Agrupamentos_grafico_estatistica_descritiva.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relatório de Análise VII"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando Agrupamentos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "      <th>Valor m2</th>\n",
       "      <th>Tipo Agregado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>42.50</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Jardim Botânico</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>800.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.33</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Higienópolis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>800.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Cachambi</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Casa de Condomínio</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>750</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.33</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Casa de Condomínio</td>\n",
       "      <td>Ramos</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.38</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Grajaú</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>21.43</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Lins de Vasconcelos</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Tipo               Bairro  Quartos  Vagas  Suites  Area  \\\n",
       "0           Quitinete           Copacabana        1      0       0    40   \n",
       "1                Casa      Jardim Botânico        2      0       1   100   \n",
       "2         Apartamento               Centro        1      0       0    15   \n",
       "3         Apartamento         Higienópolis        1      0       0    48   \n",
       "4         Apartamento             Cachambi        2      0       0    50   \n",
       "5  Casa de Condomínio      Barra da Tijuca        5      4       5   750   \n",
       "6  Casa de Condomínio                Ramos        2      2       0    65   \n",
       "7         Apartamento               Grajaú        2      1       0    70   \n",
       "8         Apartamento  Lins de Vasconcelos        3      1       1    90   \n",
       "9         Apartamento           Copacabana        1      0       1    40   \n",
       "\n",
       "     Valor  Condominio  IPTU  Valor m2 Tipo Agregado  \n",
       "0   1700.0       500.0  60.0     42.50          Casa  \n",
       "1   7000.0         0.0   0.0     70.00          Casa  \n",
       "2    800.0       390.0  20.0     53.33   Apartamento  \n",
       "3    800.0       230.0   0.0     16.67   Apartamento  \n",
       "4   1300.0       301.0  17.0     26.00   Apartamento  \n",
       "5  22000.0         0.0   0.0     29.33          Casa  \n",
       "6   1000.0         0.0   0.0     15.38          Casa  \n",
       "7   1500.0       642.0  74.0     21.43   Apartamento  \n",
       "8   1500.0       455.0  14.0     16.67   Apartamento  \n",
       "9   2000.0       561.0  50.0     50.00   Apartamento  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('dados/alugueis_residencial_tratado_melhorado.csv', sep=';')\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5029.848610924708"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mostra a media da coluna valor\n",
    "dataset['Valor'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   Copacabana\n",
       "1              Jardim Botânico\n",
       "2                       Centro\n",
       "3                 Higienópolis\n",
       "4                     Cachambi\n",
       "5              Barra da Tijuca\n",
       "6                        Ramos\n",
       "7                       Grajaú\n",
       "8          Lins de Vasconcelos\n",
       "12                    Botafogo\n",
       "13                     Taquara\n",
       "14     Freguesia (Jacarepaguá)\n",
       "16                      Tijuca\n",
       "18                      Olaria\n",
       "20                      Leblon\n",
       "21                     Ipanema\n",
       "24                Campo Grande\n",
       "28    Recreio dos Bandeirantes\n",
       "31             Jardim Oceânico\n",
       "38                     Humaitá\n",
       "46                   Península\n",
       "48                       Méier\n",
       "49              Vargem Pequena\n",
       "51                    Maracanã\n",
       "53                 Jacarepaguá\n",
       "59                 São Conrado\n",
       "64              Vila Valqueire\n",
       "65                       Gávea\n",
       "68                 Cosme Velho\n",
       "69                  Bonsucesso\n",
       "Name: Bairro, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Bairro'].drop_duplicates().head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "bairros = ['Barra da Tijuca','Copacabana', 'Ipanema', 'Leblon', 'Botafogo', 'Flamengo', 'Tijuca']\n",
    "selecao = dataset['Bairro'].isin(bairros)\n",
    "dataset = dataset[selecao]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Copacabana\n",
       "5     Barra da Tijuca\n",
       "12           Botafogo\n",
       "16             Tijuca\n",
       "20             Leblon\n",
       "21            Ipanema\n",
       "80           Flamengo\n",
       "Name: Bairro, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Bairro'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cria um grupo com os itens da coluna\n",
    "grupo_bairro = dataset.groupby('Bairro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.generic.DataFrameGroupBy"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#objeto do tipo DataFrameGroupBy\n",
    "type(grupo_bairro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Barra da Tijuca': [5, 15, 17, 23, 32, 34, 37, 44, 45, 62, 63, 66, 70, 76, 81, 95, 109, 112, 114, 119, 132, 134, 138, 154, 157, 159, 169, 170, 177, 190, 193, 212, 219, 226, 229, 238, 239, 242, 245, 250, 265, 271, 281, 283, 298, 299, 300, 302, 309, 315, 318, 332, 342, 343, 352, 367, 373, 375, 381, 387, 394, 396, 407, 419, 439, 440, 442, 443, 446, 456, 459, 470, 471, 473, 481, 484, 492, 502, 508, 511, 519, 535, 546, 547, 548, 549, 552, 556, 561, 563, 564, 565, 581, 595, 598, 599, 601, 605, 607, 612, ...], 'Botafogo': [12, 25, 50, 89, 90, 113, 121, 129, 136, 201, 205, 230, 341, 369, 422, 423, 458, 506, 532, 534, 554, 604, 660, 696, 730, 768, 777, 798, 836, 841, 875, 933, 942, 946, 1024, 1063, 1101, 1113, 1133, 1139, 1150, 1163, 1188, 1225, 1229, 1230, 1248, 1275, 1308, 1321, 1330, 1343, 1374, 1411, 1445, 1450, 1487, 1550, 1562, 1612, 1664, 1668, 1690, 1706, 1727, 1736, 1778, 1779, 1827, 1855, 1864, 1867, 1894, 1964, 1974, 2000, 2015, 2024, 2032, 2073, 2110, 2113, 2126, 2128, 2129, 2133, 2142, 2193, 2194, 2210, 2225, 2243, 2290, 2295, 2346, 2403, 2432, 2447, 2464, 2482, ...], 'Copacabana': [0, 9, 10, 11, 26, 27, 30, 33, 88, 93, 106, 111, 127, 131, 142, 143, 146, 147, 155, 166, 178, 188, 196, 199, 211, 228, 255, 269, 282, 285, 288, 296, 297, 308, 314, 326, 334, 336, 355, 366, 372, 378, 379, 395, 403, 411, 432, 433, 444, 466, 468, 493, 494, 495, 505, 539, 541, 543, 566, 573, 584, 585, 594, 617, 621, 623, 626, 644, 661, 670, 677, 678, 689, 698, 700, 705, 712, 724, 742, 748, 751, 785, 822, 838, 850, 888, 897, 910, 914, 925, 930, 935, 937, 939, 940, 947, 970, 981, 982, 998, ...], 'Flamengo': [80, 140, 223, 291, 328, 354, 363, 368, 376, 400, 428, 437, 449, 485, 553, 591, 631, 680, 738, 776, 809, 869, 902, 907, 918, 961, 985, 1064, 1121, 1151, 1160, 1234, 1259, 1266, 1386, 1425, 1428, 1540, 1546, 1558, 1569, 1579, 1588, 1636, 1639, 1716, 1733, 1798, 1801, 1875, 1885, 1914, 1983, 2016, 2049, 2054, 2079, 2265, 2274, 2312, 2322, 2360, 2384, 2413, 2425, 2477, 2512, 2584, 2585, 2594, 2598, 2623, 2651, 2683, 2694, 2763, 2771, 2776, 2837, 2841, 2931, 2945, 2989, 3006, 3024, 3044, 3107, 3161, 3178, 3204, 3206, 3216, 3255, 3342, 3368, 3403, 3479, 3486, 3498, 3507, ...], 'Ipanema': [21, 41, 42, 47, 54, 55, 75, 86, 94, 116, 123, 130, 145, 163, 181, 183, 221, 222, 235, 248, 251, 276, 290, 333, 345, 348, 377, 384, 393, 399, 412, 451, 490, 497, 523, 531, 544, 576, 579, 586, 587, 588, 590, 600, 602, 625, 635, 639, 658, 659, 667, 682, 692, 701, 719, 721, 728, 734, 737, 741, 743, 758, 759, 765, 769, 792, 796, 806, 814, 824, 826, 830, 843, 873, 874, 893, 904, 915, 917, 923, 924, 941, 945, 956, 969, 971, 988, 1001, 1003, 1012, 1019, 1028, 1032, 1041, 1046, 1052, 1069, 1070, 1072, 1074, ...], 'Leblon': [20, 29, 61, 92, 99, 117, 135, 144, 165, 171, 175, 218, 224, 254, 321, 322, 331, 359, 360, 402, 414, 427, 450, 455, 467, 472, 474, 478, 498, 504, 509, 517, 527, 559, 608, 610, 614, 650, 651, 669, 697, 714, 722, 745, 761, 788, 797, 833, 842, 847, 848, 853, 857, 883, 890, 916, 949, 951, 963, 977, 983, 1073, 1079, 1090, 1091, 1093, 1096, 1109, 1111, 1126, 1165, 1178, 1184, 1189, 1217, 1228, 1232, 1239, 1241, 1261, 1304, 1305, 1310, 1327, 1328, 1362, 1364, 1372, 1396, 1437, 1438, 1441, 1446, 1459, 1536, 1559, 1573, 1580, 1585, 1593, ...], 'Tijuca': [16, 19, 22, 82, 84, 128, 149, 153, 162, 168, 180, 184, 186, 189, 202, 207, 264, 344, 349, 361, 370, 445, 457, 477, 487, 512, 521, 536, 574, 578, 609, 611, 638, 647, 648, 664, 685, 750, 755, 756, 774, 789, 802, 805, 808, 810, 825, 852, 855, 865, 886, 891, 901, 943, 965, 986, 992, 1005, 1008, 1039, 1050, 1058, 1081, 1142, 1201, 1204, 1222, 1288, 1303, 1400, 1462, 1466, 1489, 1511, 1563, 1567, 1596, 1598, 1607, 1614, 1669, 1673, 1693, 1715, 1739, 1741, 1755, 1766, 1788, 1816, 1820, 1829, 1861, 1871, 1872, 1877, 1909, 1919, 1942, 1953, ...]}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dicionario com os itens e os indexs q ele aparece\n",
    "#faz uma indexação do DF para facilitar as consultas\n",
    "grupo_bairro.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barra da Tijuca\n",
      "Botafogo\n",
      "Copacabana\n",
      "Flamengo\n",
      "Ipanema\n",
      "Leblon\n",
      "Tijuca\n"
     ]
    }
   ],
   "source": [
    "for bairro, data in grupo_bairro:\n",
    "    print(bairro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "#dados é um DF para cada bairro com seus dados\n",
    "for bairro, data in grupo_bairro:\n",
    "    print(type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barra da Tijuca -> 6962.35\n",
      "Botafogo -> 8522.79\n",
      "Copacabana -> 4096.41\n",
      "Flamengo -> 4103.39\n",
      "Ipanema -> 9170.72\n",
      "Leblon -> 8581.58\n",
      "Tijuca -> 2043.52\n"
     ]
    }
   ],
   "source": [
    "#calcula o valor medio para cada bairro\n",
    "for bairro, data in grupo_bairro:\n",
    "    print(f'{bairro} -> {data.Valor.mean().round(2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bairro\n",
       "Barra da Tijuca    6962.353253\n",
       "Botafogo           8522.794536\n",
       "Copacabana         4096.410011\n",
       "Flamengo           4103.390821\n",
       "Ipanema            9170.718016\n",
       "Leblon             8581.582451\n",
       "Tijuca             2043.520000\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Forma simples\n",
    "grupo_bairro['Valor'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
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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>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bairro</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Barra da Tijuca</th>\n",
       "      <td>6962.35</td>\n",
       "      <td>3537.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botafogo</th>\n",
       "      <td>8522.79</td>\n",
       "      <td>981.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copacabana</th>\n",
       "      <td>4096.41</td>\n",
       "      <td>1152.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flamengo</th>\n",
       "      <td>4103.39</td>\n",
       "      <td>1099.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ipanema</th>\n",
       "      <td>9170.72</td>\n",
       "      <td>2283.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leblon</th>\n",
       "      <td>8581.58</td>\n",
       "      <td>2091.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tijuca</th>\n",
       "      <td>2043.52</td>\n",
       "      <td>711.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Valor  Condominio\n",
       "Bairro                              \n",
       "Barra da Tijuca  6962.35     3537.38\n",
       "Botafogo         8522.79      981.77\n",
       "Copacabana       4096.41     1152.28\n",
       "Flamengo         4103.39     1099.84\n",
       "Ipanema          9170.72     2283.87\n",
       "Leblon           8581.58     2091.95\n",
       "Tijuca           2043.52      711.69"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pode fazer com mais de uma variavel e criar um DF\n",
    "grupo_bairro[['Valor', 'Condominio']].mean().round(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estatística Descritiva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Bairro</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>Barra da Tijuca</th>\n",
       "      <td>3966.0</td>\n",
       "      <td>6962.353253</td>\n",
       "      <td>11740.048464</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botafogo</th>\n",
       "      <td>915.0</td>\n",
       "      <td>8522.794536</td>\n",
       "      <td>148669.419958</td>\n",
       "      <td>700.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4310.0</td>\n",
       "      <td>4500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copacabana</th>\n",
       "      <td>2717.0</td>\n",
       "      <td>4096.410011</td>\n",
       "      <td>3572.941254</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4792.0</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flamengo</th>\n",
       "      <td>719.0</td>\n",
       "      <td>4103.390821</td>\n",
       "      <td>3828.552301</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ipanema</th>\n",
       "      <td>1915.0</td>\n",
       "      <td>9170.718016</td>\n",
       "      <td>7971.385799</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leblon</th>\n",
       "      <td>1322.0</td>\n",
       "      <td>8581.582451</td>\n",
       "      <td>6877.750631</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tijuca</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>2043.520000</td>\n",
       "      <td>1664.337667</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count         mean            std     min     25%     50%  \\\n",
       "Bairro                                                                        \n",
       "Barra da Tijuca  3966.0  6962.353253   11740.048464   800.0  2500.0  4400.0   \n",
       "Botafogo          915.0  8522.794536  148669.419958   700.0  2200.0  3000.0   \n",
       "Copacabana       2717.0  4096.410011    3572.941254   100.0  2000.0  3000.0   \n",
       "Flamengo          719.0  4103.390821    3828.552301   800.0  1900.0  2900.0   \n",
       "Ipanema          1915.0  9170.718016    7971.385799  1200.0  4400.0  7000.0   \n",
       "Leblon           1322.0  8581.582451    6877.750631   100.0  4500.0  6800.0   \n",
       "Tijuca           1100.0  2043.520000    1664.337667   750.0  1500.0  1800.0   \n",
       "\n",
       "                     75%        max  \n",
       "Bairro                               \n",
       "Barra da Tijuca   8500.0   600000.0  \n",
       "Botafogo          4310.0  4500000.0  \n",
       "Copacabana        4792.0    35000.0  \n",
       "Flamengo          4900.0    35000.0  \n",
       "Ipanema          10500.0    90000.0  \n",
       "Leblon           10000.0   100000.0  \n",
       "Tijuca            2300.0    45000.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe() - gera um conjunto de estatistica descritiva\n",
    "#count = frequencia, mean = media, std = desvio padrao, min = minimo, max = maximo, 25, 50, 75 = primeiro, segundo e terceiro quartio\n",
    "grupo_bairro['Valor'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Problemas: o std tem variações muito grandes em botafogo e barra da tijuca\n",
    "#           o valor máximo apresena valores muito elevados em BTF por exemplo\n",
    "#           informações estão errôneas, muito provavelmente esses valores correspondem à venda e não à locação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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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       "    }\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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bairro</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Barra da Tijuca</th>\n",
       "      <td>800.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>27612693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botafogo</th>\n",
       "      <td>700.0</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>7798357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copacabana</th>\n",
       "      <td>100.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>11129946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flamengo</th>\n",
       "      <td>800.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>2950338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ipanema</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>17561925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leblon</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>11344852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tijuca</th>\n",
       "      <td>750.0</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>2247872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    min        max         sum\n",
       "Bairro                                        \n",
       "Barra da Tijuca   800.0   600000.0  27612693.0\n",
       "Botafogo          700.0  4500000.0   7798357.0\n",
       "Copacabana        100.0    35000.0  11129946.0\n",
       "Flamengo          800.0    35000.0   2950338.0\n",
       "Ipanema          1200.0    90000.0  17561925.0\n",
       "Leblon            100.0   100000.0  11344852.0\n",
       "Tijuca            750.0    45000.0   2247872.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#aggregate mostra o conjunto de estatisticas desejadas\n",
    "grupo_bairro['Valor'].aggregate(['min', 'max', 'sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Mínimo</th>\n",
       "      <th>Máximo</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bairro</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Barra da Tijuca</th>\n",
       "      <td>800.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>27612693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botafogo</th>\n",
       "      <td>700.0</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>7798357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copacabana</th>\n",
       "      <td>100.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>11129946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flamengo</th>\n",
       "      <td>800.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>2950338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ipanema</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>17561925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leblon</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>11344852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tijuca</th>\n",
       "      <td>750.0</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>2247872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Mínimo     Máximo         sum\n",
       "Bairro                                        \n",
       "Barra da Tijuca   800.0   600000.0  27612693.0\n",
       "Botafogo          700.0  4500000.0   7798357.0\n",
       "Copacabana        100.0    35000.0  11129946.0\n",
       "Flamengo          800.0    35000.0   2950338.0\n",
       "Ipanema          1200.0    90000.0  17561925.0\n",
       "Leblon            100.0   100000.0  11344852.0\n",
       "Tijuca            750.0    45000.0   2247872.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename() - organiza melhor os nomes\n",
    "grupo_bairro['Valor'].aggregate(['min', 'max', 'sum']).rename(columns={'min': 'Mínimo', 'max': 'Máximo'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conseguimos visualizar os problemas de ultra variação em alguns pontos do DataFrame porque temos poucas informações"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualização para grandes quantidades de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#para visualizar no jupyter\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#configurar o tamanho desejado para os graficos\n",
    "plt.rc('figure', figsize = (20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Bairro'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b4w0HQ4cO7aXVQN/W0tKS9evXp66uLmPGjMkxxxzT20uCPuexxx57wf9Octhhh2XDhg29sCL2NQGGPmPs2LH56le/mssvv7zH8SVLlmTMmDG9tCro284888zU1dVl982Uzx+rq6vL2972tnz961/PsGHDemmV0He8733vS//+/fPNb34zhxxyiM+egP3gjDPOyI033phx48blrW99a6rVan7wgx/kxz/+cWbOnJmf/OQnOe200/K1r30t73znO3t7udAnPProo/ngBz+Y733ve/n1r39dO+4NB7BvtLW15d3vfne+973v5fWvf32q1Wo6OjpyyimnZMmSJTn44IN7e4nQZ4wcOTI//vGPc/jhh/c4/qMf/SgHHXRQ7yyKfUqAoc/42Mc+lne961155JFH8va3vz1J8p3vfCdf+cpXfP4L7CMrVqzIX/zFX+STn/xk3vrWtyZJ1q5dm7/8y7/Mxz72sVQqlZx//vmZP39+/vZv/7aXVwuvfq2trWlpackb3/jG3l4KvGb86le/yrx58/Kxj32sx/FPfOITeeyxx7J8+fJcccUV+au/+isBBgp5z3vekyT5u7/7uzQ0NHjDAexjF154YTo7O/Pggw/m6KOPTpL85Cc/yTnnnJM5c+bkK1/5Si+vEPqOd7/73ZkzZ06GDBmSP/zDP0zy3O3HPvShD+Xd7353L6+OfcFnwNCn3HnnnVmwYEFaW1szaNCgvOlNb8oVV1yRk046qbeXBn3S2LFj84UvfCEnnHBCj+P/9m//lvPOOy8PPvhgvv3tb+f973+/rbRQwHHHHZfrr78+b3vb23p7KfCaUalU0tLSkt/93d/tcfzf//3fM378+HR0dOShhx7Kcccdl6effrqXVgl9y2/91m+lpaUlo0eP7u2lwGtCpVLJt7/97Rx33HE9jq9duzaTJk3KU0891TsLgz6ou7s7zc3N+ad/+qf07//c3ohnn302733ve/P5z3/+BT9egVc3O2DoU6ZMmZIpU6b09jLgNeORRx55wXtwDx06ND//+c+TJEceeWR+9atf7e+lQZ909dVX55JLLsmCBQsybty4DBgwoMd598SH8g444ICsXr16jwCzevXqHHDAAUme+0tzfX19bywP+qTjjjsuGzduFGBgP3n22Wf3+PfK5LnP1N39M5iA/zcDBw7MV7/61fzVX/1VfvSjH2XQoEEZN25cDjvssN5eGvuIAAPAKzZ+/PhcfPHF+Yd/+IfafYG3bt2aSy65pPbuqZ/97Gf5nd/5nd5cJvQZp512WpLk1FNP7XHcPfFh37nwwgtzwQUXpKWlJccdd1zq6uqydu3a/M3f/E0++tGPJknuvvtuH1QMBf3N3/xNLrjggvzyl7/M2LFj9/gPw29605t6aWXQN7397W/Phz70oXzlK19JU1NTkuSXv/xlPvzhD+/x751AGUcddVSOOuqo3l4G+4FbkNFnvO51r3vRewP7j1JQ3sMPP5x3vvOdefTRRzNq1KjU1dVlw4YNecMb3pDbb789Rx11VL7+9a/n6aefTnNzc28vF171Vq5c+aLn3XIT9o1bb701ixcvzsMPP5wkGT16dC688MLMmDEjSbJjx47U1dXVdsQA/2/WrFmTGTNm5Be/+EXtWF1dnTccwD6ycePGvPOd78y6det6/L1u3Lhxuf32272hDgp6//vf/6Ln/+7v/m4/rYT9RYChz7j99tt7fL9z58788Ic/zBe/+MVceeWVOffcc3tpZdC3VavV3H333fnpT3+aarWaN77xjTn99NPzute9rreXBgDAq9CYMWNy9NFH55JLLklDQ8Meb7RzmxbYN1asWJGHHnoo1Wo1Y8aMqe2+Bsr54z/+4x7f79y5M+vWrctTTz2Vt7/97fna177WSytjXxFg6PO+/OUv56tf/eoegQYAXq2eeeaZbNiwId3d3T2OuyUL7Dvd3d1pa2vb4174hx56aC+tCPquwYMH50c/+tEen70EAH3Rs88+m9mzZ+cNb3hDLrnkkt5eDoUJMPR5jzzySN70pjdl+/btvb0U6JNWrlyZ6667LuvXr09dXV2OPvroXHzxxfmDP/iD3l4a9Dlbt27N+973vtx1110veN4tWaC8n/3sZ3n/+9+f1atX9zjuVkiw70ybNi0zZ87Mu971rt5eCvRZn/3sZ1/27Jw5c/bhSoDkuVu8n3zyydm8eXNvL4XC+vf2AmBf2rFjR2644Qb3K4V95Etf+lLe97735X/+z/+ZOXPmpFqtZvXq1Tn11FNz88031+6ND5Qxd+7ctLe3Z82aNTnllFOydOnSPPHEE/nEJz6RT3/60729POiTZs6cmf79++eb3/xmDjnkkBf9zEGgjGnTpuXDH/5wHnjggYwbNy4DBgzocX769Om9tDLoO66//vqXNVdXVyfAwH7wyCOP5D//8z97exnsA3bA0GcMGzasx1+Iq9Vqnn766Rx44IH50pe+5F/SYR84+uijc9555+XDH/5wj+OLFi3KX//1X2f9+vW9tDLomw455JDcfvvteetb35qhQ4fmBz/4QY466qjccccdueaaa7Jq1areXiL0OYMHD05LS0ve+MY39vZS4DXjxT5L0M4zAF7NLrrooh7fV6vVbN68OXfeeWfOOeecLF68uJdWxr5iBwx9xvXXX98jwLzuda/LwQcfnAkTJmTYsGG9uDLou37+859n2rRpexyfPn16PvrRj/bCiqBv2759e0aOHJkkGT58eLZu3Zqjjjoq48aNy/3339/Lq4O+acyYMfnVr37V28uA15TdP2sJ2H+ef5+2HZ+wb/zwhz/s8f3z//3y/2/v3sN7rv8/jj8+H2NmJ2MOw2yxoWmLkVRfMoo5Jn0vh69vjIgux0lUGpGkb62VQ0yKlFqOX5EwLOUwpzlnGsVUm2QMm5Ftvz/6tuu3HFI+26u9d79dl+vaXu93211XXTs8P+/XKzo6Wv379zdUhaLEAAaWERERYToBKHV8fX21YcOGaw5I3bBhg3x9fQ1VAdZVv359HTlyRP7+/mrUqJFiY2Pl7++v2bNny8fHx3QeYEmvvvqqxowZoylTplx3KyQPDw9DZUDpkJOTo/Lly5vOACzv3XffVUxMjFJSUiRJgYGBGjlypAYMGGC4DLCWhIQE0wkoZgxgUKLt379fd911l+x2u/bv33/Te93c3OTr63vND80A/rqnn35aw4cP1969e3X//ffLZrNp8+bNmj9/vt566y3TeYDljBw5suBQxgkTJqhdu3ZauHChypUrp/nz55uNAyzqoYcekiS1adOm0Hp+fj5bIQFFJDc3V1OmTNHs2bN16tQpffPNN6pTp46ioqLk7++vJ554wnQiYClRUVGKiYnRsGHDdN9990mStm3bpsjISB0/flyTJ082XAgAJRdnwKBEs9vtSk9PV9WqVWW322Wz2XSz/6Q9PT01e/Zs9ejRoxgrAWtbvny5oqOjC857ufPOO/XMM8/okUceMVwGWF92draSk5NVu3ZteXt7m84BLGnTpk03vf7ggw8WUwlQekyaNEnvv/++Jk2apIEDB+rgwYOqU6eOFi1apJiYGG3bts10ImAp3t7emj59unr16lVo/eOPP9awYcPYihO4TaGhodqwYYO8vLzUuHHjm27x5+bmpoYNG+r5559nZxGLYACDEu3EiROqXbu2bDabTpw4cdN7L1++rMWLF+udd97R8ePHiycQsKBp06bpySefVPny5ZWamipfX1/2BwYMYH9uAIBVBQQEKDY2Vm3atJG7u7v27dunOnXqKDk5Wffdd5/Onj1rOhGwFC8vL+3YsUOBgYGF1r/55hs1a9ZM586dMxMGWMTEiRP1zDPPqEKFCpo4ceJN7718+bI2bNig8uXL/+ELgVAyMIBBqXL27Fk98cQTWrZsmekUoMRycnLSjz/+qKpVq6pMmTJKS0srOBQcQNFjf26g+H311VeKjY3Vt99+q8WLF6tmzZr64IMPdMcdd+gf//iH6TzAclxcXJScnCw/P79CA5ivv/5azZo108WLF00nApYybNgwlS1bVm+88Uah9dGjR+vSpUuaOXOmoTKgdDp27JgaNmyonJwc0ylwAM6AQYn2Z86ACQkJkZeXF8MX4DbVqFFDS5cuVYcOHZSfn6/vv//+ht8U1K5du5jrAGtjf26g+C1dulSPP/64evfuraSkJF2+fFmSdOHCBU2ZMkWrV682XAhYT8OGDfXVV1/Jz8+v0PrixYvVuHFjQ1WAtYwaNargbZvNprlz52rdunVq3ry5JCkxMVEnT55Unz59TCUCpVbdunV16tQp0xlwEJ6AQYl2q2fAcEAq4Dhz5szRsGHDdPXq1Rvew8HEQNFgf26g+DVu3FiRkZHq06dPoVfi7927V+Hh4UpPTzedCFjOypUr9fjjj+u5557TpEmTNHHiRB05ckQLFizQqlWr9PDDD5tOBEq8sLCwW7rPZrNp48aNRVwDWNufOQMmKSmpGMtQHHgCBiXad999pypVqhS8DaDoPfnkk+rVq5dOnDihkJAQrV+/XpUrVzadBZQKubm5atq06TXrTZo0uelQFMBfd+TIEbVs2fKadQ8PD/bEB4pI586d9cknn2jKlCmy2WwaP368QkNDtXLlSoYvgIMkJCSYTgBKjUceeUTOzs6SpK5du5qNQbHjCRiUeJxBAZjz/vvvq2fPngXfSAAoWuzPDRS/unXrKjY2Vg899FChJ2AWLFigqVOn6uuvvzadCACAQxw9elTHjh1Ty5Yt5eLiUrCzAYDb179/f7311ltyd3c3nYJixhMwKPGYIQLm9O3b13QCYHnszw2YNWjQII0YMULvvfeebDabfvzxR23btk2jR4/W+PHjTecBlnblyhX99NNPysvLK7TOOYOAY505c0bdu3dXQkKCbDabUlJSVKdOHQ0YMEAVK1ZUdHS06USgxHv//fc1depUBjClEAMYAMBflpubq5iYGC1atEipqam6cuVKoesZGRmGygDr2LNnT6H3mzRpIkk6duyYJKlKlSqqUqWKDh06VOxtQGkwZswYZWZmKiwsTDk5OWrZsqWcnZ01evRoDR061HQeYEkpKSnq37+/tm7dWmidcwaBohEZGamyZcsqNTVVd955Z8F6jx49FBkZyQAGcABeQF56MYCBJaxdu1aenp43vadLly7FVAOUHhMnTtTcuXM1atQoRUVFady4cTp+/Lj++9//8qpgwEHYnxsw7+WXX9a4ceP09ddfKy8vT0FBQXJzczOdBVhWRESEnJyctGrVKvn4+LAFElDE1q1bp7Vr16pWrVqF1gMDA3XixAlDVYD18PWsdOIMGJR4drv9D+/hVVJA0ahbt66mTZumjh07yt3dXXv37i1YS0xM1EcffWQ6EQAAACWMq6urdu/erQYNGphOAUoFd3d3JSUlKTAwsNB5Zzt37lR4eLjOnDljOhEo8ex2uzw9Pf9wCMNOItbDEzCwhPT0dFWtWtV0BlDqpKenKzg4WJLk5uamzMxMSVKnTp0UFRVlMg2wrJ07d2rx4sXX3fZv2bJlhqoA68rJydH06dOVkJBw3bMokpKSDJUB1hUUFKSff/7ZdAZQarRs2VILFizQSy+9JOnXF7Hm5eXptddeU6tWrczGARYyceLEP9zBB9bDAAYlHo/vAebUqlVLaWlpql27tgICArRu3TqFhoZq586dcnZ2Np0HWE5cXJz69Omjtm3bKj4+Xm3btlVKSorS09P16KOPms4DLKl///6Kj4/XP//5TzVr1ozvPYFi8Oqrr2rMmDGaMmWKgoODVbZs2ULXPTw8DJUB1vTboGXXrl26cuWKxowZo0OHDikjI0NbtmwxnQdYRs+ePXkBeSnEFmQo8ex2O0/AAIY8++yz8vDw0PPPP68lS5aoV69e8vf3V2pqqiIjIzV16lTTiYClhISEaNCgQRoyZEjB9hB33HGHBg0aJB8fH02cONF0ImA5np6eWr16tR544AHTKUCp8ds2078feObn57O9NFBE0tPTNWvWLO3evVt5eXkKDQ3VoEGDNGHCBL333num84ASr0yZMkpLS+P3l6UQAxiUeP369dO0adPk7u5uOgUo9bZv364tW7YoICBAXbp0MZ0DWI6rq6sOHTokf39/eXt7KyEhQcHBwTp8+LBat26ttLQ004mA5QQFBSkuLk4hISGmU4BSY9OmTTe9/uCDDxZTCVC67du3T6GhoQw9AQfgBeSlF1uQocSbN2+e6QSg1Pryyy91//33y8np1y8n9957r+69915dvXpVX375pVq2bGm4ELCWSpUq6cKFC5KkmjVr6uDBgwoODta5c+eUnZ1tuA6wpujoaI0dO1azZ8+Wn5+f6RygVGDAAgCwmt+fI4jSgwEMAOAvCwsLu+4jtJmZmQoLC+OVUoCDtWjRQvHx8QoODlb37t01YsQIbdy4UfHx8WrTpo3pPMCSmjZtqpycHNWpU0cVKlS45iyKjIwMQ2WA9WVnZys1NVVXrlwptM4TaQAAoKRgAAMA+Mt+24f7986cOSNXV1cDRYC1zZgxQzk5OZKk5557TmXLltXmzZvVrVs3RUVFGa4DrKlXr1764YcfNGXKFFWrVu26X/cAONbp06fVr18/ff7559e9zot8AABAScEZMACAP61bt26SpBUrVig8PFzOzs4F13Jzc7V//37Vr19fa9asMZUIAIBDVKhQQdu2bdPdd99tOgUoNXr37q3jx4/rzTffVFhYmJYvX65Tp05p8uTJio6OVseOHU0nApbw2891N3Lu3Dlt2rSJoScA3AaegAEA/Gmenp6Sfn0Cxt3dXS4uLgXXypUrp+bNm2vgwIGm8gDLWr16tcqUKaN27doVWl+3bp1yc3PVvn17Q2WAdTVo0ECXLl0ynQGUKhs3btSKFSt0zz33yG63y8/PTw8//LA8PDz0yiuvMIABHOS3n+tudr1Pnz7FVAMA1sQABpayc+dOLV68+Lr7BC9btsxQFWA98+bNkyT5+/tr9OjRbDcGFJNnn31WU6dOvWY9Ly9Pzz77LAMYoAhMnTpVTz/9tF5++WUFBwdfcwaMh4eHoTLAurKysgrOGKxUqZJOnz6tevXqKTg4WElJSYbrAOv47ec6AEDRYQADy4iLi1OfPn3Utm1bxcfHq23btkpJSVF6eroeffRR03mAJU2YMEHSr/t0HzlyRDabTfXq1VOVKlUMlwHWlJKSoqCgoGvWGzRooKNHjxooAqwvPDxcktSmTZtC67+dg8a2LIDj1a9fX0eOHJG/v78aNWqk2NhY+fv7a/bs2fLx8TGdBwAAcMsYwMAypkyZopiYGA0ZMkTu7u566623dMcdd2jQoEF8kw4UkezsbA0dOlQLFixQXl6eJKlMmTLq06ePpk+frgoVKhguBKzF09NT3377rfz9/QutHz16lCfRgCKSkJBgOgEodUaOHKm0tDRJv77gp127dlq4cKHKlSun+fPnm40DAAD4E2z5+fn5piMAR3B1ddWhQ4fk7+8vb29vJSQkKDg4WIcPH1br1q0LvoEH4DiDBg3S+vXrNWPGDD3wwAOSpM2bN2v48OF6+OGHNWvWLMOFgLU8+eSTSkxM1PLly1W3bl1Jvw5fHnvsMd1zzz2aO3eu4UIAABwvOztbycnJql27try9vU3nAAAA3DKegIFlVKpUSRcuXJAk1axZUwcPHlRwcLDOnTun7Oxsw3WANS1dulRLlixRq1atCtY6dOggFxcXde/enQEM4GCvvfaawsPD1aBBA9WqVUuS9P3336tFixZ6/fXXDdcB1padnX3dcwZDQkIMFQGlQ35+vlxcXBQaGmo6BQAA4E9jAAPLaNGiheLj4xUcHKzu3btrxIgR2rhxo+Lj46/ZsxuAY2RnZ6tatWrXrFetWpXBJ1AEPD09tXXrVsXHx2vfvn1ycXFRSEiIWrZsaToNsKzTp0+rX79++vzzz697nTNggKLx7rvvKiYmRikpKZKkwMBAjRw5UgMGDDBcBgAAcOvYggyWkZGRoZycHNWoUUN5eXl6/fXXtXnzZgUEBCgqKkpeXl6mEwHLadOmjSpXrqwFCxaofPnykqRLly6pb9++ysjI0Pr16w0XAgBwe3r37q3jx4/rzTffVFhYmJYvX65Tp05p8uTJio6OVseOHU0nApYTFRWlmJgYDRs2TPfdd58kadu2bZoxY4ZGjBihyZMnGy4EAAC4NQxgAAB/2cGDBxUeHq6cnBzdfffdstls2rt3r5ydnbVu3To1bNjQdCJgOVlZWdq0adN1t0IaPny4oSrAunx8fLRixQo1a9ZMHh4e2rVrl+rVq6dPP/1U//nPf7R582bTiYDleHt7a/r06erVq1eh9Y8//ljDhg3Tzz//bKgMAADgz2ELMpRo58+fv+V7PTw8irAEKJ3uuusupaSk6MMPP1RycrLy8/PVs2dP9e7dWy4uLqbzAMvZs2ePOnTooOzsbGVlZalSpUr6+eefVaFCBVWtWpUBDFAEsrKyVLVqVUm/njl4+vRp1atXT8HBwUpKSjJcB1hTbm6umjZtes16kyZNdPXqVQNFAAAAf43ddABwOypWrCgvL69b+gPA8c6cOSMXFxcNHDhQI0aMkJubm44cOaJdu3aZTgMsKTIyUp07d1ZGRoZcXFyUmJioEydOqEmTJnr99ddN5wGWVL9+fR05ckSS1KhRI8XGxuqHH37Q7Nmz5ePjY7gOsKZ///vfmjVr1jXrc+bMUe/evQ0UAQAA/DVsQYYSbdOmTQVvHz9+XM8++6wiIiIK7RP8/vvv65VXXlHfvn1NZQKWc+DAAXXu3FknT55UYGCg4uLiFB4erqysLNntdmVlZWnJkiXq2rWr6VTAUipWrKjt27erfv36qlixorZt26Y777xT27dvV9++fZWcnGw6EbCchQsX6pdfflFERIT27Nmjdu3a6cyZMypXrpzmz5+vHj16mE4ELGfYsGFasGCBfH191bx5c0lSYmKiTp48qT59+qhs2bIF977xxhumMgEAAP4QAxhYRps2bTRgwIBr9gn+6KOPNGfOHH3xxRdmwgALat++vZycnDR27Fh9+OGHWrVqldq2bau5c+dK+vWH5t27dysxMdFwKWAtVapU0ZYtW1SvXj3Vr19f06ZNU7t27ZScnKzQ0FBlZ2ebTgQsLzs7W8nJyapdu7a8vb1N5wCWFBYWdkv32Ww2bdy4sYhrAAAA/joGMLCMChUqaN++fQoMDCy0/s0336hRo0b8UgpwIG9vb23cuFEhISG6ePGiPDw8tGPHjoK9upOTk9W8eXOdO3fObChgMW3btlVERIT+9a9/afDgwdqzZ4+GDx+uDz74QGfPntX27dtNJwIAAAAAgP9xMh0AOIqvr69mz56t6OjoQuuxsbHy9fU1VAVYU0ZGhqpXry5JcnNzk6urqypVqlRw3cvLSxcuXDCVB1jWlClTCv7feumll9S3b1899dRTCggI0Lx58wzXAdYxatSoW76X7Y8Ax+nWrdsf3mOz2bR06dJiqAEAALh9DGBgGTExMXrssce0du3aQvsEHzt2jG/QgSJgs9lu+j4Ax/vtKTPp1+3IVq9ebbAGsK49e/bc0n187QMcy9PT03QCAACAQ7EFGSzl+++/16xZs3T48GHl5+crKChIgwcP5gkYwMHsdrvat28vZ2dnSdLKlSvVunVrubq6SpIuX76sNWvWKDc312QmYFk//fSTjhw5IpvNpvr166tKlSqmkwDL+fbbb+Xv7y+73W46BQAAAEAJxQAGAPCn9evX75buY0skwLHOnz+vIUOGKC4urmDAWaZMGfXo0UMzZ87klcOAA5UpU0ZpaWmqWrWqJKlHjx6aNm2aqlWrZrgMAAAAQEnBAAYAAKCE6N69u/bu3avp06frvvvuk81m09atWzVixAiFhIRo0aJFphMBy7Db7UpPTy8YwLi7u2vfvn2qU6eO4TIAAAAAJQUDGAAAgBLC1dVVa9eu1T/+8Y9C61999ZXCw8OVlZVlqAywHgYwAAAAAG4XGxoDAACUEJU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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grupo_bairro['Valor'].std().plot.bar(color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Valor Médio do Aluguel')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = grupo_bairro['Valor'].mean().plot.bar(color = 'blue')\n",
    "fig.set_ylabel('Valor do Aluguel')\n",
    "fig.set_title('Valor Médio do Aluguel', {'fontsize' : 22})"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ambiente",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "078be856d3baac0a1b5fca3d500da5e3f0197fb170b2c6c6bc3b09ecf224b0d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}