postagem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uma Introdução Simples ao *pandas* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pandas** é uma biblioteca Python que fornece ferramentas de análise de dados e estruturas de dados de alta performance e *fáceis de usar*. Por ser a principal e mais completa biblioteca para estes objetivos, **pandas** é fundamental para análise de dados com Python.\n",
    "\n",
    "Esta introdução foi escrita como uma alternativa em português às introduções já existentes e à [introdução de 10 minutos apresentada na documentação oficial](http://pandas.pydata.org/pandas-docs/stable/10min.html), e tem por objetivo fornecer de forma enxuta e simplificada uma apresentação básica às principais ferramentas fornecidas pelo **pandas**, cobrindo manipulação, leitura e visualização de dados, contudo também farei outros comentários pontuais por julgar necessário, como por exemplo uma breve explicação sobre arquivos *.csv*. A introdução pressupõe apenas conhecimento básico em Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos começar com as importações, usaremos além do pandas, o numpy, biblioteca para computação científica e o matplotlib, biblioteca principal para visualização de dados, entretanto, como veremos mais adiante, o próprio pandas nos fornece facilidades em relação à visualização de dados, com métodos construídos com base no matplotlib, também importamos esta biblioteca para, além de poder modificar esteticamente nossos gráficos, facilitar a exibição dos gráficos.\n",
    "A linha `%matplotlib inline` faz parte da mágica do Jupyter e você não deve rodá-la caso esteja em outra IDE/Ambiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existem dois tipos principais de estruturas de dados no pandas:\n",
    " 1. **Series** \n",
    " 1. **DataFrame**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma Series é como um array unidimensional, uma lista de valores. Toda Series possui um índice, o `index`, que dá rótulos a cada elemento da lista. Abaixo criamos uma Series `notas`, o `index` desta Series é a coluna à esquerda, que vai de 0 a 4 neste caso, que o pandas criou automaticamente, já que não especificamos uma lista de rótulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     7\n",
       "2     5\n",
       "3    10\n",
       "4     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas = pd.Series([2,7,5,10,6])\n",
    "notas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Já podemos aqui verificar os atributos da nossa Series, comecemos pelos valores e o índice, os dois atributos *fundamentais* nesta estrutura:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  7,  5, 10,  6])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como ao criar a Series não demos um índice específico o pandas usou os inteiros positivos crescentes como padrão. Pode ser conveniente atribuirmos um índice diferente do padrão, supondo que essas sejam notas de uma turma, poderíamos atribuir nomes ao index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wilfred     2\n",
       "Abbie       7\n",
       "Harry       5\n",
       "Julia      10\n",
       "Carrie      6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas = pd.Series([2,7,5,10,6], index=[\"Wilfred\", \"Abbie\", \"Harry\", \"Julia\", \"Carrie\"])\n",
    "notas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O index nos ajuda para referenciar um determinado valor, ele nos permite acessar os valores pelo seu rótulo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas[\"Julia\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outra facilidade proporcionada pela estrutura são seus métodos que fornecem informações estatísticas sobre os valores, como **média** `.mean()` e **desvio padrão** `.std()`. Encorajo o leitor(a) a investigar e verificar alguns dos métodos e atributos da estrutura usando o `TAB` para auto-completação na shell do Python, ou simplesmente checar a completíssima [documentação oficial](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html#pandas.Series) deste objeto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média: 6.0\n",
      "Desvio padrão: 2.9154759474226504\n"
     ]
    }
   ],
   "source": [
    "print(\"Média:\", notas.mean())\n",
    "print(\"Desvio padrão:\", notas.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geralmente para resumir brevemente as estatísticas dos dados se usa o `.describe()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5.000000\n",
       "mean      6.000000\n",
       "std       2.915476\n",
       "min       2.000000\n",
       "25%       5.000000\n",
       "50%       6.000000\n",
       "75%       7.000000\n",
       "max      10.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A estrutura é flexível o suficiente pra aplicarmos algumas expressões matemáticas e funções matemáticas do numpy diretamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wilfred      4\n",
       "Abbie       49\n",
       "Harry       25\n",
       "Julia      100\n",
       "Carrie      36\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wilfred    0.693147\n",
       "Abbie      1.945910\n",
       "Harry      1.609438\n",
       "Julia      2.302585\n",
       "Carrie     1.791759\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(notas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Já um DataFrame é uma estrutura bidimensional de dados, como uma planilha. Abaixo criaremos um DataFrame que possui valores de diferentes tipos, usando um dicionário como entrada dos dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aluno</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Prova</th>\n",
       "      <th>Seminário</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wilfred</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbie</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julia</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Aluno  Faltas  Prova  Seminário\n",
       "0  Wilfred       3      2        8.5\n",
       "1    Abbie       4      7        7.5\n",
       "2    Harry       2      5        9.0\n",
       "3    Julia       1     10        7.5\n",
       "4   Carrie       4      6        8.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Aluno' : [\"Wilfred\", \"Abbie\", \"Harry\", \"Julia\", \"Carrie\"],\n",
    "                   'Faltas' : [3,4,2,1,4],\n",
    "                   'Prova' : [2,7,5,10,6],\n",
    "                   'Seminário': [8.5,7.5,9.0,7.5,8.0]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os tipos de dados que compõe as colunas podem ser verificados por um método próprio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aluno         object\n",
       "Faltas         int64\n",
       "Prova          int64\n",
       "Seminário    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É possível acessar a lista de colunas de forma bem intuitiva:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Aluno', 'Faltas', 'Prova', 'Seminário'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os nomes das colunas podem ser usadas pra acessar seus valores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8.5\n",
       "1    7.5\n",
       "2    9.0\n",
       "3    7.5\n",
       "4    8.0\n",
       "Name: Seminário, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Seminário\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para DataFrames, `.describe()` também é uma boa forma de verificar resumidamente a disposição estatística dos dados numéricos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Prova</th>\n",
       "      <th>Seminário</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.80000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.30384</td>\n",
       "      <td>2.915476</td>\n",
       "      <td>0.65192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Faltas      Prova  Seminário\n",
       "count  5.00000   5.000000    5.00000\n",
       "mean   2.80000   6.000000    8.10000\n",
       "std    1.30384   2.915476    0.65192\n",
       "min    1.00000   2.000000    7.50000\n",
       "25%    2.00000   5.000000    7.50000\n",
       "50%    3.00000   6.000000    8.00000\n",
       "75%    4.00000   7.000000    8.50000\n",
       "max    4.00000  10.000000    9.00000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outra tarefa comum aplicada em DataFrames é ordená-los por determinada coluna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aluno</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Prova</th>\n",
       "      <th>Seminário</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbie</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julia</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wilfred</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Aluno  Faltas  Prova  Seminário\n",
       "1    Abbie       4      7        7.5\n",
       "3    Julia       1     10        7.5\n",
       "4   Carrie       4      6        8.0\n",
       "0  Wilfred       3      2        8.5\n",
       "2    Harry       2      5        9.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"Seminário\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note que simplesmente usar o método `sort_values` não modifica o nosso DataFrame original:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aluno</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Prova</th>\n",
       "      <th>Seminário</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wilfred</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbie</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julia</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Aluno  Faltas  Prova  Seminário\n",
       "0  Wilfred       3      2        8.5\n",
       "1    Abbie       4      7        7.5\n",
       "2    Harry       2      5        9.0\n",
       "3    Julia       1     10        7.5\n",
       "4   Carrie       4      6        8.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Muitas vezes é necessário selecionarmos valores específicos de um DataFrame, seja uma linha ou uma célula específica, e isso pode ser feito de diversas formas. A documentação oficial contém [vasta informação](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing) para esse tipo de tarefa, aqui nos concentraremos nas formas mais comuns de selecionarmos dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para selecionar pelo index ou rótulo usamos o atributo `.loc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aluno        Julia\n",
       "Faltas           1\n",
       "Prova           10\n",
       "Seminário      7.5\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para selecionar de acordo com critérios condicionais, se usa o que se chama de **Boolean Indexing**.\n",
    "\n",
    "Suponha que queiramos selecionar apenas as linhas em que o valor da coluna *Seminário* seja acima de 8.0, podemos realizar esta tarefa passando a condição diretamente como índice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aluno</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Prova</th>\n",
       "      <th>Seminário</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wilfred</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Aluno  Faltas  Prova  Seminário\n",
       "0  Wilfred       3      2        8.5\n",
       "2    Harry       2      5        9.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Seminário\"] > 8.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este tipo de indexação também possibilita checar condições de múltiplas colunas. Diferentemente do que estamos habituados em Python, aqui se usam operadores bitwise, ou seja, `&`, `|`, `~` ao invés de `and`, `or`, `not`, respectivamente. Suponha que além de `df[\"Seminário\"] > 8.0` queiramos que o valor da coluna `Prova` não seja menor que 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aluno</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Prova</th>\n",
       "      <th>Seminário</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aluno  Faltas  Prova  Seminário\n",
       "2  Harry       2      5        9.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Seminário\"] > 8.0) & (df[\"Prova\"] > 3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por enquanto é isso para manipulação de Series e DataFrames, conforme a seção de leitura de dados for se estendendo irei aprensentar alguns outros métodos dessas estruturas que poderão ser interessantes no contexto.\n",
    "Na seção anterior vimos como manipular dados que foram criados durante esta apresentação, acontece que, na maioria das vezes, queremos analisar dados que já estão prontos.\n",
    "O pandas nos fornece uma série de funcionalidades de leitura de dados, pros mais diversos formatos estruturais de dados, experimente a auto-completação de `pd.read_<TAB>`, entre eles estão:\n",
    " 1. `pd.read_csv`, para ler arquivos .csv, formato comum de armazenar dados de tabelas\n",
    " 1. `pd.read_xlsx`, para ler arquivos Excel .xlsx, é necessário instalar uma biblioteca adicional pra esta funcionalidade.\n",
    " 1. `pd.read_html`, para ler tabelas diretamente de um website\n",
    " \n",
    "Usaremos para analisar dados externos nesta introdução o `.read_csv`, pois é neste formato que se encontram nossos dados. CSV, ou comma-separated values é um formato muito comum de dados abertos, trata-se, como a sigla sugere, de valores divididos por vírgula, apesar de o caracter separador poder ser o ponto-e-vírgula ou outro.\n",
    "\n",
    "O arquivo `dados.csv` está na mesma pasta do nosso script, então podemos passar como argumento do `.read_csv` apenas o seu nome. Outro argumento interessante da função é o `sep`, que por padrão é a vírgula, mas que pode ser definido como outro caractere caso seu dado esteja usando outro separador.\n",
    "\n",
    "Estes dados que usaremos como exemplo são dados sobre preços de apartamentos em 7 bairros da cidade do Rio de Janeiro: Botafogo, Copacabana, Gávea, Grajaú, Ipanema, Leblon, Tijuca. São dados adaptados de um arquivo que pode ser encontrado [aqui](https://www.kaggle.com/rbarbera/rio-brasil-modelo-preditivo-precos-imoveis/data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condominio</th>\n",
       "      <th>quartos</th>\n",
       "      <th>suites</th>\n",
       "      <th>vagas</th>\n",
       "      <th>area</th>\n",
       "      <th>bairro</th>\n",
       "      <th>preco</th>\n",
       "      <th>pm2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>340000</td>\n",
       "      <td>16190.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>770000</td>\n",
       "      <td>12031.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>674</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>600000</td>\n",
       "      <td>9836.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>700000</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>515000</td>\n",
       "      <td>11704.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>917</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>630000</td>\n",
       "      <td>10500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>740000</td>\n",
       "      <td>11384.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>570000</td>\n",
       "      <td>13255.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>430000</td>\n",
       "      <td>16538.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>510</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>500000</td>\n",
       "      <td>11904.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>500000</td>\n",
       "      <td>14285.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>552</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>790000</td>\n",
       "      <td>11791.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>495</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>515000</td>\n",
       "      <td>9537.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>340</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>410000</td>\n",
       "      <td>10250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>625000</td>\n",
       "      <td>10416.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>530</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>360000</td>\n",
       "      <td>9000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>670000</td>\n",
       "      <td>14255.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>500000</td>\n",
       "      <td>11111.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>465</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>570000</td>\n",
       "      <td>11400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1400</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>570000</td>\n",
       "      <td>11400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>630000</td>\n",
       "      <td>10862.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>400000</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>750000</td>\n",
       "      <td>13888.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>578000</td>\n",
       "      <td>8892.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>481</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>350000</td>\n",
       "      <td>12500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>130000</td>\n",
       "      <td>3250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1071</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>597000</td>\n",
       "      <td>13568.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>750</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>995000</td>\n",
       "      <td>14214.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>992</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>1150000</td>\n",
       "      <td>10952.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>990</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>1200000</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>600</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>780000</td>\n",
       "      <td>7878.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>780</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>780000</td>\n",
       "      <td>6500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>1068</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>795000</td>\n",
       "      <td>8368.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>680000</td>\n",
       "      <td>7391.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>480</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>142</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>600000</td>\n",
       "      <td>4225.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>588</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>1100000</td>\n",
       "      <td>11224.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>210</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>440000</td>\n",
       "      <td>4631.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>1370</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>1050000</td>\n",
       "      <td>8076.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>955000</td>\n",
       "      <td>9362.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>670</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>878000</td>\n",
       "      <td>6316.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>730</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>550000</td>\n",
       "      <td>6111.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>125</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>825000</td>\n",
       "      <td>6600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>1050</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>95</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>899000</td>\n",
       "      <td>9463.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>1100</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>850000</td>\n",
       "      <td>7727.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>850</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>118</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>850000</td>\n",
       "      <td>7203.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>1100</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>750000</td>\n",
       "      <td>6818.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>950</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>730000</td>\n",
       "      <td>6952.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>970</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>650000</td>\n",
       "      <td>5909.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>1336</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>850000</td>\n",
       "      <td>7083.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>860000</td>\n",
       "      <td>9555.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>880</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>800000</td>\n",
       "      <td>8000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>870</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>580000</td>\n",
       "      <td>5800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>480000</td>\n",
       "      <td>5217.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>686</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>490000</td>\n",
       "      <td>4666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>450</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>435000</td>\n",
       "      <td>6041.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1080</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>680000</td>\n",
       "      <td>8500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>650000</td>\n",
       "      <td>7926.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>700</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>629900</td>\n",
       "      <td>6299.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1850</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>166</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>1600000</td>\n",
       "      <td>9638.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>800</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>540000</td>\n",
       "      <td>5046.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1997 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      condominio  quartos  suites  vagas  area    bairro    preco       pm2\n",
       "0            350        1     0.0    1.0    21  Botafogo   340000  16190.48\n",
       "1            800        1     0.0    1.0    64  Botafogo   770000  12031.25\n",
       "2            674        1     0.0    1.0    61  Botafogo   600000   9836.07\n",
       "3            700        1     1.0    1.0    70  Botafogo   700000  10000.00\n",
       "4            440        1     0.0    1.0    44  Botafogo   515000  11704.55\n",
       "5            917        1     1.0    1.0    60  Botafogo   630000  10500.00\n",
       "6            850        1     1.0    1.0    65  Botafogo   740000  11384.62\n",
       "7            350        1     1.0    1.0    43  Botafogo   570000  13255.81\n",
       "8            440        1     1.0    1.0    26  Botafogo   430000  16538.46\n",
       "9            510        1     1.0    1.0    42  Botafogo   500000  11904.76\n",
       "10           200        1     0.0    1.0    35  Botafogo   500000  14285.71\n",
       "11           552        1     1.0    1.0    67  Botafogo   790000  11791.04\n",
       "12           495        1     1.0    1.0    54  Botafogo   515000   9537.04\n",
       "13           340        1     1.0    1.0    40  Botafogo   410000  10250.00\n",
       "14           800        1     1.0    1.0    60  Botafogo   625000  10416.67\n",
       "15           530        1     0.0    1.0    40  Botafogo   360000   9000.00\n",
       "16           500        1     0.0    1.0    47  Botafogo   670000  14255.32\n",
       "17           600        1     1.0    1.0    45  Botafogo   500000  11111.11\n",
       "18           465        1     0.0    1.0    50  Botafogo   570000  11400.00\n",
       "19          1400        1     1.0    1.0    50  Botafogo   570000  11400.00\n",
       "20           700        1     1.0    1.0    58  Botafogo   630000  10862.07\n",
       "21           250        1     0.0    1.0    40  Botafogo   400000  10000.00\n",
       "22          1000        1     1.0    1.0    54  Botafogo   750000  13888.89\n",
       "23           280        1     0.0    1.0    65  Botafogo   578000   8892.31\n",
       "24           481        1     1.0    1.0    28  Botafogo   350000  12500.00\n",
       "25           150        1     0.0    1.0    40  Botafogo   130000   3250.00\n",
       "26          1071        1     1.0    1.0    44  Botafogo   597000  13568.18\n",
       "27           750        2     1.0    1.0    70  Botafogo   995000  14214.29\n",
       "28           992        2     1.0    1.0   105  Botafogo  1150000  10952.38\n",
       "29           990        2     1.0    1.0   100  Botafogo  1200000  12000.00\n",
       "...          ...      ...     ...    ...   ...       ...      ...       ...\n",
       "1967         600        3     1.0    1.0    99    Tijuca   780000   7878.79\n",
       "1968         780        3     1.0    1.0   120    Tijuca   780000   6500.00\n",
       "1969        1068        3     1.0    1.0    95    Tijuca   795000   8368.42\n",
       "1970         900        3     1.0    1.0    92    Tijuca   680000   7391.30\n",
       "1971         480        3     1.0    3.0   142    Tijuca   600000   4225.35\n",
       "1972         588        3     1.0    1.0    98    Tijuca  1100000  11224.49\n",
       "1973         210        3     1.0    1.0    95    Tijuca   440000   4631.58\n",
       "1974        1370        3     1.0    2.0   130    Tijuca  1050000   8076.92\n",
       "1975         500        3     1.0    1.0   102    Tijuca   955000   9362.75\n",
       "1976         670        3     2.0    1.0   139    Tijuca   878000   6316.55\n",
       "1977         730        3     0.0    1.0    90    Tijuca   550000   6111.11\n",
       "1978        1000        3     1.0    2.0   125    Tijuca   825000   6600.00\n",
       "1979        1050        3     1.0    2.0    95    Tijuca   899000   9463.16\n",
       "1980        1100        3     1.0    2.0   110    Tijuca   850000   7727.27\n",
       "1981         850        3     1.0    2.0   118    Tijuca   850000   7203.39\n",
       "1982        1100        3     1.0    1.0   110    Tijuca   750000   6818.18\n",
       "1983         950        3     1.0    1.0   105    Tijuca   730000   6952.38\n",
       "1984         970        3     1.0    1.0   110    Tijuca   650000   5909.09\n",
       "1985        1336        3     2.0    1.0   120    Tijuca   850000   7083.33\n",
       "1986         900        3     1.0    1.0    90    Tijuca   860000   9555.56\n",
       "1987         880        3     1.0    1.0   100    Tijuca   800000   8000.00\n",
       "1988         870        3     1.0    1.0   100    Tijuca   580000   5800.00\n",
       "1989         400        3     1.0    1.0    92    Tijuca   480000   5217.39\n",
       "1990         686        3     0.0    1.0   105    Tijuca   490000   4666.67\n",
       "1991         450        3     1.0    1.0    72    Tijuca   435000   6041.67\n",
       "1992        1080        3     1.0    1.0    80    Tijuca   680000   8500.00\n",
       "1993         750        3     0.0    1.0    82    Tijuca   650000   7926.83\n",
       "1994         700        3     1.0    1.0   100    Tijuca   629900   6299.00\n",
       "1995        1850        3     1.0    2.0   166    Tijuca  1600000   9638.55\n",
       "1996         800        3     1.0    1.0   107    Tijuca   540000   5046.73\n",
       "\n",
       "[1997 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dados.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como esperado, o DataFrame tem muitas linhas de dados, pra visualizar sucintamente as primeiras linhas de um DataFrame existe o método `.head()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>condominio</th>\n",
       "      <th>quartos</th>\n",
       "      <th>suites</th>\n",
       "      <th>vagas</th>\n",
       "      <th>area</th>\n",
       "      <th>bairro</th>\n",
       "      <th>preco</th>\n",
       "      <th>pm2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>340000</td>\n",
       "      <td>16190.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>770000</td>\n",
       "      <td>12031.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>674</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>600000</td>\n",
       "      <td>9836.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>700000</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>515000</td>\n",
       "      <td>11704.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condominio  quartos  suites  vagas  area    bairro   preco       pm2\n",
       "0         350        1     0.0    1.0    21  Botafogo  340000  16190.48\n",
       "1         800        1     0.0    1.0    64  Botafogo  770000  12031.25\n",
       "2         674        1     0.0    1.0    61  Botafogo  600000   9836.07\n",
       "3         700        1     1.0    1.0    70  Botafogo  700000  10000.00\n",
       "4         440        1     0.0    1.0    44  Botafogo  515000  11704.55"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por padrão `.head()` exibe as 5 primeiras linhas, mas isso pode ser alterado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condominio</th>\n",
       "      <th>quartos</th>\n",
       "      <th>suites</th>\n",
       "      <th>vagas</th>\n",
       "      <th>area</th>\n",
       "      <th>bairro</th>\n",
       "      <th>preco</th>\n",
       "      <th>pm2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>340000</td>\n",
       "      <td>16190.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>770000</td>\n",
       "      <td>12031.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>674</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>600000</td>\n",
       "      <td>9836.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>700000</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>515000</td>\n",
       "      <td>11704.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>917</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>630000</td>\n",
       "      <td>10500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>740000</td>\n",
       "      <td>11384.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>570000</td>\n",
       "      <td>13255.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>430000</td>\n",
       "      <td>16538.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>510</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>500000</td>\n",
       "      <td>11904.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condominio  quartos  suites  vagas  area    bairro   preco       pm2\n",
       "0         350        1     0.0    1.0    21  Botafogo  340000  16190.48\n",
       "1         800        1     0.0    1.0    64  Botafogo  770000  12031.25\n",
       "2         674        1     0.0    1.0    61  Botafogo  600000   9836.07\n",
       "3         700        1     1.0    1.0    70  Botafogo  700000  10000.00\n",
       "4         440        1     0.0    1.0    44  Botafogo  515000  11704.55\n",
       "5         917        1     1.0    1.0    60  Botafogo  630000  10500.00\n",
       "6         850        1     1.0    1.0    65  Botafogo  740000  11384.62\n",
       "7         350        1     1.0    1.0    43  Botafogo  570000  13255.81\n",
       "8         440        1     1.0    1.0    26  Botafogo  430000  16538.46\n",
       "9         510        1     1.0    1.0    42  Botafogo  500000  11904.76"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarmente existe o `.tail()`, que exibe por padrão as últimas 5 linhas do DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condominio</th>\n",
       "      <th>quartos</th>\n",
       "      <th>suites</th>\n",
       "      <th>vagas</th>\n",
       "      <th>area</th>\n",
       "      <th>bairro</th>\n",
       "      <th>preco</th>\n",
       "      <th>pm2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1080</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>680000</td>\n",
       "      <td>8500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>650000</td>\n",
       "      <td>7926.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>700</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>629900</td>\n",
       "      <td>6299.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1850</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>166</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>1600000</td>\n",
       "      <td>9638.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>800</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107</td>\n",
       "      <td>Tijuca</td>\n",
       "      <td>540000</td>\n",
       "      <td>5046.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      condominio  quartos  suites  vagas  area  bairro    preco      pm2\n",
       "1992        1080        3     1.0    1.0    80  Tijuca   680000  8500.00\n",
       "1993         750        3     0.0    1.0    82  Tijuca   650000  7926.83\n",
       "1994         700        3     1.0    1.0   100  Tijuca   629900  6299.00\n",
       "1995        1850        3     1.0    2.0   166  Tijuca  1600000  9638.55\n",
       "1996         800        3     1.0    1.0   107  Tijuca   540000  5046.73"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Além de confiar em mim, quando mencionei os bairros que continham no nosso conjunto de dados, você pode verificar a informação usando um método que lista os valores únicos numa coluna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Botafogo', 'Copacabana', 'Gávea', 'Grajaú', 'Ipanema', 'Leblon',\n",
       "       'Tijuca'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bairro\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Também parece interessante verificarmos a hegemoneidade da nossa amostra em relação aos bairros. Pra tarefas de contar valores podemos sempre aproveitar de outro método disponível, o `.value_counts()`, também veremos um pouco mais abaixo como visualizar estes valores em forma de gráfico de barras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Copacabana    346\n",
       "Tijuca        341\n",
       "Botafogo      307\n",
       "Ipanema       281\n",
       "Leblon        280\n",
       "Grajaú        237\n",
       "Gávea         205\n",
       "Name: bairro, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bairro\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os valores contados também podem ser normalizados para expressar porcentagens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Copacabana    0.173260\n",
       "Tijuca        0.170756\n",
       "Botafogo      0.153731\n",
       "Ipanema       0.140711\n",
       "Leblon        0.140210\n",
       "Grajaú        0.118678\n",
       "Gávea         0.102654\n",
       "Name: bairro, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bairro\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupar os dados se baseando em certos critérios é outro processo que o pandas facilita bastante com o `.groupby()`.\n",
    "Esse método pode ser usado para resolver os mais **amplos** dos problemas, aqui abordarei apenas o agrupamento simples, a divisão de um DataFrame em grupos.\n",
    "\n",
    "Abaixo agrupamos o nosso DataFrame pelos valores da coluna `\"bairro\"`, e em seguida aplicamos o `.mean()` para termos um objeto GroupBy com informação das médias agrupadas pelos valores da coluna bairros. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condominio</th>\n",
       "      <th>quartos</th>\n",
       "      <th>suites</th>\n",
       "      <th>vagas</th>\n",
       "      <th>area</th>\n",
       "      <th>preco</th>\n",
       "      <th>pm2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bairro</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Botafogo</th>\n",
       "      <td>914.475570</td>\n",
       "      <td>2.107492</td>\n",
       "      <td>1.048860</td>\n",
       "      <td>1.159609</td>\n",
       "      <td>83.837134</td>\n",
       "      <td>1.010614e+06</td>\n",
       "      <td>12034.486189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copacabana</th>\n",
       "      <td>991.861272</td>\n",
       "      <td>2.101156</td>\n",
       "      <td>1.034682</td>\n",
       "      <td>1.080925</td>\n",
       "      <td>101.855491</td>\n",
       "      <td>1.216344e+06</td>\n",
       "      <td>11965.298699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grajaú</th>\n",
       "      <td>619.940928</td>\n",
       "      <td>2.097046</td>\n",
       "      <td>0.970464</td>\n",
       "      <td>1.130802</td>\n",
       "      <td>79.949367</td>\n",
       "      <td>4.788869e+05</td>\n",
       "      <td>6145.624473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gávea</th>\n",
       "      <td>985.234146</td>\n",
       "      <td>2.058537</td>\n",
       "      <td>1.029268</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>88.497561</td>\n",
       "      <td>1.454571e+06</td>\n",
       "      <td>16511.582780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ipanema</th>\n",
       "      <td>1357.120996</td>\n",
       "      <td>2.181495</td>\n",
       "      <td>1.192171</td>\n",
       "      <td>1.220641</td>\n",
       "      <td>100.615658</td>\n",
       "      <td>2.033096e+06</td>\n",
       "      <td>19738.407794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leblon</th>\n",
       "      <td>1260.010714</td>\n",
       "      <td>2.207143</td>\n",
       "      <td>1.064286</td>\n",
       "      <td>1.164286</td>\n",
       "      <td>91.832143</td>\n",
       "      <td>1.946193e+06</td>\n",
       "      <td>20761.351036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tijuca</th>\n",
       "      <td>681.175953</td>\n",
       "      <td>2.131965</td>\n",
       "      <td>0.944282</td>\n",
       "      <td>1.143695</td>\n",
       "      <td>81.457478</td>\n",
       "      <td>5.750780e+05</td>\n",
       "      <td>7149.804985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             condominio   quartos    suites     vagas        area  \\\n",
       "bairro                                                              \n",
       "Botafogo     914.475570  2.107492  1.048860  1.159609   83.837134   \n",
       "Copacabana   991.861272  2.101156  1.034682  1.080925  101.855491   \n",
       "Grajaú       619.940928  2.097046  0.970464  1.130802   79.949367   \n",
       "Gávea        985.234146  2.058537  1.029268  1.200000   88.497561   \n",
       "Ipanema     1357.120996  2.181495  1.192171  1.220641  100.615658   \n",
       "Leblon      1260.010714  2.207143  1.064286  1.164286   91.832143   \n",
       "Tijuca       681.175953  2.131965  0.944282  1.143695   81.457478   \n",
       "\n",
       "                   preco           pm2  \n",
       "bairro                                  \n",
       "Botafogo    1.010614e+06  12034.486189  \n",
       "Copacabana  1.216344e+06  11965.298699  \n",
       "Grajaú      4.788869e+05   6145.624473  \n",
       "Gávea       1.454571e+06  16511.582780  \n",
       "Ipanema     2.033096e+06  19738.407794  \n",
       "Leblon      1.946193e+06  20761.351036  \n",
       "Tijuca      5.750780e+05   7149.804985  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"bairro\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para extrairmos dados de uma coluna deste objeto basta acessá-lo convencionalmente, para obtermos os valores da média do preço do metro quadrado em ordem crescente, por exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bairro\n",
       "Grajaú         6145.624473\n",
       "Tijuca         7149.804985\n",
       "Copacabana    11965.298699\n",
       "Botafogo      12034.486189\n",
       "Gávea         16511.582780\n",
       "Ipanema       19738.407794\n",
       "Leblon        20761.351036\n",
       "Name: pm2, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"bairro\").mean()[\"pm2\"].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É comum queremos aplicar uma função qualquer aos dados, ou à parte deles, neste caso o pandas fornece o método `.apply`. Por exemplo, para deixar os nomes dos bairros como apenas as suas três primeiras letras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Bot\n",
       "1       Bot\n",
       "2       Bot\n",
       "3       Bot\n",
       "4       Bot\n",
       "5       Bot\n",
       "6       Bot\n",
       "7       Bot\n",
       "8       Bot\n",
       "9       Bot\n",
       "10      Bot\n",
       "11      Bot\n",
       "12      Bot\n",
       "13      Bot\n",
       "14      Bot\n",
       "15      Bot\n",
       "16      Bot\n",
       "17      Bot\n",
       "18      Bot\n",
       "19      Bot\n",
       "20      Bot\n",
       "21      Bot\n",
       "22      Bot\n",
       "23      Bot\n",
       "24      Bot\n",
       "25      Bot\n",
       "26      Bot\n",
       "27      Bot\n",
       "28      Bot\n",
       "29      Bot\n",
       "       ... \n",
       "1967    Tij\n",
       "1968    Tij\n",
       "1969    Tij\n",
       "1970    Tij\n",
       "1971    Tij\n",
       "1972    Tij\n",
       "1973    Tij\n",
       "1974    Tij\n",
       "1975    Tij\n",
       "1976    Tij\n",
       "1977    Tij\n",
       "1978    Tij\n",
       "1979    Tij\n",
       "1980    Tij\n",
       "1981    Tij\n",
       "1982    Tij\n",
       "1983    Tij\n",
       "1984    Tij\n",
       "1985    Tij\n",
       "1986    Tij\n",
       "1987    Tij\n",
       "1988    Tij\n",
       "1989    Tij\n",
       "1990    Tij\n",
       "1991    Tij\n",
       "1992    Tij\n",
       "1993    Tij\n",
       "1994    Tij\n",
       "1995    Tij\n",
       "1996    Tij\n",
       "Name: bairro, Length: 1997, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def truncar(bairro):\n",
    "    return bairro[:3]\n",
    "\n",
    "df[\"bairro\"].apply(truncar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou de um jeito mais prático, usando uma função lambda:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Bot\n",
       "1       Bot\n",
       "2       Bot\n",
       "3       Bot\n",
       "4       Bot\n",
       "5       Bot\n",
       "6       Bot\n",
       "7       Bot\n",
       "8       Bot\n",
       "9       Bot\n",
       "10      Bot\n",
       "11      Bot\n",
       "12      Bot\n",
       "13      Bot\n",
       "14      Bot\n",
       "15      Bot\n",
       "16      Bot\n",
       "17      Bot\n",
       "18      Bot\n",
       "19      Bot\n",
       "20      Bot\n",
       "21      Bot\n",
       "22      Bot\n",
       "23      Bot\n",
       "24      Bot\n",
       "25      Bot\n",
       "26      Bot\n",
       "27      Bot\n",
       "28      Bot\n",
       "29      Bot\n",
       "       ... \n",
       "1967    Tij\n",
       "1968    Tij\n",
       "1969    Tij\n",
       "1970    Tij\n",
       "1971    Tij\n",
       "1972    Tij\n",
       "1973    Tij\n",
       "1974    Tij\n",
       "1975    Tij\n",
       "1976    Tij\n",
       "1977    Tij\n",
       "1978    Tij\n",
       "1979    Tij\n",
       "1980    Tij\n",
       "1981    Tij\n",
       "1982    Tij\n",
       "1983    Tij\n",
       "1984    Tij\n",
       "1985    Tij\n",
       "1986    Tij\n",
       "1987    Tij\n",
       "1988    Tij\n",
       "1989    Tij\n",
       "1990    Tij\n",
       "1991    Tij\n",
       "1992    Tij\n",
       "1993    Tij\n",
       "1994    Tij\n",
       "1995    Tij\n",
       "1996    Tij\n",
       "Name: bairro, Length: 1997, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bairro\"].apply(lambda x: x[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma das tarefas na qual o pandas é reconhecidamente poderoso é a habilidade de tratar dados incompletos.\n",
    "Por muitos motivos pode haver incompletude no dataset, o `np.nan` é um valor especial definido no Numpy, sigla para Not a Number, o pandas preenche células sem valores em um DataFrame lido com `np.nan`.\n",
    "\n",
    "Vamos criar um novo dataframe usando as 5 primeiras linhas do nosso original, usando o já visto `.head()`. Abaixo é usado o `.replace` para substituir um valor específico por um `NaN`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condominio</th>\n",
       "      <th>quartos</th>\n",
       "      <th>suites</th>\n",
       "      <th>vagas</th>\n",
       "      <th>area</th>\n",
       "      <th>bairro</th>\n",
       "      <th>preco</th>\n",
       "      <th>pm2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>340000</td>\n",
       "      <td>16190.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>770000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>674</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>600000</td>\n",
       "      <td>9836.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>700000</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>515000</td>\n",
       "      <td>11704.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condominio  quartos  suites  vagas  area    bairro   preco       pm2\n",
       "0         350        1     0.0    1.0    21  Botafogo  340000  16190.48\n",
       "1         800        1     0.0    1.0    64  Botafogo  770000       NaN\n",
       "2         674        1     0.0    1.0    61  Botafogo  600000   9836.07\n",
       "3         700        1     1.0    1.0    70  Botafogo  700000  10000.00\n",
       "4         440        1     0.0    1.0    44  Botafogo  515000  11704.55"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.head()\n",
    "df2 = df2.replace({\"pm2\": {12031.25: np.nan}})\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O pandas simplifica a remoção de quaiquer linhas ou colunas que possuem um `np.nan`, por padrão o `.dropna()` retorna as linhas que não contém um NaN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th>condominio</th>\n",
       "      <th>quartos</th>\n",
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       "      <th>vagas</th>\n",
       "      <th>area</th>\n",
       "      <th>bairro</th>\n",
       "      <th>preco</th>\n",
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       "      <td>16190.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>61</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>600000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>700000</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>440</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>Botafogo</td>\n",
       "      <td>515000</td>\n",
       "      <td>11704.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condominio  quartos  suites  vagas  area    bairro   preco       pm2\n",
       "0         350        1     0.0    1.0    21  Botafogo  340000  16190.48\n",
       "2         674        1     0.0    1.0    61  Botafogo  600000   9836.07\n",
       "3         700        1     1.0    1.0    70  Botafogo  700000  10000.00\n",
       "4         440        1     0.0    1.0    44  Botafogo  515000  11704.55"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preencher todos os valores NaN por um outro específico também é bastante simples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>61</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>600000</td>\n",
       "      <td>9836.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>700000</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>515000</td>\n",
       "      <td>11704.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condominio  quartos  suites  vagas  area    bairro   preco       pm2\n",
       "0         350        1     0.0    1.0    21  Botafogo  340000  16190.48\n",
       "1         800        1     0.0    1.0    64  Botafogo  770000     99.00\n",
       "2         674        1     0.0    1.0    61  Botafogo  600000   9836.07\n",
       "3         700        1     1.0    1.0    70  Botafogo  700000  10000.00\n",
       "4         440        1     0.0    1.0    44  Botafogo  515000  11704.55"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.fillna(99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acaba sendo muitas vezes conveniente termos um método que indica quais valores de um dataframe são NaN e quais não são:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>vagas</th>\n",
       "      <th>area</th>\n",
       "      <th>bairro</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condominio  quartos  suites  vagas   area  bairro  preco    pm2\n",
       "0       False    False   False  False  False   False  False  False\n",
       "1       False    False   False  False  False   False  False   True\n",
       "2       False    False   False  False  False   False  False  False\n",
       "3       False    False   False  False  False   False  False  False\n",
       "4       False    False   False  False  False   False  False  False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Partiremos agora para visualização de dados com o pandas. Os métodos de visualização do pandas são construídos com base no matplotlib para exploração rápida dos dados. Para se ter mais liberdade no conteúdo e possibilidades de visualização se recomenda usar diretamente o matplotlib ou ainda, para visualização estatística, o seaborn. \n",
    "Nesta introdução tratarei apenas dos métodos de visualização incluídos no pandas, que por outro lado, oferece uma sintaxe bastante simples para realizar a tarefa.\n",
    "\n",
    "Comecemos verificando que tanto Series como DataFrame possuem um método `.plot()` que também é um atributo e pode ser encadeado para gerar visualização de diversos tipos, como histograma, área, pizza e dispersão, com respectivamente  `.hist()`, `.area()`, `.pie()` e  `.scatter()`, além de vários [outros](https://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting).\n",
    "\n",
    "Vamos verificar a distribuição dos preços usando o encadeamento `.plot.hist()`, o eixo x, que é o preço, está numa escala de \\*10^7, como mostrado na imagem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f4f6b780>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"preco\"].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por padrão esse método usa 10 bins, ou seja, divide os dados em 10 partes, mas é claro que podemos especificar um valor para a plotagem. Abaixo, além de especificar a quantidade de bins, também especifiquei a cor das bordas como preta, que por padrão é transparente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2efd710>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"preco\"].plot.hist(bins=30, edgecolor='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos usar os valores de contagem de cada bairro como exemplo de dado para um plot tanto de barras verticais quando de barras horizontais, para verificar visualmente esses dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2e76588>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"bairro\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2e928d0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"bairro\"].value_counts().plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os métodos são flexíveis o suficiente para aceitarem argumentos como um título para a imagem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2df4ba8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"bairro\"].value_counts().plot.barh(title=\"Número de apartamentos\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um gráfico de dispersão usando um DataFrame pode ser usado especificando-se quais colunas usar como dados no eixo x e y:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2db2c50>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='preco', y='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para fins estéticos, o matplotlib fornece uma série de styles diferentes que podem ser usados, um deles é o ggplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora este estilo será usado em todas as imagens geradas após essa linha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2d77080>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='pm2', y='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lista de estilos disponíveis pode ser vista através de um método próprio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'Solarize_Light2',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-white',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn',\n",
       " 'tableau-colorblind10',\n",
       " 'seaborn-deep',\n",
       " 'classic',\n",
       " 'seaborn-dark',\n",
       " 'grayscale',\n",
       " 'seaborn-paper',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-muted',\n",
       " '_classic_test',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-colorblind',\n",
       " 'dark_background',\n",
       " 'seaborn-whitegrid',\n",
       " 'ggplot',\n",
       " 'fast']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A coluna de quartos diz quantos quartos tem um determinado apartamento, também se pode ver a contagem e distribuição usando outros métodos de plotagem oferecidos pelo pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2d2cd30>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"quartos\"].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma coisa a se notar do gráfico de scatter é a poluição causada pela enorme quantidade de dados agrupadas num dos cantos do gráfico, além de podermos diminuir o tamanho dos pontos passando o argumento `s` ao método `.scatter` podemos também usar um método do pandas que cria uma amostragem aleatória dos dados.\n",
    "\n",
    "O `.sample` pode receber tanto um argumento `frac`, que determina uma fração dos itens que o método retornará (no caso abaixo, 10%), ou `n`, que determina um valor absoluto de itens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2d4dbe0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='preco', y='area', s=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2d00a90>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sample(frac=.1).plot.scatter(x='preco', y='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, a tarefa de salvar seu DataFrame externamente para um formato específico é feita com a mesma simplicidade que a leitura de dados é feita no pandas, pode-se usar, por exemplo, o método `to_csv`, e o arquivo será criado com os dados do DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Aluno' : [\"Wilfred\", \"Abbie\", \"Harry\", \"Julia\", \"Carrie\"],\n",
    "                   'Faltas' : [3,4,2,1,4],\n",
    "                   'Prova' : [2,7,5,10,6],\n",
    "                   'Seminário': [8.5,7.5,9.0,7.5,8.0]})\n",
    "df.to_csv(\"aulas.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Aluno</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Prova</th>\n",
       "      <th>Seminário</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Wilfred</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Abbie</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Harry</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Julia</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Carrie</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Aluno  Faltas  Prova  Seminário\n",
       "0           0  Wilfred       3      2        8.5\n",
       "1           1    Abbie       4      7        7.5\n",
       "2           2    Harry       2      5        9.0\n",
       "3           3    Julia       1     10        7.5\n",
       "4           4   Carrie       4      6        8.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"aulas.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com o que foi abordado nesta introdução você já deve estar apto a fazer exploração e manipulação básica de dados com o **pandas**, para aprofundar mais aqui vão algumas referências:\n",
    "\n",
    " - [Documentação oficial](http://pandas.pydata.org/pandas-docs/stable/index.html)\n",
    " - [Coletânea de notebooks Jupyter que abordam profundamente várias ferramentas e casos de uso do Pandas\n",
    "](https://github.com/donnemartin/data-science-ipython-notebooks#pandas)\n",
    " - [Exercícios de Pandas com soluções, separados por temas](https://github.com/guipsamora/pandas_exercises)\n",
    " "
   ]
  }
 ],
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