+07  1.574804e+07  1.779322e+07   \n",
       "2  2.497717e+07  3.031373e+07  3.335213e+07  3.915691e+07  4.202321e+07   \n",
       "3  3.434107e+06  3.846125e+06  4.361255e+06  5.260018e+06  6.022130e+06   \n",
       "4  6.814694e+07  7.908321e+07  9.091932e+07  9.652072e+07  1.096518e+08   \n",
       "\n",
       "           2008          2009          2010          2011          2012  \n",
       "0  6.730107e+06  7.386436e+06  8.476515e+06  8.794362e+06  9.629239e+06  \n",
       "1  1.947686e+07  2.123495e+07  2.457481e+07  2.854030e+07  2.954471e+07  \n",
       "2  4.682256e+07  4.961425e+07  5.977929e+07  6.455540e+07  6.411984e+07  \n",
       "3  6.764833e+06  7.404389e+06  8.265965e+06  8.968032e+06  1.041954e+07  \n",
       "4  1.215070e+08  1.370747e+08  1.543405e+08  1.598686e+08  1.677274e+08  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pib_01 = df_pib.drop(columns=['Unnamed: 26', 'Código','Estado'])\n",
    "for c in df_pib_01.loc[:, '1990':].columns:\n",
    "    df_pib_01[c] = df_pib_01[c].apply(converter_numero)\n",
    "df_pib_01.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>UF</th>\n",
       "      <th>PIB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>AC</td>\n",
       "      <td>15.996393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990</td>\n",
       "      <td>AL</td>\n",
       "      <td>81.602266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990</td>\n",
       "      <td>AM</td>\n",
       "      <td>210.045517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>AP</td>\n",
       "      <td>18.591401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990</td>\n",
       "      <td>BA</td>\n",
       "      <td>518.982825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ANO  UF         PIB\n",
       "0  1990  AC   15.996393\n",
       "1  1990  AL   81.602266\n",
       "2  1990  AM  210.045517\n",
       "3  1990  AP   18.591401\n",
       "4  1990  BA  518.982825"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pib_02 = pd.melt(df_pib_01, id_vars='Sigla', var_name='ANO', value_name='PIB')\n",
    "df_pib_02['ANO'] = df_pib_02['ANO'].astype(int)\n",
    "df_pib_02 = df_pib_02.rename(columns={'Sigla': 'UF'})[['ANO', 'UF', 'PIB']]\n",
    "df_pib_02.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>UF</th>\n",
       "      <th>Dívida</th>\n",
       "      <th>PIB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "      <td>1459645.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997</td>\n",
       "      <td>AC</td>\n",
       "      <td>5.181932e+08</td>\n",
       "      <td>1555306.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>AC</td>\n",
       "      <td>5.451644e+08</td>\n",
       "      <td>1694328.657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.918954e+08</td>\n",
       "      <td>1866542.316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.152596e+08</td>\n",
       "      <td>2154284.485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ANO  UF        Dívida          PIB\n",
       "0  1996  AC  4.961674e+08  1459645.547\n",
       "1  1997  AC  5.181932e+08  1555306.521\n",
       "2  1998  AC  5.451644e+08  1694328.657\n",
       "3  1999  AC  7.918954e+08  1866542.316\n",
       "4  2000  AC  7.152596e+08  2154284.485"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divida_pib = pd.merge(df_divida_02, df_pib_02, how='outer', on=['ANO', 'UF'])\n",
    "df_divida_pib.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 02\n",
    "\n",
    "Utilize interpolação para completar os valores de PIB faltantes no Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>UF</th>\n",
       "      <th>Dívida</th>\n",
       "      <th>PIB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>2016</td>\n",
       "      <td>TO</td>\n",
       "      <td>3.076927e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>2016</td>\n",
       "      <td>SP</td>\n",
       "      <td>2.573549e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>2016</td>\n",
       "      <td>SE</td>\n",
       "      <td>2.905640e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2016</td>\n",
       "      <td>SC</td>\n",
       "      <td>1.971713e+10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>2016</td>\n",
       "      <td>RS</td>\n",
       "      <td>6.612660e+10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ANO  UF        Dívida  PIB\n",
       "566  2016  TO  3.076927e+09  NaN\n",
       "545  2016  SP  2.573549e+11  NaN\n",
       "524  2016  SE  2.905640e+09  NaN\n",
       "503  2016  SC  1.971713e+10  NaN\n",
       "482  2016  RS  6.612660e+10  NaN"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_divida_pib_02 = df_divida_pib.sort_values(['ANO', 'UF'], ascending=False)\n",
    "df_divida_pib_02['Dívida'] = df_divida_pib_02['Dívida'].interpolate()\n",
    "df_divida_pib_02.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANO</th>\n",
       "      <th>UF</th>\n",
       "      <th>Dívida</th>\n",
       "      <th>PIB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "      <td>1.599639e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "      <td>8.080820e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "      <td>9.280680e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "      <td>2.010563e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "      <td>5.483563e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "      <td>1.237097e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1996</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.961674e+08</td>\n",
       "      <td>1.459646e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1997</td>\n",
       "      <td>AC</td>\n",
       "      <td>5.181932e+08</td>\n",
       "      <td>1.555307e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1998</td>\n",
       "      <td>AC</td>\n",
       "      <td>5.451644e+08</td>\n",
       "      <td>1.694329e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1999</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.918954e+08</td>\n",
       "      <td>1.866542e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.152596e+08</td>\n",
       "      <td>2.154284e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2001</td>\n",
       "      <td>AC</td>\n",
       "      <td>6.909050e+08</td>\n",
       "      <td>2.475169e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2002</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.446959e+08</td>\n",
       "      <td>2.868452e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2003</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.384811e+08</td>\n",
       "      <td>3.304771e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2004</td>\n",
       "      <td>AC</td>\n",
       "      <td>7.899110e+08</td>\n",
       "      <td>3.940315e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2005</td>\n",
       "      <td>AC</td>\n",
       "      <td>8.864648e+08</td>\n",
       "      <td>4.482919e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006</td>\n",
       "      <td>AC</td>\n",
       "      <td>9.660532e+08</td>\n",
       "      <td>4.834621e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2007</td>\n",
       "      <td>AC</td>\n",
       "      <td>9.448445e+08</td>\n",
       "      <td>5.760500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2008</td>\n",
       "      <td>AC</td>\n",
       "      <td>9.933599e+08</td>\n",
       "      <td>6.730107e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2009</td>\n",
       "      <td>AC</td>\n",
       "      <td>1.333884e+09</td>\n",
       "      <td>7.386436e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010</td>\n",
       "      <td>AC</td>\n",
       "      <td>1.722086e+09</td>\n",
       "      <td>8.476515e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2011</td>\n",
       "      <td>AC</td>\n",
       "      <td>1.858785e+09</td>\n",
       "      <td>8.794362e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>AC</td>\n",
       "      <td>2.636362e+09</td>\n",
       "      <td>9.629239e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>AC</td>\n",
       "      <td>3.019823e+09</td>\n",
       "      <td>9.629239e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014</td>\n",
       "      <td>AC</td>\n",
       "      <td>3.531637e+09</td>\n",
       "      <td>9.629239e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015</td>\n",
       "      <td>AC</td>\n",
       "      <td>4.035152e+09</td>\n",
       "      <td>9.629239e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016</td>\n",
       "      <td>AC</td>\n",
       "      <td>3.658661e+09</td>\n",
       "      <td>9.629239e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1990</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.336063e+09</td>\n",
       "      <td>8.160227e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1991</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.336063e+09</td>\n",
       "      <td>4.289000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1992</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.336063e+09</td>\n",
       "      <td>4.630855e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1993</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.336063e+09</td>\n",
       "      <td>9.346436e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1994</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.336063e+09</td>\n",
       "      <td>2.386300e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1995</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.336063e+09</td>\n",
       "      <td>4.656516e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1996</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.336063e+09</td>\n",
       "      <td>5.369180e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1997</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.915426e+09</td>\n",
       "      <td>6.214731e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1998</td>\n",
       "      <td>AL</td>\n",
       "      <td>2.503892e+09</td>\n",
       "      <td>6.676333e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1999</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.151235e+09</td>\n",
       "      <td>6.935080e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2000</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.073981e+09</td>\n",
       "      <td>7.768754e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2001</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.363686e+09</td>\n",
       "      <td>8.488146e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2002</td>\n",
       "      <td>AL</td>\n",
       "      <td>4.449588e+09</td>\n",
       "      <td>9.812402e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2003</td>\n",
       "      <td>AL</td>\n",
       "      <td>4.882397e+09</td>\n",
       "      <td>1.120951e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2004</td>\n",
       "      <td>AL</td>\n",
       "      <td>5.362488e+09</td>\n",
       "      <td>1.289051e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2005</td>\n",
       "      <td>AL</td>\n",
       "      <td>5.645043e+09</td>\n",
       "      <td>1.413934e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2006</td>\n",
       "      <td>AL</td>\n",
       "      <td>6.046136e+09</td>\n",
       "      <td>1.574804e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2007</td>\n",
       "      <td>AL</td>\n",
       "      <td>6.206366e+09</td>\n",
       "      <td>1.779322e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2008</td>\n",
       "      <td>AL</td>\n",
       "      <td>6.943805e+09</td>\n",
       "      <td>1.947686e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2009</td>\n",
       "      <td>AL</td>\n",
       "      <td>6.834761e+09</td>\n",
       "      <td>2.123495e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2010</td>\n",
       "      <td>AL</td>\n",
       "      <td>7.525269e+09</td>\n",
       "      <td>2.457481e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2011</td>\n",
       "      <td>AL</td>\n",
       "      <td>7.830490e+09</td>\n",
       "      <td>2.854030e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2012</td>\n",
       "      <td>AL</td>\n",
       "      <td>8.178423e+09</td>\n",
       "      <td>2.954471e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2013</td>\n",
       "      <td>AL</td>\n",
       "      <td>9.035402e+09</td>\n",
       "      <td>2.954471e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2014</td>\n",
       "      <td>AL</td>\n",
       "      <td>9.703065e+09</td>\n",
       "      <td>2.954471e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2015</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.078967e+10</td>\n",
       "      <td>2.954471e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>8.945553e+09</td>\n",
       "      <td>2.954471e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1990</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.339078e+09</td>\n",
       "      <td>2.100455e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1991</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.339078e+09</td>\n",
       "      <td>1.015382e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1992</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.339078e+09</td>\n",
       "      <td>1.013827e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1993</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.339078e+09</td>\n",
       "      <td>2.668422e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1994</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.339078e+09</td>\n",
       "      <td>5.878452e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1995</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.339078e+09</td>\n",
       "      <td>1.006942e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1996</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.339078e+09</td>\n",
       "      <td>1.249504e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1997</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.365492e+09</td>\n",
       "      <td>1.257225e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1998</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.616160e+09</td>\n",
       "      <td>1.319851e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1999</td>\n",
       "      <td>AM</td>\n",
       "      <td>2.087435e+09</td>\n",
       "      <td>1.393392e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2000</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.920489e+09</td>\n",
       "      <td>1.674976e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2001</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.987282e+09</td>\n",
       "      <td>1.805038e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2002</td>\n",
       "      <td>AM</td>\n",
       "      <td>2.286497e+09</td>\n",
       "      <td>2.179117e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2003</td>\n",
       "      <td>AM</td>\n",
       "      <td>2.185980e+09</td>\n",
       "      <td>2.497717e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2004</td>\n",
       "      <td>AM</td>\n",
       "      <td>2.102247e+09</td>\n",
       "      <td>3.031373e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2005</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.981720e+09</td>\n",
       "      <td>3.335213e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2006</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.955251e+09</td>\n",
       "      <td>3.915691e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2007</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.956955e+09</td>\n",
       "      <td>4.202321e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2008</td>\n",
       "      <td>AM</td>\n",
       "      <td>2.225403e+09</td>\n",
       "      <td>4.682256e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2009</td>\n",
       "      <td>AM</td>\n",
       "      <td>2.778229e+09</td>\n",
       "      <td>4.961425e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2010</td>\n",
       "      <td>AM</td>\n",
       "      <td>3.189417e+09</td>\n",
       "      <td>5.977929e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2011</td>\n",
       "      <td>AM</td>\n",
       "      <td>3.302190e+09</td>\n",
       "      <td>6.455540e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2012</td>\n",
       "      <td>AM</td>\n",
       "      <td>3.877973e+09</td>\n",
       "      <td>6.411984e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2013</td>\n",
       "      <td>AM</td>\n",
       "      <td>4.667463e+09</td>\n",
       "      <td>6.411984e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2014</td>\n",
       "      <td>AM</td>\n",
       "      <td>5.779712e+09</td>\n",
       "      <td>6.411984e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2015</td>\n",
       "      <td>AM</td>\n",
       "      <td>6.906914e+09</td>\n",
       "      <td>6.411984e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2016</td>\n",
       "      <td>AM</td>\n",
       "      <td>6.286692e+09</td>\n",
       "      <td>6.411984e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1990</td>\n",
       "      <td>AP</td>\n",
       "      <td>7.235000e+07</td>\n",
       "      <td>1.859140e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1991</td>\n",
       "      <td>AP</td>\n",
       "      <td>7.235000e+07</td>\n",
       "      <td>9.691090e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1992</td>\n",
       "      <td>AP</td>\n",
       "      <td>7.235000e+07</td>\n",
       "      <td>1.125865e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1993</td>\n",
       "      <td>AP</td>\n",
       "      <td>7.235000e+07</td>\n",
       "      <td>2.304808e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1994</td>\n",
       "      <td>AP</td>\n",
       "      <td>7.235000e+07</td>\n",
       "      <td>6.245822e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1995</td>\n",
       "      <td>AP</td>\n",
       "      <td>7.235000e+07</td>\n",
       "      <td>1.506573e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1996</td>\n",
       "      <td>AP</td>\n",
       "      <td>7.235000e+07</td>\n",
       "      <td>1.819440e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1997</td>\n",
       "      <td>AP</td>\n",
       "      <td>7.545000e+07</td>\n",
       "      <td>2.030050e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1998</td>\n",
       "      <td>AP</td>\n",
       "      <td>8.439500e+07</td>\n",
       "      <td>2.038323e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1999</td>\n",
       "      <td>AP</td>\n",
       "      <td>1.010310e+08</td>\n",
       "      <td>2.128307e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2000</td>\n",
       "      <td>AP</td>\n",
       "      <td>1.133770e+08</td>\n",
       "      <td>2.357036e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2001</td>\n",
       "      <td>AP</td>\n",
       "      <td>1.027055e+08</td>\n",
       "      <td>2.801925e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2002</td>\n",
       "      <td>AP</td>\n",
       "      <td>2.297858e+08</td>\n",
       "      <td>3.291535e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2003</td>\n",
       "      <td>AP</td>\n",
       "      <td>2.604588e+08</td>\n",
       "      <td>3.434107e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2004</td>\n",
       "      <td>AP</td>\n",
       "      <td>2.738821e+08</td>\n",
       "      <td>3.846125e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2005</td>\n",
       "      <td>AP</td>\n",
       "      <td>2.659483e+08</td>\n",
       "      <td>4.361255e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2006</td>\n",
       "      <td>AP</td>\n",
       "      <td>2.550358e+08</td>\n",
       "      <td>5.260018e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2007</td>\n",
       "      <td>AP</td>\n",
       "      <td>2.593615e+08</td>\n",
       "      <td>6.022130e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2008</td>\n",
       "      <td>AP</td>\n",
       "      <td>2.373695e+08</td>\n",
       "      <td>6.764833e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ANO  UF        Dívida           PIB\n",
       "0   1990  AC  4.961674e+08  1.599639e+01\n",
       "1   1991  AC  4.961674e+08  8.080820e+01\n",
       "2   1992  AC  4.961674e+08  9.280680e+02\n",
       "3   1993  AC  4.961674e+08  2.010563e+04\n",
       "4   1994  AC  4.961674e+08  5.483563e+05\n",
       "5   1995  AC  4.961674e+08  1.237097e+06\n",
       "6   1996  AC  4.961674e+08  1.459646e+06\n",
       "7   1997  AC  5.181932e+08  1.555307e+06\n",
       "8   1998  AC  5.451644e+08  1.694329e+06\n",
       "9   1999  AC  7.918954e+08  1.866542e+06\n",
       "10  2000  AC  7.152596e+08  2.154284e+06\n",
       "11  2001  AC  6.909050e+08  2.475169e+06\n",
       "12  2002  AC  7.446959e+08  2.868452e+06\n",
       "13  2003  AC  7.384811e+08  3.304771e+06\n",
       "14  2004  AC  7.899110e+08  3.940315e+06\n",
       "15  2005  AC  8.864648e+08  4.482919e+06\n",
       "16  2006  AC  9.660532e+08  4.834621e+06\n",
       "17  2007  AC  9.448445e+08  5.760500e+06\n",
       "18  2008  AC  9.933599e+08  6.730107e+06\n",
       "19  2009  AC  1.333884e+09  7.386436e+06\n",
       "20  2010  AC  1.722086e+09  8.476515e+06\n",
       "21  2011  AC  1.858785e+09  8.794362e+06\n",
       "22  2012  AC  2.636362e+09  9.629239e+06\n",
       "23  2013  AC  3.019823e+09  9.629239e+06\n",
       "24  2014  AC  3.531637e+09  9.629239e+06\n",
       "25  2015  AC  4.035152e+09  9.629239e+06\n",
       "26  2016  AC  3.658661e+09  9.629239e+06\n",
       "27  1990  AL  1.336063e+09  8.160227e+01\n",
       "28  1991  AL  1.336063e+09  4.289000e+02\n",
       "29  1992  AL  1.336063e+09  4.630855e+03\n",
       "30  1993  AL  1.336063e+09  9.346436e+04\n",
       "31  1994  AL  1.336063e+09  2.386300e+06\n",
       "32  1995  AL  1.336063e+09  4.656516e+06\n",
       "33  1996  AL  1.336063e+09  5.369180e+06\n",
       "34  1997  AL  1.915426e+09  6.214731e+06\n",
       "35  1998  AL  2.503892e+09  6.676333e+06\n",
       "36  1999  AL  3.151235e+09  6.935080e+06\n",
       "37  2000  AL  3.073981e+09  7.768754e+06\n",
       "38  2001  AL  3.363686e+09  8.488146e+06\n",
       "39  2002  AL  4.449588e+09  9.812402e+06\n",
       "40  2003  AL  4.882397e+09  1.120951e+07\n",
       "41  2004  AL  5.362488e+09  1.289051e+07\n",
       "42  2005  AL  5.645043e+09  1.413934e+07\n",
       "43  2006  AL  6.046136e+09  1.574804e+07\n",
       "44  2007  AL  6.206366e+09  1.779322e+07\n",
       "45  2008  AL  6.943805e+09  1.947686e+07\n",
       "46  2009  AL  6.834761e+09  2.123495e+07\n",
       "47  2010  AL  7.525269e+09  2.457481e+07\n",
       "48  2011  AL  7.830490e+09  2.854030e+07\n",
       "49  2012  AL  8.178423e+09  2.954471e+07\n",
       "50  2013  AL  9.035402e+09  2.954471e+07\n",
       "51  2014  AL  9.703065e+09  2.954471e+07\n",
       "52  2015  AL  1.078967e+10  2.954471e+07\n",
       "53  2016  AL  8.945553e+09  2.954471e+07\n",
       "54  1990  AM  1.339078e+09  2.100455e+02\n",
       "55  1991  AM  1.339078e+09  1.015382e+03\n",
       "56  1992  AM  1.339078e+09  1.013827e+04\n",
       "57  1993  AM  1.339078e+09  2.668422e+05\n",
       "58  1994  AM  1.339078e+09  5.878452e+06\n",
       "59  1995  AM  1.339078e+09  1.006942e+07\n",
       "60  1996  AM  1.339078e+09  1.249504e+07\n",
       "61  1997  AM  1.365492e+09  1.257225e+07\n",
       "62  1998  AM  1.616160e+09  1.319851e+07\n",
       "63  1999  AM  2.087435e+09  1.393392e+07\n",
       "64  2000  AM  1.920489e+09  1.674976e+07\n",
       "65  2001  AM  1.987282e+09  1.805038e+07\n",
       "66  2002  AM  2.286497e+09  2.179117e+07\n",
       "67  2003  AM  2.185980e+09  2.497717e+07\n",
       "68  2004  AM  2.102247e+09  3.031373e+07\n",
       "69  2005  AM  1.981720e+09  3.335213e+07\n",
       "70  2006  AM  1.955251e+09  3.915691e+07\n",
       "71  2007  AM  1.956955e+09  4.202321e+07\n",
       "72  2008  AM  2.225403e+09  4.682256e+07\n",
       "73  2009  AM  2.778229e+09  4.961425e+07\n",
       "74  2010  AM  3.189417e+09  5.977929e+07\n",
       "75  2011  AM  3.302190e+09  6.455540e+07\n",
       "76  2012  AM  3.877973e+09  6.411984e+07\n",
       "77  2013  AM  4.667463e+09  6.411984e+07\n",
       "78  2014  AM  5.779712e+09  6.411984e+07\n",
       "79  2015  AM  6.906914e+09  6.411984e+07\n",
       "80  2016  AM  6.286692e+09  6.411984e+07\n",
       "81  1990  AP  7.235000e+07  1.859140e+01\n",
       "82  1991  AP  7.235000e+07  9.691090e+01\n",
       "83  1992  AP  7.235000e+07  1.125865e+03\n",
       "84  1993  AP  7.235000e+07  2.304808e+04\n",
       "85  1994  AP  7.235000e+07  6.245822e+05\n",
       "86  1995  AP  7.235000e+07  1.506573e+06\n",
       "87  1996  AP  7.235000e+07  1.819440e+06\n",
       "88  1997  AP  7.545000e+07  2.030050e+06\n",
       "89  1998  AP  8.439500e+07  2.038323e+06\n",
       "90  1999  AP  1.010310e+08  2.128307e+06\n",
       "91  2000  AP  1.133770e+08  2.357036e+06\n",
       "92  2001  AP  1.027055e+08  2.801925e+06\n",
       "93  2002  AP  2.297858e+08  3.291535e+06\n",
       "94  2003  AP  2.604588e+08  3.434107e+06\n",
       "95  2004  AP  2.738821e+08  3.846125e+06\n",
       "96  2005  AP  2.659483e+08  4.361255e+06\n",
       "97  2006  AP  2.550358e+08  5.260018e+06\n",
       "98  2007  AP  2.593615e+08  6.022130e+06\n",
       "99  2008  AP  2.373695e+08  6.764833e+06"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista = []\n",
    "df_grupos = df_divida_pib.groupby('UF')\n",
    "for idx, df in df_grupos:\n",
    "    df = df.sort_values('ANO', ascending=False)\n",
    "    df['Dívida'] = df['Dívida'].interpolate()\n",
    "    \n",
    "    df = df.sort_values('ANO', ascending=True)\n",
    "    df['PIB'] = df['PIB'].interpolate()\n",
    "    lista.append(df)\n",
    "\n",
    "df_interpolado = pd.concat(lista, axis=0)\n",
    "df_interpolado = df_interpolado.sort_values(['UF', 'ANO'])\n",
    "df_interpolado = df_interpolado.reset_index(drop=True)\n",
    "df_interpolado.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 16, 10\n",
    "ax = None\n",
    "for uf in set(df_interpolado['UF']):\n",
    "    ax = df_interpolado[(df_interpolado['UF'] == uf) & (df_interpolado['ANO'] > 1995)].plot(x='ANO', y='PIB', ax=ax, label=uf)\n",
    "    \n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
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   "toc_position": {},
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  "varInspector": {
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
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     "delete_cmd_postfix": ") ",
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   "types_to_exclude": [
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