AnalisePopulacao.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise da população brasileira\n",
    "\n",
    "Uma coisa importante na análise de dados é entender o contexto dos dados coletados, qual a realidade que eles estão inseridos. \n",
    "\n",
    "Por exemplo:\n",
    "**Parece aceitável que mais da metade do congresso seja formada por homens brancos se esse grupo demográfico for mais da metade da população?** Nesse caso nós poderíamos pensar que existes mais indivíduos desse grupo na população e que por isso eles tem uma grande representatividade no congresso... Legal, vamos testar!\n",
    "\n",
    "Vamos então tentar entender qual é o contexto das informações que vimos na análise anterior:\n",
    "- Qual o perfil da população brasileira quanto a gênero, cor ou raça e situação do domicílio?\n",
    "- Como esse perfil se comporta quando analisamos por estado?\n",
    "\n",
    "\n",
    "Os dados aqui analisados são do censo de 2010, disponível pelo SIDRA [Sistema IBGE de Recuperação Automática](https://sidra.ibge.gov.br/home/pms/brasil) [Nessa tabela](https://sidra.ibge.gov.br/tabela/3175)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import io\n",
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unidade da Federação</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th>Situação do domicílio</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>18720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>6024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>1755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>26827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>9230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>16735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>6252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>2665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>30783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>11985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>17809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>7160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>3312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9054</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>25653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9055</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9056</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9057</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9058</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9059</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9060</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>25954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9061</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9062</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>3037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9063</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9064</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9065</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9066</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>15849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9067</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9069</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9070</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9071</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9072</th>\n",
       "      <td>Fonte: IBGE - Censo Demográfico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9073</th>\n",
       "      <td>Notas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9074</th>\n",
       "      <td>1 - Dados do Universo.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9075</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9076</th>\n",
       "      <td>Legenda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9077</th>\n",
       "      <td>Símbolo</td>\n",
       "      <td>Significado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9078</th>\n",
       "      <td>-</td>\n",
       "      <td>Zero absoluto, não resultante de um cálculo ou...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9079</th>\n",
       "      <td>0</td>\n",
       "      <td>Zero resultante de um cálculo ou arredondament...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9080</th>\n",
       "      <td>X</td>\n",
       "      <td>Valor inibido para não identificar o informant...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9081</th>\n",
       "      <td>..</td>\n",
       "      <td>Valor não se aplica.\\nEx: Não se pode obter o ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9082</th>\n",
       "      <td>...</td>\n",
       "      <td>Valor não disponível.\\nEx: A produção de feijã...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9083</th>\n",
       "      <td>A a Z\\n(exceto X)</td>\n",
       "      <td>Significa uma faixa de valores. Varia em funçã...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9084 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Unidade da Federação  \\\n",
       "0                            Rondônia   \n",
       "1                            Rondônia   \n",
       "2                            Rondônia   \n",
       "3                            Rondônia   \n",
       "4                            Rondônia   \n",
       "5                            Rondônia   \n",
       "6                            Rondônia   \n",
       "7                            Rondônia   \n",
       "8                            Rondônia   \n",
       "9                            Rondônia   \n",
       "10                           Rondônia   \n",
       "11                           Rondônia   \n",
       "12                           Rondônia   \n",
       "13                           Rondônia   \n",
       "14                           Rondônia   \n",
       "15                           Rondônia   \n",
       "16                           Rondônia   \n",
       "17                           Rondônia   \n",
       "18                           Rondônia   \n",
       "19                           Rondônia   \n",
       "20                           Rondônia   \n",
       "21                           Rondônia   \n",
       "22                           Rondônia   \n",
       "23                           Rondônia   \n",
       "24                           Rondônia   \n",
       "25                           Rondônia   \n",
       "26                           Rondônia   \n",
       "27                           Rondônia   \n",
       "28                           Rondônia   \n",
       "29                           Rondônia   \n",
       "...                               ...   \n",
       "9054                 Distrito Federal   \n",
       "9055                 Distrito Federal   \n",
       "9056                 Distrito Federal   \n",
       "9057                 Distrito Federal   \n",
       "9058                 Distrito Federal   \n",
       "9059                 Distrito Federal   \n",
       "9060                 Distrito Federal   \n",
       "9061                 Distrito Federal   \n",
       "9062                 Distrito Federal   \n",
       "9063                 Distrito Federal   \n",
       "9064                 Distrito Federal   \n",
       "9065                 Distrito Federal   \n",
       "9066                 Distrito Federal   \n",
       "9067                 Distrito Federal   \n",
       "9068                 Distrito Federal   \n",
       "9069                 Distrito Federal   \n",
       "9070                 Distrito Federal   \n",
       "9071                 Distrito Federal   \n",
       "9072  Fonte: IBGE - Censo Demográfico   \n",
       "9073                            Notas   \n",
       "9074           1 - Dados do Universo.   \n",
       "9075                              NaN   \n",
       "9076                          Legenda   \n",
       "9077                          Símbolo   \n",
       "9078                                -   \n",
       "9079                                0   \n",
       "9080                                X   \n",
       "9081                               ..   \n",
       "9082                              ...   \n",
       "9083                A a Z\\n(exceto X)   \n",
       "\n",
       "                                                   Sexo            Idade  \\\n",
       "0                                                Homens       0 a 4 anos   \n",
       "1                                                Homens       0 a 4 anos   \n",
       "2                                                Homens       0 a 4 anos   \n",
       "3                                                Homens       0 a 4 anos   \n",
       "4                                                Homens       0 a 4 anos   \n",
       "5                                                Homens       0 a 4 anos   \n",
       "6                                                Homens       0 a 4 anos   \n",
       "7                                                Homens       0 a 4 anos   \n",
       "8                                                Homens       0 a 4 anos   \n",
       "9                                                Homens       0 a 4 anos   \n",
       "10                                               Homens       0 a 4 anos   \n",
       "11                                               Homens       0 a 4 anos   \n",
       "12                                               Homens       5 a 9 anos   \n",
       "13                                               Homens       5 a 9 anos   \n",
       "14                                               Homens       5 a 9 anos   \n",
       "15                                               Homens       5 a 9 anos   \n",
       "16                                               Homens       5 a 9 anos   \n",
       "17                                               Homens       5 a 9 anos   \n",
       "18                                               Homens       5 a 9 anos   \n",
       "19                                               Homens       5 a 9 anos   \n",
       "20                                               Homens       5 a 9 anos   \n",
       "21                                               Homens       5 a 9 anos   \n",
       "22                                               Homens       5 a 9 anos   \n",
       "23                                               Homens       5 a 9 anos   \n",
       "24                                               Homens     10 a 14 anos   \n",
       "25                                               Homens     10 a 14 anos   \n",
       "26                                               Homens     10 a 14 anos   \n",
       "27                                               Homens     10 a 14 anos   \n",
       "28                                               Homens     10 a 14 anos   \n",
       "29                                               Homens     10 a 14 anos   \n",
       "...                                                 ...              ...   \n",
       "9054                                           Mulheres     60 a 69 anos   \n",
       "9055                                           Mulheres     60 a 69 anos   \n",
       "9056                                           Mulheres     60 a 69 anos   \n",
       "9057                                           Mulheres     60 a 69 anos   \n",
       "9058                                           Mulheres     60 a 69 anos   \n",
       "9059                                           Mulheres     60 a 69 anos   \n",
       "9060                                           Mulheres  70 anos ou mais   \n",
       "9061                                           Mulheres  70 anos ou mais   \n",
       "9062                                           Mulheres  70 anos ou mais   \n",
       "9063                                           Mulheres  70 anos ou mais   \n",
       "9064                                           Mulheres  70 anos ou mais   \n",
       "9065                                           Mulheres  70 anos ou mais   \n",
       "9066                                           Mulheres  70 anos ou mais   \n",
       "9067                                           Mulheres  70 anos ou mais   \n",
       "9068                                           Mulheres  70 anos ou mais   \n",
       "9069                                           Mulheres  70 anos ou mais   \n",
       "9070                                           Mulheres  70 anos ou mais   \n",
       "9071                                           Mulheres  70 anos ou mais   \n",
       "9072                                                NaN              NaN   \n",
       "9073                                                NaN              NaN   \n",
       "9074                                                NaN              NaN   \n",
       "9075                                                NaN              NaN   \n",
       "9076                                                NaN              NaN   \n",
       "9077                                        Significado              NaN   \n",
       "9078  Zero absoluto, não resultante de um cálculo ou...              NaN   \n",
       "9079  Zero resultante de um cálculo ou arredondament...              NaN   \n",
       "9080  Valor inibido para não identificar o informant...              NaN   \n",
       "9081  Valor não se aplica.\\nEx: Não se pode obter o ...              NaN   \n",
       "9082  Valor não disponível.\\nEx: A produção de feijã...              NaN   \n",
       "9083  Significa uma faixa de valores. Varia em funçã...              NaN   \n",
       "\n",
       "         Ano     Cor ou raça Situação do domicílio Unnamed: 6  \n",
       "0     2010.0          Branca                Urbana      18720  \n",
       "1     2010.0          Branca                 Rural       6024  \n",
       "2     2010.0           Preta                Urbana       1755  \n",
       "3     2010.0           Preta                 Rural        580  \n",
       "4     2010.0         Amarela                Urbana        643  \n",
       "5     2010.0         Amarela                 Rural        162  \n",
       "6     2010.0           Parda                Urbana      26827  \n",
       "7     2010.0           Parda                 Rural       9230  \n",
       "8     2010.0        Indígena                Urbana        116  \n",
       "9     2010.0        Indígena                 Rural        793  \n",
       "10    2010.0  Sem declaração                Urbana          4  \n",
       "11    2010.0  Sem declaração                 Rural          -  \n",
       "12    2010.0          Branca                Urbana      16735  \n",
       "13    2010.0          Branca                 Rural       6252  \n",
       "14    2010.0           Preta                Urbana       2665  \n",
       "15    2010.0           Preta                 Rural        938  \n",
       "16    2010.0         Amarela                Urbana        790  \n",
       "17    2010.0         Amarela                 Rural        186  \n",
       "18    2010.0           Parda                Urbana      30783  \n",
       "19    2010.0           Parda                 Rural      11985  \n",
       "20    2010.0        Indígena                Urbana        124  \n",
       "21    2010.0        Indígena                 Rural        753  \n",
       "22    2010.0  Sem declaração                Urbana          5  \n",
       "23    2010.0  Sem declaração                 Rural          -  \n",
       "24    2010.0          Branca                Urbana      17809  \n",
       "25    2010.0          Branca                 Rural       7160  \n",
       "26    2010.0           Preta                Urbana       3312  \n",
       "27    2010.0           Preta                 Rural       1171  \n",
       "28    2010.0         Amarela                Urbana        777  \n",
       "29    2010.0         Amarela                 Rural        195  \n",
       "...      ...             ...                   ...        ...  \n",
       "9054  2010.0           Parda                Urbana      25653  \n",
       "9055  2010.0           Parda                 Rural        704  \n",
       "9056  2010.0        Indígena                Urbana        222  \n",
       "9057  2010.0        Indígena                 Rural          4  \n",
       "9058  2010.0  Sem declaração                Urbana          3  \n",
       "9059  2010.0  Sem declaração                 Rural          -  \n",
       "9060  2010.0          Branca                Urbana      25954  \n",
       "9061  2010.0          Branca                 Rural        455  \n",
       "9062  2010.0           Preta                Urbana       3037  \n",
       "9063  2010.0           Preta                 Rural         60  \n",
       "9064  2010.0         Amarela                Urbana        627  \n",
       "9065  2010.0         Amarela                 Rural         71  \n",
       "9066  2010.0           Parda                Urbana      15849  \n",
       "9067  2010.0           Parda                 Rural        437  \n",
       "9068  2010.0        Indígena                Urbana        139  \n",
       "9069  2010.0        Indígena                 Rural          4  \n",
       "9070  2010.0  Sem declaração                Urbana          4  \n",
       "9071  2010.0  Sem declaração                 Rural          1  \n",
       "9072     NaN             NaN                   NaN        NaN  \n",
       "9073     NaN             NaN                   NaN        NaN  \n",
       "9074     NaN             NaN                   NaN        NaN  \n",
       "9075     NaN             NaN                   NaN        NaN  \n",
       "9076     NaN             NaN                   NaN        NaN  \n",
       "9077     NaN             NaN                   NaN        NaN  \n",
       "9078     NaN             NaN                   NaN        NaN  \n",
       "9079     NaN             NaN                   NaN        NaN  \n",
       "9080     NaN             NaN                   NaN        NaN  \n",
       "9081     NaN             NaN                   NaN        NaN  \n",
       "9082     NaN             NaN                   NaN        NaN  \n",
       "9083     NaN             NaN                   NaN        NaN  \n",
       "\n",
       "[9084 rows x 7 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/pyladiespb-org/minicurso-analise-descritiva-dados-abertos/master/data/populacao/populacao_idade_agregada.csv'\n",
    "dados_url = requests.get(url).content\n",
    "populacao = pd.read_csv(io.StringIO(dados_url.decode('utf-8')), delimiter=';', header=2)\n",
    "populacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unidade da Federação</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th>Situação do domicílio</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>18720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>6024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>1755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>26827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>9230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>16735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>6252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>2665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>30783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>11985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>5 a 9 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>17809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>7160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>3312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>10 a 14 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9042</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>55 a 59 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>21693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9043</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>55 a 59 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9044</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>55 a 59 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9045</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>55 a 59 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9046</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>55 a 59 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9047</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>55 a 59 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9048</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>33083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9049</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9050</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>4501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9051</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9052</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9053</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9054</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>25653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9055</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9056</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9057</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9058</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9059</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>60 a 69 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9060</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>25954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9061</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9062</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>3037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9063</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9064</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9065</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Rural</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9066</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>15849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9067</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Rural</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9069</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Indígena</td>\n",
       "      <td>Rural</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9070</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9071</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>Mulheres</td>\n",
       "      <td>70 anos ou mais</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sem declaração</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9072 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unidade da Federação      Sexo            Idade     Ano     Cor ou raça  \\\n",
       "0                Rondônia    Homens       0 a 4 anos  2010.0          Branca   \n",
       "1                Rondônia    Homens       0 a 4 anos  2010.0          Branca   \n",
       "2                Rondônia    Homens       0 a 4 anos  2010.0           Preta   \n",
       "3                Rondônia    Homens       0 a 4 anos  2010.0           Preta   \n",
       "4                Rondônia    Homens       0 a 4 anos  2010.0         Amarela   \n",
       "5                Rondônia    Homens       0 a 4 anos  2010.0         Amarela   \n",
       "6                Rondônia    Homens       0 a 4 anos  2010.0           Parda   \n",
       "7                Rondônia    Homens       0 a 4 anos  2010.0           Parda   \n",
       "8                Rondônia    Homens       0 a 4 anos  2010.0        Indígena   \n",
       "9                Rondônia    Homens       0 a 4 anos  2010.0        Indígena   \n",
       "10               Rondônia    Homens       0 a 4 anos  2010.0  Sem declaração   \n",
       "11               Rondônia    Homens       0 a 4 anos  2010.0  Sem declaração   \n",
       "12               Rondônia    Homens       5 a 9 anos  2010.0          Branca   \n",
       "13               Rondônia    Homens       5 a 9 anos  2010.0          Branca   \n",
       "14               Rondônia    Homens       5 a 9 anos  2010.0           Preta   \n",
       "15               Rondônia    Homens       5 a 9 anos  2010.0           Preta   \n",
       "16               Rondônia    Homens       5 a 9 anos  2010.0         Amarela   \n",
       "17               Rondônia    Homens       5 a 9 anos  2010.0         Amarela   \n",
       "18               Rondônia    Homens       5 a 9 anos  2010.0           Parda   \n",
       "19               Rondônia    Homens       5 a 9 anos  2010.0           Parda   \n",
       "20               Rondônia    Homens       5 a 9 anos  2010.0        Indígena   \n",
       "21               Rondônia    Homens       5 a 9 anos  2010.0        Indígena   \n",
       "22               Rondônia    Homens       5 a 9 anos  2010.0  Sem declaração   \n",
       "23               Rondônia    Homens       5 a 9 anos  2010.0  Sem declaração   \n",
       "24               Rondônia    Homens     10 a 14 anos  2010.0          Branca   \n",
       "25               Rondônia    Homens     10 a 14 anos  2010.0          Branca   \n",
       "26               Rondônia    Homens     10 a 14 anos  2010.0           Preta   \n",
       "27               Rondônia    Homens     10 a 14 anos  2010.0           Preta   \n",
       "28               Rondônia    Homens     10 a 14 anos  2010.0         Amarela   \n",
       "29               Rondônia    Homens     10 a 14 anos  2010.0         Amarela   \n",
       "...                   ...       ...              ...     ...             ...   \n",
       "9042     Distrito Federal  Mulheres     55 a 59 anos  2010.0           Parda   \n",
       "9043     Distrito Federal  Mulheres     55 a 59 anos  2010.0           Parda   \n",
       "9044     Distrito Federal  Mulheres     55 a 59 anos  2010.0        Indígena   \n",
       "9045     Distrito Federal  Mulheres     55 a 59 anos  2010.0        Indígena   \n",
       "9046     Distrito Federal  Mulheres     55 a 59 anos  2010.0  Sem declaração   \n",
       "9047     Distrito Federal  Mulheres     55 a 59 anos  2010.0  Sem declaração   \n",
       "9048     Distrito Federal  Mulheres     60 a 69 anos  2010.0          Branca   \n",
       "9049     Distrito Federal  Mulheres     60 a 69 anos  2010.0          Branca   \n",
       "9050     Distrito Federal  Mulheres     60 a 69 anos  2010.0           Preta   \n",
       "9051     Distrito Federal  Mulheres     60 a 69 anos  2010.0           Preta   \n",
       "9052     Distrito Federal  Mulheres     60 a 69 anos  2010.0         Amarela   \n",
       "9053     Distrito Federal  Mulheres     60 a 69 anos  2010.0         Amarela   \n",
       "9054     Distrito Federal  Mulheres     60 a 69 anos  2010.0           Parda   \n",
       "9055     Distrito Federal  Mulheres     60 a 69 anos  2010.0           Parda   \n",
       "9056     Distrito Federal  Mulheres     60 a 69 anos  2010.0        Indígena   \n",
       "9057     Distrito Federal  Mulheres     60 a 69 anos  2010.0        Indígena   \n",
       "9058     Distrito Federal  Mulheres     60 a 69 anos  2010.0  Sem declaração   \n",
       "9059     Distrito Federal  Mulheres     60 a 69 anos  2010.0  Sem declaração   \n",
       "9060     Distrito Federal  Mulheres  70 anos ou mais  2010.0          Branca   \n",
       "9061     Distrito Federal  Mulheres  70 anos ou mais  2010.0          Branca   \n",
       "9062     Distrito Federal  Mulheres  70 anos ou mais  2010.0           Preta   \n",
       "9063     Distrito Federal  Mulheres  70 anos ou mais  2010.0           Preta   \n",
       "9064     Distrito Federal  Mulheres  70 anos ou mais  2010.0         Amarela   \n",
       "9065     Distrito Federal  Mulheres  70 anos ou mais  2010.0         Amarela   \n",
       "9066     Distrito Federal  Mulheres  70 anos ou mais  2010.0           Parda   \n",
       "9067     Distrito Federal  Mulheres  70 anos ou mais  2010.0           Parda   \n",
       "9068     Distrito Federal  Mulheres  70 anos ou mais  2010.0        Indígena   \n",
       "9069     Distrito Federal  Mulheres  70 anos ou mais  2010.0        Indígena   \n",
       "9070     Distrito Federal  Mulheres  70 anos ou mais  2010.0  Sem declaração   \n",
       "9071     Distrito Federal  Mulheres  70 anos ou mais  2010.0  Sem declaração   \n",
       "\n",
       "     Situação do domicílio Unnamed: 6  \n",
       "0                   Urbana      18720  \n",
       "1                    Rural       6024  \n",
       "2                   Urbana       1755  \n",
       "3                    Rural        580  \n",
       "4                   Urbana        643  \n",
       "5                    Rural        162  \n",
       "6                   Urbana      26827  \n",
       "7                    Rural       9230  \n",
       "8                   Urbana        116  \n",
       "9                    Rural        793  \n",
       "10                  Urbana          4  \n",
       "11                   Rural          -  \n",
       "12                  Urbana      16735  \n",
       "13                   Rural       6252  \n",
       "14                  Urbana       2665  \n",
       "15                   Rural        938  \n",
       "16                  Urbana        790  \n",
       "17                   Rural        186  \n",
       "18                  Urbana      30783  \n",
       "19                   Rural      11985  \n",
       "20                  Urbana        124  \n",
       "21                   Rural        753  \n",
       "22                  Urbana          5  \n",
       "23                   Rural          -  \n",
       "24                  Urbana      17809  \n",
       "25                   Rural       7160  \n",
       "26                  Urbana       3312  \n",
       "27                   Rural       1171  \n",
       "28                  Urbana        777  \n",
       "29                   Rural        195  \n",
       "...                    ...        ...  \n",
       "9042                Urbana      21693  \n",
       "9043                 Rural        635  \n",
       "9044                Urbana        195  \n",
       "9045                 Rural          1  \n",
       "9046                Urbana          2  \n",
       "9047                 Rural          -  \n",
       "9048                Urbana      33083  \n",
       "9049                 Rural        744  \n",
       "9050                Urbana       4501  \n",
       "9051                 Rural        102  \n",
       "9052                Urbana        905  \n",
       "9053                 Rural         72  \n",
       "9054                Urbana      25653  \n",
       "9055                 Rural        704  \n",
       "9056                Urbana        222  \n",
       "9057                 Rural          4  \n",
       "9058                Urbana          3  \n",
       "9059                 Rural          -  \n",
       "9060                Urbana      25954  \n",
       "9061                 Rural        455  \n",
       "9062                Urbana       3037  \n",
       "9063                 Rural         60  \n",
       "9064                Urbana        627  \n",
       "9065                 Rural         71  \n",
       "9066                Urbana      15849  \n",
       "9067                 Rural        437  \n",
       "9068                Urbana        139  \n",
       "9069                 Rural          4  \n",
       "9070                Urbana          4  \n",
       "9071                 Rural          1  \n",
       "\n",
       "[9072 rows x 7 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populacao = populacao[:9072]\n",
    "populacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unidade da Federação', 'Sexo', 'Idade', 'Ano', 'Cor ou raça',\n",
       "       'Situação do domicílio', 'Unnamed: 6'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populacao.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estado</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th>Situação do domicílio</th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>18720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>6024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>1755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Estado    Sexo       Idade     Ano Cor ou raça Situação do domicílio  \\\n",
       "0  Rondônia  Homens  0 a 4 anos  2010.0      Branca                Urbana   \n",
       "1  Rondônia  Homens  0 a 4 anos  2010.0      Branca                 Rural   \n",
       "2  Rondônia  Homens  0 a 4 anos  2010.0       Preta                Urbana   \n",
       "3  Rondônia  Homens  0 a 4 anos  2010.0       Preta                 Rural   \n",
       "4  Rondônia  Homens  0 a 4 anos  2010.0     Amarela                Urbana   \n",
       "\n",
       "  Quantidade  \n",
       "0      18720  \n",
       "1       6024  \n",
       "2       1755  \n",
       "3        580  \n",
       "4        643  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populacao = populacao.rename(columns={'Unnamed: 6': \"Quantidade\", \n",
    "                                      'Unidade da Federação': \"Estado\"})\n",
    "populacao.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conteúdo sobre [Características Étnico-Raciais da População](https://biblioteca.ibge.gov.br/visualizacao/livros/liv63405.pdf) desenvolvido pelo IBGE.\n",
    "Ainda sobre [indicadores sociais](https://www.ibge.gov.br/estatisticas-novoportal/sociais.html) existe uma página dedicada dentro do site do IBGE a dispor conteúdo sobre esse tema.\n",
    "\n",
    "`populacao = populacao.replace(['Preta', 'Parda'], 'Negra')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estado</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th>Situação do domicílio</th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
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       "      <td>Branca</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>18720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>6024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>1755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Estado    Sexo       Idade     Ano Cor ou raça Situação do domicílio  \\\n",
       "0  Rondônia  Homens  0 a 4 anos  2010.0      Branca                Urbana   \n",
       "1  Rondônia  Homens  0 a 4 anos  2010.0      Branca                 Rural   \n",
       "2  Rondônia  Homens  0 a 4 anos  2010.0       Preta                Urbana   \n",
       "3  Rondônia  Homens  0 a 4 anos  2010.0       Preta                 Rural   \n",
       "4  Rondônia  Homens  0 a 4 anos  2010.0     Amarela                Urbana   \n",
       "\n",
       "  Quantidade  \n",
       "0      18720  \n",
       "1       6024  \n",
       "2       1755  \n",
       "3        580  \n",
       "4        643  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populacao = populacao.replace('-', 0)\n",
    "populacao.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estado                    object\n",
       "Sexo                      object\n",
       "Idade                     object\n",
       "Ano                      float64\n",
       "Cor ou raça               object\n",
       "Situação do domicílio     object\n",
       "Quantidade                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populacao.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estado                   object\n",
       "Sexo                     object\n",
       "Idade                    object\n",
       "Ano                      object\n",
       "Cor ou raça              object\n",
       "Situação do domicílio    object\n",
       "Quantidade                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populacao['Quantidade'] = populacao['Quantidade'].astype(int)\n",
    "populacao['Ano'] = populacao['Ano'].astype(object)\n",
    "populacao.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estado</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Ano</th>\n",
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       "      <th>Situação do domicílio</th>\n",
       "      <th>Quantidade</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
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       "      <td>Branca</td>\n",
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       "      <td>18720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>6024</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>1755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010</td>\n",
       "      <td>Preta</td>\n",
       "      <td>Rural</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>Homens</td>\n",
       "      <td>0 a 4 anos</td>\n",
       "      <td>2010</td>\n",
       "      <td>Amarela</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Estado    Sexo       Idade   Ano Cor ou raça Situação do domicílio  \\\n",
       "0  Rondônia  Homens  0 a 4 anos  2010      Branca                Urbana   \n",
       "1  Rondônia  Homens  0 a 4 anos  2010      Branca                 Rural   \n",
       "2  Rondônia  Homens  0 a 4 anos  2010       Preta                Urbana   \n",
       "3  Rondônia  Homens  0 a 4 anos  2010       Preta                 Rural   \n",
       "4  Rondônia  Homens  0 a 4 anos  2010     Amarela                Urbana   \n",
       "\n",
       "   Quantidade  \n",
       "0       18720  \n",
       "1        6024  \n",
       "2        1755  \n",
       "3         580  \n",
       "4         643  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populacao.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agora que organizamos os dados, vamos brincar!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Qual o perfil da população brasileira quanto a gênero, idade e cor ou raça?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupando por sexo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>93406990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulheres</th>\n",
       "      <td>97348809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Quantidade\n",
       "Sexo                \n",
       "Homens      93406990\n",
       "Mulheres    97348809"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genero = populacao.groupby('Sexo').sum()\n",
    "genero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colocando os valores em porcentagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_e9ae08c0_d73c_11e8_a9b9_988389088fa3\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Quantidade</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Sexo</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_e9ae08c0_d73c_11e8_a9b9_988389088fa3level0_row0\" class=\"row_heading level0 row0\" >Homens</th> \n",
       "        <td id=\"T_e9ae08c0_d73c_11e8_a9b9_988389088fa3row0_col0\" class=\"data row0 col0\" >48.97%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_e9ae08c0_d73c_11e8_a9b9_988389088fa3level0_row1\" class=\"row_heading level0 row1\" >Mulheres</th> \n",
       "        <td id=\"T_e9ae08c0_d73c_11e8_a9b9_988389088fa3row1_col0\" class=\"data row1 col0\" >51.03%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f25e5a9df60>"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genero_porcentagem = genero['Quantidade']/sum(genero['Quantidade'])*100\n",
    "\n",
    "genero_porcentagem = pd.DataFrame(genero_porcentagem)\n",
    "\n",
    "genero_porcentagem.style.format({\n",
    "    'Quantidade': '{:.2f}%'.format\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificar estilos disponíveis para os gráficos no matplotlib\n",
    "É possível como eles se parecem [nesse link](https://matplotlib.org/devdocs/gallery/style_sheets/style_sheets_reference.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_classic_test',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-pastel',\n",
       " 'fast',\n",
       " 'seaborn-dark',\n",
       " 'Solarize_Light2',\n",
       " 'bmh',\n",
       " 'grayscale',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-muted',\n",
       " 'seaborn',\n",
       " 'ggplot',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-colorblind',\n",
       " 'dark_background',\n",
       " 'classic',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn-darkgrid',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-white',\n",
       " 'seaborn-poster']"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecionar um estilo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "Exemplos de [gráficos com matplotlib](https://matplotlib.org/devdocs/gallery/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criando um gráfico com a distribuição por gênero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribuição da população por gênero')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pbdu2rfTzPXbsGFavXo3jx4/jxo0bWseOs7Oz1YF38uRJANB5D4aFhQW6dOmCHTt24NSp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\ndlKBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e5a9f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (5, 5)\n",
    "genero_porcentagem.plot(kind='bar', legend=None)\n",
    "plt.title('Distribuição da população por gênero')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupando por idade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Quantidade</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Idade</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row0\" class=\"row_heading level0 row0\" >20 a 24 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row0_col0\" class=\"data row0 col0\" >9.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row1\" class=\"row_heading level0 row1\" >10 a 14 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row1_col0\" class=\"data row1 col0\" >9.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row2\" class=\"row_heading level0 row2\" >25 a 29 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row2_col0\" class=\"data row2 col0\" >8.97%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row3\" class=\"row_heading level0 row3\" >15 a 19 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row3_col0\" class=\"data row3 col0\" >8.91%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row4\" class=\"row_heading level0 row4\" >30 a 34 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row4_col0\" class=\"data row4 col0\" >8.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row5\" class=\"row_heading level0 row5\" >5 a 9 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row5_col0\" class=\"data row5 col0\" >7.85%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row6\" class=\"row_heading level0 row6\" >35 a 39 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row6_col0\" class=\"data row6 col0\" >7.28%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row7\" class=\"row_heading level0 row7\" >0 a 4 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row7_col0\" class=\"data row7 col0\" >7.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row8\" class=\"row_heading level0 row8\" >40 a 44 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row8_col0\" class=\"data row8 col0\" >6.82%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row9\" class=\"row_heading level0 row9\" >45 a 49 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row9_col0\" class=\"data row9 col0\" >6.20%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row10\" class=\"row_heading level0 row10\" >60 a 69 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row10_col0\" class=\"data row10 col0\" >5.95%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row11\" class=\"row_heading level0 row11\" >50 a 54 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row11_col0\" class=\"data row11 col0\" >5.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row12\" class=\"row_heading level0 row12\" >70 anos ou mais</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row12_col0\" class=\"data row12 col0\" >4.84%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3level0_row13\" class=\"row_heading level0 row13\" >55 a 59 anos</th> \n",
       "        <td id=\"T_edc36ab8_d73c_11e8_a9b9_988389088fa3row13_col0\" class=\"data row13 col0\" >4.34%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f25e5a6bc88>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idade = populacao.groupby('Idade').sum().sort_values('Quantidade', ascending=False)\n",
    "\n",
    "idade = idade['Quantidade']/sum(idade['Quantidade'])*100\n",
    "\n",
    "idade = pd.DataFrame(idade)\n",
    "\n",
    "idade.style.format({\n",
    "    'Quantidade': '{:.2f}%'.format\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupando por cor ou raça"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Branca</th>\n",
       "      <td>91051646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parda</th>\n",
       "      <td>82277333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Preta</th>\n",
       "      <td>14517961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amarela</th>\n",
       "      <td>2084288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indígena</th>\n",
       "      <td>817963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sem declaração</th>\n",
       "      <td>6608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Quantidade\n",
       "Cor ou raça               \n",
       "Branca            91051646\n",
       "Parda             82277333\n",
       "Preta             14517961\n",
       "Amarela            2084288\n",
       "Indígena            817963\n",
       "Sem declaração        6608"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_ou_raca = populacao.groupby('Cor ou raça').sum().sort_values('Quantidade', ascending=False)\n",
    "cor_ou_raca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Branca</th>\n",
       "      <td>47.732046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parda</th>\n",
       "      <td>43.132284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Preta</th>\n",
       "      <td>7.610757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amarela</th>\n",
       "      <td>1.092647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indígena</th>\n",
       "      <td>0.428801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sem declaração</th>\n",
       "      <td>0.003464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Quantidade\n",
       "Cor ou raça               \n",
       "Branca           47.732046\n",
       "Parda            43.132284\n",
       "Preta             7.610757\n",
       "Amarela           1.092647\n",
       "Indígena          0.428801\n",
       "Sem declaração    0.003464"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_ou_raca_porcentagem = cor_ou_raca['Quantidade']/sum(cor_ou_raca['Quantidade'])*100\n",
    "cor_ou_raca_porcentagem = pd.DataFrame(cor_ou_raca_porcentagem)\n",
    "cor_ou_raca_porcentagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25e5a048d0>"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AJSISCAOXiEggDFwiIoEwcImIBMLAJSISCAOXiEggDFwiIoGUG7g//PADXF1dYWFhAQsL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\nL7/I69f3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e59e21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor_ou_raca_porcentagem.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mulheres</th>\n",
       "      <th>Branca</th>\n",
       "      <td>24.848082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <th>Branca</th>\n",
       "      <td>22.883964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulheres</th>\n",
       "      <th>Parda</th>\n",
       "      <td>21.711250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Homens</th>\n",
       "      <th>Parda</th>\n",
       "      <td>21.421034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Preta</th>\n",
       "      <td>3.945679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mulheres</th>\n",
       "      <th>Preta</th>\n",
       "      <td>3.665079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amarela</th>\n",
       "      <td>0.593892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Homens</th>\n",
       "      <th>Amarela</th>\n",
       "      <td>0.498755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indígena</th>\n",
       "      <td>0.214886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulheres</th>\n",
       "      <th>Indígena</th>\n",
       "      <td>0.213915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <th>Sem declaração</th>\n",
       "      <td>0.002472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulheres</th>\n",
       "      <th>Sem declaração</th>\n",
       "      <td>0.000992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Quantidade\n",
       "Sexo     Cor ou raça               \n",
       "Mulheres Branca           24.848082\n",
       "Homens   Branca           22.883964\n",
       "Mulheres Parda            21.711250\n",
       "Homens   Parda            21.421034\n",
       "         Preta             3.945679\n",
       "Mulheres Preta             3.665079\n",
       "         Amarela           0.593892\n",
       "Homens   Amarela           0.498755\n",
       "         Indígena          0.214886\n",
       "Mulheres Indígena          0.213915\n",
       "Homens   Sem declaração    0.002472\n",
       "Mulheres Sem declaração    0.000992"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sexo_cor_ou_raca = populacao.groupby(['Sexo', 'Cor ou raça']).sum().sort_values('Quantidade', ascending=False)\n",
    "sexo_cor_ou_raca = pd.DataFrame(sexo_cor_ou_raca['Quantidade']/sum(sexo_cor_ou_raca['Quantidade'])*100)\n",
    "sexo_cor_ou_raca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Como esse perfil se comporta quando analisamos por estado?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analisando características para o estado da Paraíba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estado</th>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Paraíba</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Parda</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>26.895751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>25.848288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Branca</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>20.980542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>18.824100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Preta</th>\n",
       "      <th>Homens</th>\n",
       "      <td>2.965569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulheres</th>\n",
       "      <td>2.688657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Amarela</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>0.743231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>0.544082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Indígena</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>0.254478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>0.253921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sem declaração</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>0.000717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>0.000664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Quantidade\n",
       "Estado  Cor ou raça    Sexo                \n",
       "Paraíba Parda          Mulheres   26.895751\n",
       "                       Homens     25.848288\n",
       "        Branca         Mulheres   20.980542\n",
       "                       Homens     18.824100\n",
       "        Preta          Homens      2.965569\n",
       "                       Mulheres    2.688657\n",
       "        Amarela        Mulheres    0.743231\n",
       "                       Homens      0.544082\n",
       "        Indígena       Mulheres    0.254478\n",
       "                       Homens      0.253921\n",
       "        Sem declaração Mulheres    0.000717\n",
       "                       Homens      0.000664"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "por_estado = populacao.loc[populacao['Estado']=='Paraíba'].\\\n",
    "                groupby(['Estado', 'Cor ou raça', 'Sexo']).sum().\\\n",
    "                sort_values('Quantidade', ascending=False)\n",
    "        \n",
    "por_estado = pd.DataFrame(por_estado['Quantidade']/sum(por_estado['Quantidade'])*100)\n",
    "\n",
    "por_estado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analisando características para o estado da Bahia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estado</th>\n",
       "      <th>Cor ou raça</th>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Bahia</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Parda</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>30.125885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>29.038791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Branca</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>11.515387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>10.676422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Preta</th>\n",
       "      <th>Homens</th>\n",
       "      <td>8.649962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulheres</th>\n",
       "      <td>8.452593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Amarela</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>0.628798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>0.505012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Indígena</th>\n",
       "      <th>Mulheres</th>\n",
       "      <td>0.204089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homens</th>\n",
       "      <td>0.198146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sem declaração</th>\n",
       "      <th>Homens</th>\n",
       "      <td>0.002882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulheres</th>\n",
       "      <td>0.002033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Quantidade\n",
       "Estado Cor ou raça    Sexo                \n",
       "Bahia  Parda          Mulheres   30.125885\n",
       "                      Homens     29.038791\n",
       "       Branca         Mulheres   11.515387\n",
       "                      Homens     10.676422\n",
       "       Preta          Homens      8.649962\n",
       "                      Mulheres    8.452593\n",
       "       Amarela        Mulheres    0.628798\n",
       "                      Homens      0.505012\n",
       "       Indígena       Mulheres    0.204089\n",
       "                      Homens      0.198146\n",
       "       Sem declaração Homens      0.002882\n",
       "                      Mulheres    0.002033"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "por_estado = populacao.loc[populacao['Estado']=='Bahia'].\\\n",
    "                groupby(['Estado', 'Cor ou raça', 'Sexo']).sum().\\\n",
    "                sort_values('Quantidade', ascending=False)\n",
    "        \n",
    "por_estado = pd.DataFrame(por_estado['Quantidade']/sum(por_estado['Quantidade'])*100)\n",
    "\n",
    "por_estado"
   ]
  }
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