{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manipulação de dados - II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## *DataFrames*\n",
    "\n",
    "Como dissemos anterioremente, o *DataFrame* é a segunda estrutura basilar do *pandas*. Um *DataFrame*:\n",
    "- é uma tabela, ou seja, é bidimensional;\n",
    "- tem cada coluna formada como uma *Series* do *pandas*;\n",
    "- pode ter *Series* contendo tipos de dado diferentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Criação de um *DataFrame*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O método padrão para criarmos um *DataFrame* é através de uma função com mesmo nome.\n",
    "\n",
    "```python\n",
    "df_exemplo = pd.DataFrame(dados_de_interesse, index = indice_de_interesse, \n",
    "                          columns = colunas_de_interesse)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Ao criar um *DataFrame*, podemos informar\n",
    "- `index`: rótulos para as linhas (atributos *index* das *Series*).\n",
    "- `columns`: rótulos para as colunas (atributos *name* das *Series*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "No _template_, `dados_de_interesse` pode ser\n",
    "\n",
    "* um dicionário de:\n",
    "  * *arrays* unidimensionais do *numpy*;\n",
    "  * listas;\n",
    "  * dicionários;\n",
    "  * *Series* do *pandas*.\n",
    "* um *array* bidimensional do *numpy*;\n",
    "* uma *Series* do *Pandas*;\n",
    "* outro *DataFrame*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### *DataFrame* a partir de dicionários de *Series*\n",
    "\n",
    "Neste método de criação, as *Series* do dicionário não precisam possuir o mesmo número de elementos. O *index* do *DataFrame* será dado pela **união** dos *index* de todas as *Series* contidas no dicionário."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "serie_Idade = pd.Series({'Ana':20, 'João': 19, 'Maria': 21, 'Pedro': 22}, name=\"Idade\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "serie_Peso = pd.Series({'Ana':55, 'João': 80, 'Maria': 62, 'Pedro': 67, 'Túlio': 73}, name=\"Peso\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "serie_Altura = pd.Series({'Ana':162, 'João': 178, 'Maria': 162, 'Pedro': 165, 'Túlio': 171}, name=\"Altura\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicionario_series_exemplo = {'Idade': serie_Idade, 'Peso': serie_Peso, 'Altura': serie_Altura}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dict_series = pd.DataFrame(dicionario_series_exemplo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20.0</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19.0</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21.0</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22.0</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana     20.0    55     162\n",
       "João    19.0    80     178\n",
       "Maria   21.0    62     162\n",
       "Pedro   22.0    67     165\n",
       "Túlio    NaN    73     171"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare este resultado com a criação de uma planilha pelos métodos usuais. Veja que há muita flexibilidade para criarmos ou modificarmos uma tabela.\n",
    "\n",
    "Vejamos exemplos sobre como acessar intervalos de dados na tabela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "Maria     21    62     162"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dicionario_series_exemplo, index=['Ana','Maria'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Peso  Altura\n",
       "Ana      55     162\n",
       "Maria    62     162"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dicionario_series_exemplo, index=['Ana','Maria'], columns=['Peso','Altura'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Neste exemplo, adicionamos a coluna `IMC`, ainda sem valores calculados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>62.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paula</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Peso  Altura  IMC\n",
       "Ana    55.0   162.0  NaN\n",
       "Maria  62.0   162.0  NaN\n",
       "Paula   NaN     NaN  NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dicionario_series_exemplo, index=['Ana','Maria','Paula'], \n",
    "             columns=['Peso','Altura','IMC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df_exemplo_IMC = pd.DataFrame(dicionario_series_exemplo, \n",
    "             columns=['Peso','Altura','IMC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, mostramos como os valores do IMC podem ser calculados diretamente por computação vetorizada sobre as *Series*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exemplo_IMC['IMC']=round(df_exemplo_IMC['Peso']/(df_exemplo_IMC['Altura']/100)**2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "      <td>23.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "      <td>24.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "      <td>24.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Peso  Altura    IMC\n",
       "Ana      55     162  20.96\n",
       "João     80     178  25.25\n",
       "Maria    62     162  23.62\n",
       "Pedro    67     165  24.61\n",
       "Túlio    73     171  24.96"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo_IMC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### *DataFrame* a partir de dicionários de listas ou *arrays* do *numpy*\n",
    "\n",
    "Neste método de criação, os *arrays* ou as listas **devem** possuir o mesmo comprimento. Se o *index* não for informado, o *index* será dado de forma similar ao do objeto tipo *Series*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplo com dicionário de listas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicionario_lista_exemplo = {'Idade': [20,19,21,22,20],\n",
    "                            'Peso': [55,80,62,67,73],\n",
    "                            'Altura': [162,178,162,165,171]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Idade  Peso  Altura\n",
       "0     20    55     162\n",
       "1     19    80     178\n",
       "2     21    62     162\n",
       "3     22    67     165\n",
       "4     20    73     171"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dicionario_lista_exemplo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Mais exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "João      19    80     178\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dicionario_lista_exemplo, index=['Ana','João','Maria','Pedro','Túlio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplos com dicionário de *arrays* do *numpy*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicionario_array_exemplo = {'Idade': np.array([20,19,21,22,20]),\n",
    "                            'Peso': np.array([55,80,62,67,73]),\n",
    "                            'Altura': np.array([162,178,162,165,171])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Idade  Peso  Altura\n",
       "0     20    55     162\n",
       "1     19    80     178\n",
       "2     21    62     162\n",
       "3     22    67     165\n",
       "4     20    73     171"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dicionario_array_exemplo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Mais exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "João      19    80     178\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dicionario_array_exemplo, index=['Ana','João','Maria','Pedro','Túlio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### *DataFrame* a partir de uma *Series* do *pandas*\n",
    "\n",
    "Neste caso, o *DataFrame* terá o mesmo *index* que a *Series* do *pandas* e apenas uma coluna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "series_exemplo = pd.Series({'Ana':20, 'João': 19, 'Maria': 21, 'Pedro': 22, 'Túlio': 20})"
   ]
  },
  {
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0\n",
       "Ana    20\n",
       "João   19\n",
       "Maria  21\n",
       "Pedro  22\n",
       "Túlio  20"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(series_exemplo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Caso a *Series* possua um atributo `name` especificado, este será o nome da coluna do *DataFrame*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "series_exemplo_Idade = pd.Series({'Ana':20, 'João': 19, 'Maria': 21, 'Pedro': 22, 'Túlio': 20}, name=\"Idade\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade\n",
       "Ana       20\n",
       "João      19\n",
       "Maria     21\n",
       "Pedro     22\n",
       "Túlio     20"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(series_exemplo_Idade)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### *DataFrame* a partir de lista de *Series* do *pandas*\n",
    "\n",
    "Neste caso, a entrada dos dados da lista no *DataFrame* será feita por linha."
   ]
  },
  {
   "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>Ana</th>\n",
       "      <th>João</th>\n",
       "      <th>Maria</th>\n",
       "      <th>Pedro</th>\n",
       "      <th>Túlio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peso</th>\n",
       "      <td>55.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Altura</th>\n",
       "      <td>162.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idade</th>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Ana   João  Maria  Pedro  Túlio\n",
       "Peso     55.0   80.0   62.0   67.0   73.0\n",
       "Altura  162.0  178.0  162.0  165.0  171.0\n",
       "Idade    20.0   19.0   21.0   22.0    NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([serie_Peso, serie_Altura, serie_Idade])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Podemos corrigir a orientação usando o método `transpose`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>80.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>62.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>67.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>73.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Peso  Altura  Idade\n",
       "Ana    55.0   162.0   20.0\n",
       "João   80.0   178.0   19.0\n",
       "Maria  62.0   162.0   21.0\n",
       "Pedro  67.0   165.0   22.0\n",
       "Túlio  73.0   171.0    NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([serie_Peso, serie_Altura, serie_Idade]).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### *DataFrame* a partir de arquivos\n",
    "\n",
    "Para criar um *DataFrame* a partir de um arquivo, precisamos de funções do tipo `pd.read_FORMATO`, onde `FORMATO` indica o formato a ser importado sob o pressuposto de que a biblioteca *pandas* foi devidamente importada com `pd`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Os formatos mais comuns são: \n",
    "\n",
    "* *csv* (comma-separated values), \n",
    "* *xls* ou *xlsx* (formatos do Microsoft Excel),\n",
    "* *hdf5* (comumente utilizado em *big data*), \n",
    "* *json* (comumente utilizado em páginas da internet)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "As funções para leitura correspondentes são: \n",
    "* `pd.read_csv`, \n",
    "* `pd.read_excel`, \n",
    "* `pd.read_hdf`, \n",
    "* `pd.read_json`, \n",
    "\n",
    "respectivamente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "De todas elas, a função mais utilizada é `read_csv`. Ela possui vários argumentos. Vejamos os mais utilizados:\n",
    "\n",
    "* `file_path_or_buffer`: o endereço do arquivo a ser lido. Pode ser um endereço da internet.\n",
    "* `sep`: o separador entre as entradas de dados. O separador padrão é `,`.\n",
    "* `index_col`: a coluna que deve ser usada para formar o *index*. O padrão é `None`. Porém pode ser alterado para outro. Um separador comumente encontrado é o `\\t` (TAB).\n",
    "* `names`: nomes das colunas a serem usadas. O padrão é `None`.\n",
    "* `header`: número da linha que servirá como nome para as colunas. O padrão é `infer` (ou seja, tenta deduzir automaticamente). Se os nomes das colunas forem passados através do `names`, então `header` será automaticamente considerado como `None`.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Exemplo:** considere o arquivo `data/exemplo_data.csv` contendo:\n",
    "\n",
    "```\n",
    ",coluna_1,coluna_2\n",
    "2020-01-01,-0.4160923582996922,1.8103644347460834\n",
    "2020-01-02,-0.1379696602473578,2.5785204825192785\n",
    "2020-01-03,0.5758273450544708,0.06086648807755068\n",
    "2020-01-04,-0.017367186564883633,1.2995865328684455\n",
    "2020-01-05,1.3842792448510655,-0.3817320973859929\n",
    "2020-01-06,0.5497056238566345,-1.308789022968975\n",
    "2020-01-07,-0.2822962331437976,-1.6889791765925102\n",
    "2020-01-08,-0.9897300598660013,-0.028120707936426497\n",
    "2020-01-09,0.27558240737928663,-0.1776585993494299\n",
    "2020-01-10,0.6851316082235455,0.5025348904591399\n",
    "``` \n",
    "\n",
    "Para ler o arquivo acima basta fazer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df_exemplo_0 = pd.read_csv('../database/exemplo_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>-0.416092</td>\n",
       "      <td>1.810364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>-0.137970</td>\n",
       "      <td>2.578520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.575827</td>\n",
       "      <td>0.060866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>-0.017367</td>\n",
       "      <td>1.299587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>1.384279</td>\n",
       "      <td>-0.381732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>0.549706</td>\n",
       "      <td>-1.308789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>-0.282296</td>\n",
       "      <td>-1.688979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>-0.989730</td>\n",
       "      <td>-0.028121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-01-09</td>\n",
       "      <td>0.275582</td>\n",
       "      <td>-0.177659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>0.685132</td>\n",
       "      <td>0.502535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  coluna_1  coluna_2\n",
       "0  2020-01-01 -0.416092  1.810364\n",
       "1  2020-01-02 -0.137970  2.578520\n",
       "2  2020-01-03  0.575827  0.060866\n",
       "3  2020-01-04 -0.017367  1.299587\n",
       "4  2020-01-05  1.384279 -0.381732\n",
       "5  2020-01-06  0.549706 -1.308789\n",
       "6  2020-01-07 -0.282296 -1.688979\n",
       "7  2020-01-08 -0.989730 -0.028121\n",
       "8  2020-01-09  0.275582 -0.177659\n",
       "9  2020-01-10  0.685132  0.502535"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "No exemplo anterior, as colunas receberam nomes corretamentes exceto pela primeira coluna que gostaríamos de considerar como *index*. Neste caso fazemos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exemplo = pd.read_csv('../database/exemplo_data.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
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       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
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       "      <td>2.578520</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.575827</td>\n",
       "      <td>0.060866</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.017367</td>\n",
       "      <td>1.299587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>1.384279</td>\n",
       "      <td>-0.381732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.549706</td>\n",
       "      <td>-1.308789</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.282296</td>\n",
       "      <td>-1.688979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.989730</td>\n",
       "      <td>-0.028121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.275582</td>\n",
       "      <td>-0.177659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.685132</td>\n",
       "      <td>0.502535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2\n",
       "2020-01-01 -0.416092  1.810364\n",
       "2020-01-02 -0.137970  2.578520\n",
       "2020-01-03  0.575827  0.060866\n",
       "2020-01-04 -0.017367  1.299587\n",
       "2020-01-05  1.384279 -0.381732\n",
       "2020-01-06  0.549706 -1.308789\n",
       "2020-01-07 -0.282296 -1.688979\n",
       "2020-01-08 -0.989730 -0.028121\n",
       "2020-01-09  0.275582 -0.177659\n",
       "2020-01-10  0.685132  0.502535"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### O método *head* do *DataFrame*\n",
    "\n",
    "O método `head`, sem argumento, permite que visualizemos as 5 primeiras linhas do *DataFrame*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-0.416092</td>\n",
       "      <td>1.810364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.137970</td>\n",
       "      <td>2.578520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.575827</td>\n",
       "      <td>0.060866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.017367</td>\n",
       "      <td>1.299587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>1.384279</td>\n",
       "      <td>-0.381732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2\n",
       "2020-01-01 -0.416092  1.810364\n",
       "2020-01-02 -0.137970  2.578520\n",
       "2020-01-03  0.575827  0.060866\n",
       "2020-01-04 -0.017367  1.299587\n",
       "2020-01-05  1.384279 -0.381732"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se for passado um argumento com valor `n`, as `n` primeiras linhas são impressas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-0.416092</td>\n",
       "      <td>1.810364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.137970</td>\n",
       "      <td>2.578520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2\n",
       "2020-01-01 -0.416092  1.810364\n",
       "2020-01-02 -0.137970  2.578520"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-0.416092</td>\n",
       "      <td>1.810364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.137970</td>\n",
       "      <td>2.578520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.575827</td>\n",
       "      <td>0.060866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.017367</td>\n",
       "      <td>1.299587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>1.384279</td>\n",
       "      <td>-0.381732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.549706</td>\n",
       "      <td>-1.308789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.282296</td>\n",
       "      <td>-1.688979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2\n",
       "2020-01-01 -0.416092  1.810364\n",
       "2020-01-02 -0.137970  2.578520\n",
       "2020-01-03  0.575827  0.060866\n",
       "2020-01-04 -0.017367  1.299587\n",
       "2020-01-05  1.384279 -0.381732\n",
       "2020-01-06  0.549706 -1.308789\n",
       "2020-01-07 -0.282296 -1.688979"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### O método `tail` do *DataFrame*\n",
    "\n",
    "O método `tail`, sem argumento, retorna as últimas 5 linhas do *DataFrame*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.549706</td>\n",
       "      <td>-1.308789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.282296</td>\n",
       "      <td>-1.688979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.989730</td>\n",
       "      <td>-0.028121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.275582</td>\n",
       "      <td>-0.177659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.685132</td>\n",
       "      <td>0.502535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2\n",
       "2020-01-06  0.549706 -1.308789\n",
       "2020-01-07 -0.282296 -1.688979\n",
       "2020-01-08 -0.989730 -0.028121\n",
       "2020-01-09  0.275582 -0.177659\n",
       "2020-01-10  0.685132  0.502535"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se for passado um argumento com valor `n`, as `n` últimas linhas são impressas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.275582</td>\n",
       "      <td>-0.177659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.685132</td>\n",
       "      <td>0.502535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2\n",
       "2020-01-09  0.275582 -0.177659\n",
       "2020-01-10  0.685132  0.502535"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.017367</td>\n",
       "      <td>1.299587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>1.384279</td>\n",
       "      <td>-0.381732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.549706</td>\n",
       "      <td>-1.308789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.282296</td>\n",
       "      <td>-1.688979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.989730</td>\n",
       "      <td>-0.028121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.275582</td>\n",
       "      <td>-0.177659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.685132</td>\n",
       "      <td>0.502535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2\n",
       "2020-01-04 -0.017367  1.299587\n",
       "2020-01-05  1.384279 -0.381732\n",
       "2020-01-06  0.549706 -1.308789\n",
       "2020-01-07 -0.282296 -1.688979\n",
       "2020-01-08 -0.989730 -0.028121\n",
       "2020-01-09  0.275582 -0.177659\n",
       "2020-01-10  0.685132  0.502535"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.tail(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Atributos de *Series* e *DataFrames*\n",
    "\n",
    "Atributos comumente usados para *Series* e *DataFrames* são:\n",
    "\n",
    "* `shape`: fornece as dimensões do objeto em questão (*Series* ou *DataFrame*) em formato consistente com o atributo `shape` de um *array* do *numpy*.\n",
    "* `index`: fornece o índice do objeto. No caso do *DataFrame* são os rótulos das linhas.\n",
    "* `columns`: fornece as colunas (apenas disponível para *DataFrames*) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "serie_1 = pd.Series([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05',\n",
       "       '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['coluna_1', 'coluna_2'], dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se quisermos obter os dados contidos nos *index* ou nas *Series* podemos utilizar a propriedade `.array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PandasArray>\n",
       "[0, 1, 2, 3, 4]\n",
       "Length: 5, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_1.index.array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PandasArray>\n",
       "['coluna_1', 'coluna_2']\n",
       "Length: 2, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.columns.array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se o interesse for obter os dados como um `array` do *numpy*, devemos utilizar o método `.to_numpy()`.\n",
    "\n",
    "Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_1.index.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['coluna_1', 'coluna_2'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.columns.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "O método `.to_numpy()` também está disponível em *DataFrames*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.41609236,  1.81036443],\n",
       "       [-0.13796966,  2.57852048],\n",
       "       [ 0.57582735,  0.06086649],\n",
       "       [-0.01736719,  1.29958653],\n",
       "       [ 1.38427924, -0.3817321 ],\n",
       "       [ 0.54970562, -1.30878902],\n",
       "       [-0.28229623, -1.68897918],\n",
       "       [-0.98973006, -0.02812071],\n",
       "       [ 0.27558241, -0.1776586 ],\n",
       "       [ 0.68513161,  0.50253489]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "A função do *numpy* `asarray()` é compatível com *index*, *columns* e *DataFrames* do *pandas*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n",
       "       '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',\n",
       "       '2020-01-09', '2020-01-10'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(df_exemplo.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['coluna_1', 'coluna_2'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(df_exemplo.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.41609236,  1.81036443],\n",
       "       [-0.13796966,  2.57852048],\n",
       "       [ 0.57582735,  0.06086649],\n",
       "       [-0.01736719,  1.29958653],\n",
       "       [ 1.38427924, -0.3817321 ],\n",
       "       [ 0.54970562, -1.30878902],\n",
       "       [-0.28229623, -1.68897918],\n",
       "       [-0.98973006, -0.02812071],\n",
       "       [ 0.27558241, -0.1776586 ],\n",
       "       [ 0.68513161,  0.50253489]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(df_exemplo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Informações sobre as colunas de um *DataFrame*\n",
    "\n",
    "Para obtermos uma breve descrição sobre as colunas de um *DataFrame* utilizamos o método `info`.\n",
    "\n",
    "Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10 entries, 2020-01-01 to 2020-01-10\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   coluna_1  10 non-null     float64\n",
      " 1   coluna_2  10 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 240.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_exemplo.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Criando arquivos a partir de *DataFrames*\n",
    "\n",
    "Para criar arquivos a partir de *DataFrames*, basta utilizar os métodos do tipo `pd.to_FORMATO`, onde `FORMATO` indica o formato a ser exportado e supondo que a biblioteca *pandas* foi importada com `pd`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Com relação aos tipos de arquivo anteriores, os métodos para exportação correspondentes são:\n",
    "* `.to_csv` ('endereço_do_arquivo'), \n",
    "* `.to_excel` ('endereço_do_arquivo'), \n",
    "* `.to_hdf` ('endereço_do_arquivo'), \n",
    "* `.to_json`('endereço_do_arquivo'), \n",
    "\n",
    "onde `endereço_do_arquivo` é uma `str` que contém o endereço do arquivo a ser exportado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplo:\n",
    "    \n",
    "Para exportar para o arquivo `exemplo_novo.csv`, utilizaremos o método `.to_csv` ao *DataFrame* `df_exemplo`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exemplo.to_csv('../database/exemplo_novo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Exemplo COVID-19 PB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Dados diários de COVID-19 do estado da Paraíba:\n",
    "\n",
    "*Fonte: https://superset.plataformatarget.com.br/superset/dashboard/microdados/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "dados_covid_PB = pd.read_csv('https://superset.plataformatarget.com.br/superset/explore_json/?form_data=%7B%22slice_id%22%3A1550%7D&csv=true', \n",
    "                             sep=',', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 559 entries, 2021-09-26 to 2020-03-16\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   casosAcumulados   559 non-null    int64  \n",
      " 1   casosNovos        559 non-null    int64  \n",
      " 2   descartados       559 non-null    int64  \n",
      " 3   recuperados       559 non-null    int64  \n",
      " 4   obitosAcumulados  559 non-null    int64  \n",
      " 5   obitosNovos       558 non-null    float64\n",
      " 6   Letalidade        559 non-null    float64\n",
      "dtypes: float64(2), int64(5)\n",
      "memory usage: 34.9+ KB\n"
     ]
    }
   ],
   "source": [
    "dados_covid_PB.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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>2021-09-26</th>\n",
       "      <td>441155</td>\n",
       "      <td>134</td>\n",
       "      <td>484336</td>\n",
       "      <td>333751</td>\n",
       "      <td>9294</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-25</th>\n",
       "      <td>441021</td>\n",
       "      <td>165</td>\n",
       "      <td>484331</td>\n",
       "      <td>333441</td>\n",
       "      <td>9292</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>440856</td>\n",
       "      <td>225</td>\n",
       "      <td>484301</td>\n",
       "      <td>332845</td>\n",
       "      <td>9290</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>440631</td>\n",
       "      <td>203</td>\n",
       "      <td>484276</td>\n",
       "      <td>332540</td>\n",
       "      <td>9287</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>440428</td>\n",
       "      <td>297</td>\n",
       "      <td>484265</td>\n",
       "      <td>331958</td>\n",
       "      <td>9284</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2021-09-26           441155         134       484336       333751   \n",
       "2021-09-25           441021         165       484331       333441   \n",
       "2021-09-24           440856         225       484301       332845   \n",
       "2021-09-23           440631         203       484276       332540   \n",
       "2021-09-22           440428         297       484265       331958   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade  \n",
       "data                                                   \n",
       "2021-09-26              9294          2.0      0.0211  \n",
       "2021-09-25              9292          2.0      0.0211  \n",
       "2021-09-24              9290          3.0      0.0211  \n",
       "2021-09-23              9287          3.0      0.0211  \n",
       "2021-09-22              9284          6.0      0.0211  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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>2020-03-20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2020-03-20                0           0            0            0   \n",
       "2020-03-19                0           0            0            0   \n",
       "2020-03-18                0           0            0            0   \n",
       "2020-03-17                0           0            0            0   \n",
       "2020-03-16                0           0            0            0   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade  \n",
       "data                                                   \n",
       "2020-03-20                 0          0.0         0.0  \n",
       "2020-03-19                 0          0.0         0.0  \n",
       "2020-03-18                 0          0.0         0.0  \n",
       "2020-03-17                 0          0.0         0.0  \n",
       "2020-03-16                 0          0.0         0.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "dados_covid_PB['estado'] = 'PB'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "      <th>estado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-26</th>\n",
       "      <td>441155</td>\n",
       "      <td>134</td>\n",
       "      <td>484336</td>\n",
       "      <td>333751</td>\n",
       "      <td>9294</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-25</th>\n",
       "      <td>441021</td>\n",
       "      <td>165</td>\n",
       "      <td>484331</td>\n",
       "      <td>333441</td>\n",
       "      <td>9292</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>440856</td>\n",
       "      <td>225</td>\n",
       "      <td>484301</td>\n",
       "      <td>332845</td>\n",
       "      <td>9290</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>440631</td>\n",
       "      <td>203</td>\n",
       "      <td>484276</td>\n",
       "      <td>332540</td>\n",
       "      <td>9287</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>440428</td>\n",
       "      <td>297</td>\n",
       "      <td>484265</td>\n",
       "      <td>331958</td>\n",
       "      <td>9284</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2021-09-26           441155         134       484336       333751   \n",
       "2021-09-25           441021         165       484331       333441   \n",
       "2021-09-24           440856         225       484301       332845   \n",
       "2021-09-23           440631         203       484276       332540   \n",
       "2021-09-22           440428         297       484265       331958   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade estado  \n",
       "data                                                          \n",
       "2021-09-26              9294          2.0      0.0211     PB  \n",
       "2021-09-25              9292          2.0      0.0211     PB  \n",
       "2021-09-24              9290          3.0      0.0211     PB  \n",
       "2021-09-23              9287          3.0      0.0211     PB  \n",
       "2021-09-22              9284          6.0      0.0211     PB  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_covid_PB.to_csv('../database/dadoscovidpb.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Índices dos valores máximos ou mínimos\n",
    "\n",
    "Os métodos `idxmin()` e `idxmax()` retornam o *index* cuja entrada fornece o valor mínimo ou máximo da *Series* ou *DataFrame*. Se houver múltiplas ocorrências de mínimos ou máximos, o método retorna a primeira ocorrência.\n",
    "\n",
    "Vamos recriar um *DataFrame* genérico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "serie_Idade = pd.Series({'Ana':20, 'João': 19, 'Maria': 21, 'Pedro': 22, 'Túlio': 20}, name=\"Idade\")\n",
    "serie_Peso = pd.Series({'Ana':55, 'João': 80, 'Maria': 62, 'Pedro': 67, 'Túlio': 73}, name=\"Peso\")\n",
    "serie_Altura = pd.Series({'Ana':162, 'João': 178, 'Maria': 162, 'Pedro': 165, 'Túlio': 171}, name=\"Altura\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicionario_series_exemplo = {'Idade': serie_Idade, 'Peso': serie_Peso, 'Altura': serie_Altura}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dict_series = pd.DataFrame(dicionario_series_exemplo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "João      19    80     178\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim, podemos localizar quem possui menores idade, peso e altura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Idade     João\n",
       "Peso       Ana\n",
       "Altura     Ana\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.idxmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De igual forma, localizamos quem possui maiores idade, peso e altura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Idade     Pedro\n",
       "Peso       João\n",
       "Altura     João\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Exemplo:** Aplicaremos as funções `idxmin()` e `idxmax()` aos dados do arquivo `data/exemplo_data.csv` para localizar entradas de interesse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-0.416092</td>\n",
       "      <td>1.810364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.137970</td>\n",
       "      <td>2.578520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.575827</td>\n",
       "      <td>0.060866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.017367</td>\n",
       "      <td>1.299587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>1.384279</td>\n",
       "      <td>-0.381732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.549706</td>\n",
       "      <td>-1.308789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.282296</td>\n",
       "      <td>-1.688979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.989730</td>\n",
       "      <td>-0.028121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.275582</td>\n",
       "      <td>-0.177659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.685132</td>\n",
       "      <td>0.502535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2\n",
       "2020-01-01 -0.416092  1.810364\n",
       "2020-01-02 -0.137970  2.578520\n",
       "2020-01-03  0.575827  0.060866\n",
       "2020-01-04 -0.017367  1.299587\n",
       "2020-01-05  1.384279 -0.381732\n",
       "2020-01-06  0.549706 -1.308789\n",
       "2020-01-07 -0.282296 -1.688979\n",
       "2020-01-08 -0.989730 -0.028121\n",
       "2020-01-09  0.275582 -0.177659\n",
       "2020-01-10  0.685132  0.502535"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo = pd.read_csv('../database/exemplo_data.csv', index_col=0); df_exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df_exemplo = pd.DataFrame(df_exemplo, columns=['coluna_1','coluna_2','coluna_3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inserimos uma terceira coluna com dados fictícios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "      <th>coluna_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-0.416092</td>\n",
       "      <td>1.810364</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.137970</td>\n",
       "      <td>2.578520</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.575827</td>\n",
       "      <td>0.060866</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.017367</td>\n",
       "      <td>1.299587</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>1.384279</td>\n",
       "      <td>-0.381732</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.549706</td>\n",
       "      <td>-1.308789</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.282296</td>\n",
       "      <td>-1.688979</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.989730</td>\n",
       "      <td>-0.028121</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.275582</td>\n",
       "      <td>-0.177659</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.685132</td>\n",
       "      <td>0.502535</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2  coluna_3\n",
       "2020-01-01 -0.416092  1.810364       1.0\n",
       "2020-01-02 -0.137970  2.578520       2.0\n",
       "2020-01-03  0.575827  0.060866       3.0\n",
       "2020-01-04 -0.017367  1.299587       4.0\n",
       "2020-01-05  1.384279 -0.381732       5.0\n",
       "2020-01-06  0.549706 -1.308789       6.0\n",
       "2020-01-07 -0.282296 -1.688979       7.0\n",
       "2020-01-08 -0.989730 -0.028121       8.0\n",
       "2020-01-09  0.275582 -0.177659       NaN\n",
       "2020-01-10  0.685132  0.502535       NaN"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo['coluna_3'] = pd.Series([1,2,3,4,5,6,7,8,np.nan,np.nan],index=df_exemplo.index)\n",
    "df_exemplo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os *index* correspondentes aos menores e maiores valores são datas, evidentemente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coluna_1    2020-01-08\n",
       "coluna_2    2020-01-07\n",
       "coluna_3    2020-01-01\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coluna_1    2020-01-05\n",
       "coluna_2    2020-01-02\n",
       "coluna_3    2020-01-08\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Reindexação de *DataFrames*\n",
    "\n",
    "No *pandas*, o método `reindex`\n",
    "\n",
    "* reordena o *DataFrame* de acordo com o conjunto de rótulos inserido como argumento;\n",
    "* insere valores faltantes caso um rótulo do novo *index* não tenha valor atribuído no conjunto de dados;\n",
    "* remove valores correspondentes a rótulos que não estão presentes no novo *index*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Altura</th>\n",
       "      <th>Peso</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Victor</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>171.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>165.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>178.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Altura  Peso  IMC\n",
       "Victor     NaN   NaN  NaN\n",
       "Túlio    171.0  73.0  NaN\n",
       "Pedro    165.0  67.0  NaN\n",
       "João     178.0  80.0  NaN"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.reindex(index=['Victor', 'Túlio', 'Pedro', 'João'], columns=['Altura','Peso','IMC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Remoção de linhas ou colunas de um *DataFrame*\n",
    "\n",
    "Para remover linhas ou colunas de um *DataFrame* do *pandas* podemos utilizar o método `drop`. O argumento `axis` identifica o eixo de remoção: `axis=0`, que é o padrão, indica a remoção de uma ou mais linhas; `axis=1` indica a remoção de uma ou mais colunas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Nos exemplos que segue, note que novos *DataFrames* são obtidos a partir de `df_dict_series` sem que este seja sobrescrito."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "João      19    80     178\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.drop('Túlio') # axis=0 implícito "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "João      19    80     178\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.drop(['Ana','Maria'], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Peso  Altura\n",
       "Ana      55     162\n",
       "João     80     178\n",
       "Maria    62     162\n",
       "Pedro    67     165\n",
       "Túlio    73     171"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.drop(['Idade'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Renomeando *index* e *columns*\n",
    "\n",
    "O método `rename` retorna uma cópia na qual o *index* (no caso de *Series* e *DataFrames*) e *columns* (no caso de *DataFrames*) foram renomeados. O método aceita como entrada um dicionário, uma *Series* do *pandas* ou uma função."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "serie_exemplo = pd.Series([1,2,3], index=['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "abacaxi    1\n",
       "banana     2\n",
       "cebola     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo.rename({'a':'abacaxi', 'b':'banana', 'c': 'cebola'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "João      19    80     178\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>I</th>\n",
       "      <th>P</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    I   P    A\n",
       "a  20  55  162\n",
       "j  19  80  178\n",
       "m  21  62  162\n",
       "p  22  67  165\n",
       "t  20  73  171"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.rename(index = {'Ana':'a', 'João':'j', 'Maria':'m', 'Pedro':'p','Túlio':'t'},\n",
    "                     columns = {'Idade':'I', 'Peso':'P','Altura':'A'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No próximo exemplo, usamos uma *Series* para renomear os rótulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "indice_novo = pd.Series({'Ana':'a', 'João':'j', 'Maria':'m', 'Pedro':'p','Túlio':'t'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Idade  Peso  Altura\n",
       "a     20    55     162\n",
       "j     19    80     178\n",
       "m     21    62     162\n",
       "p     22    67     165\n",
       "t     20    73     171"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.rename(index = indice_novo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neste exemplo, usamos a função `str.upper` (altera a `str` para \"todas maiúsculas\") para renomear colunas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>IDADE</th>\n",
       "      <th>PESO</th>\n",
       "      <th>ALTURA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IDADE  PESO  ALTURA\n",
       "Ana       20    55     162\n",
       "João      19    80     178\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.rename(columns=str.upper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ordenação de *Series* e *DataFrames*\n",
    "\n",
    "É possível ordenar ambos pelos rótulos do *index* (para tanto é necessário que eles sejam ordenáveis) ou por valores nas colunas. \n",
    "\n",
    "O método `sort_index` ordena a *Series* ou o *DataFrame* pelo *index*. O método `sort_values` ordena a *Series* ou o *DataFrame* pelos valores (escolhendo uma ou mais colunas no caso de *DataFrames*). No caso do *DataFrame*, o argumento `by` é necessário para indicar qual(is) coluna(s) será(ão) utilizada(s) como base para a ordenação."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Maria    21\n",
       "Pedro    22\n",
       "Túlio    20\n",
       "João     19\n",
       "Ana      20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_desordenada = pd.Series({'Maria': 21, 'Pedro': 22, 'Túlio': 20, 'João': 19, 'Ana':20}); \n",
    "serie_desordenada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana      20\n",
       "João     19\n",
       "Maria    21\n",
       "Pedro    22\n",
       "Túlio    20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_desordenada.sort_index() # ordenação alfabética"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Mais exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_desordenado = df_dict_series.reindex(index=['Pedro','Maria','Ana','Túlio','João'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Pedro     22    67     165\n",
       "Maria     21    62     162\n",
       "Ana       20    55     162\n",
       "Túlio     20    73     171\n",
       "João      19    80     178"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desordenado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "João      19    80     178\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desordenado.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Mais exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "João     19\n",
       "Túlio    20\n",
       "Ana      20\n",
       "Maria    21\n",
       "Pedro    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_desordenada.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Maria     21    62     162\n",
       "Ana       20    55     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171\n",
       "João      19    80     178"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desordenado.sort_values(by=['Altura']) # ordena por 'Altura'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "No caso de \"empate\", podemos utilizar outra coluna para desempatar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171\n",
       "João      19    80     178"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desordenado.sort_values(by=['Altura','Peso']) # usa a coluna 'Peso' para desempatar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Os métodos `sort_index` e `sort_values` admitem o argumento opcional `ascending`, que permite inverter a ordenação caso tenha valor `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Túlio     20    73     171\n",
       "Pedro     22    67     165\n",
       "Maria     21    62     162\n",
       "João      19    80     178\n",
       "Ana       20    55     162"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desordenado.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Pedro     22    67     165\n",
       "Maria     21    62     162\n",
       "Ana       20    55     162\n",
       "Túlio     20    73     171\n",
       "João      19    80     178"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desordenado.sort_values(by=['Idade'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Comparação de *Series* e *DataFrames*\n",
    "\n",
    "*Series* e *DataFrames* possuem os seguintes métodos de comparação lógica: \n",
    "\n",
    "- `eq` (igual);\n",
    "- `ne` (diferente);\n",
    "- `lt` (menor do que);\n",
    "- `gt` (maior do que);\n",
    "- `le` (menor ou igual a); \n",
    "- `ge` (maior ou igual a)\n",
    "\n",
    "que permitem a utilização dos operadores binários `==`, `!=`, `<`, `>`, `<=` e `>=`, respectivamente. As comparações são realizadas em cada entrada da *Series* ou do *DataFrame*.\n",
    "\n",
    "**Observação**: Para que esses métodos sejam aplicados, todos os objetos presentes nas colunas do *DataFrame* devem ser de mesma natureza. Por exemplo, se um *DataFrame* possui algumas colunas numéricas e outras com *strings*, ao realizar uma comparação do tipo `> 1`, um erro ocorrerá, pois o *pandas* tentará comparar objetos do tipo `int` com objetos do tipo `str`, assim gerando uma incompatibilidade.\n",
    "\n",
    "Exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "c    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo == 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De outra forma:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "c    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo.eq(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "c     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo > 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou, na forma funcional:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "c     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo.gt(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "      <th>coluna_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2  coluna_3\n",
       "2020-01-01     False      True     False\n",
       "2020-01-02     False      True      True\n",
       "2020-01-03     False     False      True\n",
       "2020-01-04     False      True      True\n",
       "2020-01-05      True     False      True\n",
       "2020-01-06     False     False      True\n",
       "2020-01-07     False     False      True\n",
       "2020-01-08     False     False      True\n",
       "2020-01-09     False     False     False\n",
       "2020-01-10     False     False     False"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo > 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Importante:** Ao comparar *np.nan*, o resultado tipicamente é falso:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan > np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan >= np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Só é verdadeiro para indicar que é diferente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan != np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Neste sentido, podemos ter tabelas iguais sem que a comparação usual funcione:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# 'copy', como o nome sugere, gera uma cópia do DataFrame\n",
    "df_exemplo_2 = df_exemplo.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>coluna_1</th>\n",
       "      <th>coluna_2</th>\n",
       "      <th>coluna_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coluna_1  coluna_2  coluna_3\n",
       "2020-01-01      True      True      True\n",
       "2020-01-02      True      True      True\n",
       "2020-01-03      True      True      True\n",
       "2020-01-04      True      True      True\n",
       "2020-01-05      True      True      True\n",
       "2020-01-06      True      True      True\n",
       "2020-01-07      True      True      True\n",
       "2020-01-08      True      True      True\n",
       "2020-01-09      True      True     False\n",
       "2020-01-10      True      True     False"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_exemplo == df_exemplo_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "O motivo de haver entradas como `False` ainda que `df_exemplo_2` seja uma cópia exata de `df_exemplo` é a presença do `np.nan`. Neste caso, devemos utilizar o método `equals` para realizar a comparação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.equals(df_exemplo_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Os métodos `any`, `all` e a propriedade `empty`\n",
    "\n",
    "O método `any` é aplicado a entradas booleanas (verdadeiras ou falsas) e retorna *verdadeiro* se existir alguma entrada verdadeira, ou *falso*, se todas forem falsas. O método `all` é aplicado a entradas booleanas e retorna *verdadeiro* se todas as entradas forem verdadeiras, ou *falso*,  se houver pelo menos uma entrada falsa. A propriedade `empty` retorna *verdadeiro* se a *Series* ou o *DataFrame* estiver vazio, ou *falso* caso contrário."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -1\n",
       "b    0\n",
       "c    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo_2 = serie_exemplo-2; \n",
    "serie_exemplo_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(serie_exemplo_2 > 0).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(serie_exemplo > 1).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este exemplo reproduz um valor `False` único."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_exemplo == df_exemplo_2).all().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_exemplo.empty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Mais exemplos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coluna_1    True\n",
       "coluna_2    True\n",
       "coluna_3    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_exemplo == df_exemplo_2).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exemplo.empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vazio = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vazio.empty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Seleção de colunas de um *DataFrame*\n",
    "\n",
    "Para selecionar colunas de um *DataFrame*, basta aplicar *colchetes* a uma lista contendo os nomes das colunas de interesse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "No exemplo abaixo, temos um *DataFrame* contendo as colunas `'Idade'`, `'Peso'` e `'Altura'`. Selecionaremos `'Peso'` e `'Altura'`, apenas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Peso  Altura\n",
       "Ana      55     162\n",
       "João     80     178\n",
       "Maria    62     162\n",
       "Pedro    67     165\n",
       "Túlio    73     171"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series[['Peso','Altura']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se quisermos selecionar apenas uma coluna, não há necessidade de inserir uma lista. Basta utilizar o nome da coluna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana      55\n",
       "João     80\n",
       "Maria    62\n",
       "Pedro    67\n",
       "Túlio    73\n",
       "Name: Peso, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series['Peso']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Para remover colunas, podemos utilizar o método `drop`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>Idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade\n",
       "Ana       20\n",
       "João      19\n",
       "Maria     21\n",
       "Pedro     22\n",
       "Túlio     20"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.drop(['Peso','Altura'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Criação de novas colunas a partir de colunas existentes\n",
    "\n",
    "Um método eficiente para criar novas colunas a partir de colunas já existentes é `eval`. Neste método, podemos utilizar como argumento uma *string* contendo uma expressão matemática envolvendo nomes de colunas do *DataFrame*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Como exemplo, vamos ver como calcular o IMC no *DataFrame* anterior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana      20.957171\n",
       "João     25.249337\n",
       "Maria    23.624447\n",
       "Pedro    24.609734\n",
       "Túlio    24.964946\n",
       "dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.eval('Peso/(Altura/100)**2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se quisermos obter um *DataFrame* contendo o IMC como uma nova coluna, podemos utilizar o método `assign` (sem modificar o *DataFrame* original):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "      <td>23.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "      <td>24.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "      <td>24.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura    IMC\n",
       "Ana       20    55     162  20.96\n",
       "João      19    80     178  25.25\n",
       "Maria     21    62     162  23.62\n",
       "Pedro     22    67     165  24.61\n",
       "Túlio     20    73     171  24.96"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series.assign(IMC=round(df_dict_series.eval('Peso/(Altura/100)**2'),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura\n",
       "Ana       20    55     162\n",
       "João      19    80     178\n",
       "Maria     21    62     162\n",
       "Pedro     22    67     165\n",
       "Túlio     20    73     171"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series # não modificado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se quisermos modificar o *DataFrame* para incluir a coluna IMC fazemos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dict_series['IMC']=round(df_dict_series.eval('Peso/(Altura/100)**2'),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "      <td>23.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "      <td>24.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "      <td>24.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura    IMC\n",
       "Ana       20    55     162  20.96\n",
       "João      19    80     178  25.25\n",
       "Maria     21    62     162  23.62\n",
       "Pedro     22    67     165  24.61\n",
       "Túlio     20    73     171  24.96"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series # modificado \"in-place\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Seleção de linhas de um *DataFrame*\n",
    "\n",
    "Podemos selecionar linhas de um *DataFrame* de diversas formas diferentes. Veremos agora algumas dessas formas.\n",
    "\n",
    "Diferentemente da forma de selecionar colunas, para selecionar diretamente linhas de um *DataFrame* devemos utilizar o método `loc` (fornecendo o *index*, isto é, o rótulo da linha) ou o `iloc` (fornecendo a posição da linha):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trabalharemos a seguir com um banco de dados atualizado sobre a COVID-19. Para tanto, importaremos o módulo `datetime` que nos auxiliará com datas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "dados_covid_PB = pd.read_csv('https://superset.plataformatarget.com.br/superset/explore_json/?form_data=%7B%22slice_id%22%3A1550%7D&csv=true', \n",
    "                             sep=',', index_col=0)\n",
    "\n",
    "# busca o banco na data D-1, visto que a atualização\n",
    "# ocorre em D\n",
    "ontem = (datetime.date.today() - datetime.timedelta(days=1)).strftime('%Y-%m-%d') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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>2021-09-26</th>\n",
       "      <td>441155</td>\n",
       "      <td>134</td>\n",
       "      <td>484336</td>\n",
       "      <td>333751</td>\n",
       "      <td>9294</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2021-09-26           441155         134       484336       333751   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade  \n",
       "data                                                   \n",
       "2021-09-26              9294          2.0      0.0211  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Podemos ver as informações de um único dia como argumento. Para tanto, excluímos a coluna `'Letalidade'` (valor não inteiro) e convertemos o restante para valores inteiros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "casosAcumulados     441155\n",
       "casosNovos             134\n",
       "descartados         484336\n",
       "recuperados         333751\n",
       "obitosAcumulados      9294\n",
       "obitosNovos              2\n",
       "Name: 2021-09-26, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.loc[ontem].drop('Letalidade').astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Podemos selecionar um intervalo de datas como argumento (excluindo letalidade):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>192598</td>\n",
       "      <td>1014</td>\n",
       "      <td>234215</td>\n",
       "      <td>149235</td>\n",
       "      <td>4068</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-02</th>\n",
       "      <td>193465</td>\n",
       "      <td>867</td>\n",
       "      <td>234366</td>\n",
       "      <td>149242</td>\n",
       "      <td>4082</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-03</th>\n",
       "      <td>194519</td>\n",
       "      <td>1054</td>\n",
       "      <td>234902</td>\n",
       "      <td>149248</td>\n",
       "      <td>4096</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>195482</td>\n",
       "      <td>963</td>\n",
       "      <td>235319</td>\n",
       "      <td>149792</td>\n",
       "      <td>4110</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>196542</td>\n",
       "      <td>1060</td>\n",
       "      <td>236216</td>\n",
       "      <td>150169</td>\n",
       "      <td>4123</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "2021-02-01           192598        1014       234215       149235   \n",
       "2021-02-02           193465         867       234366       149242   \n",
       "2021-02-03           194519        1054       234902       149248   \n",
       "2021-02-04           195482         963       235319       149792   \n",
       "2021-02-05           196542        1060       236216       150169   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  \n",
       "2021-02-01              4068         12.0  \n",
       "2021-02-02              4082         14.0  \n",
       "2021-02-03              4096         14.0  \n",
       "2021-02-04              4110         14.0  \n",
       "2021-02-05              4123         13.0  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.index = pd.to_datetime(dados_covid_PB.index) # Convertendo o index de string para data\n",
    "dados_covid_PB.loc[pd.date_range('2021-02-01',periods=5,freq=\"D\")].drop('Letalidade',axis=1) \n",
    "                #função pd.date_range é muito útil para criar índices a partir de datas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Podemos colocar uma lista como argumento:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>167062</td>\n",
       "      <td>578</td>\n",
       "      <td>219890</td>\n",
       "      <td>127388</td>\n",
       "      <td>3680</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>192598</td>\n",
       "      <td>1014</td>\n",
       "      <td>234215</td>\n",
       "      <td>149235</td>\n",
       "      <td>4068</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "2021-01-01           167062         578       219890       127388   \n",
       "2021-02-01           192598        1014       234215       149235   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade  \n",
       "2021-01-01              3680          8.0      0.0220  \n",
       "2021-02-01              4068         12.0      0.0211  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.loc[pd.to_datetime(['2021-01-01','2021-02-01'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Vamos agora examinar os dados da posição 100 (novamente excluindo a coluna letalidade e convertendo para inteiro):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "casosAcumulados     371133\n",
       "casosNovos            2555\n",
       "descartados         328941\n",
       "recuperados         243203\n",
       "obitosAcumulados      8316\n",
       "obitosNovos             47\n",
       "Name: 2021-06-18 00:00:00, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.iloc[100].drop('Letalidade').astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Podemos colocar um intervalo como argumento:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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>2021-08-07</th>\n",
       "      <td>424983</td>\n",
       "      <td>406</td>\n",
       "      <td>456236</td>\n",
       "      <td>298889</td>\n",
       "      <td>9049</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-06</th>\n",
       "      <td>424577</td>\n",
       "      <td>423</td>\n",
       "      <td>455493</td>\n",
       "      <td>298554</td>\n",
       "      <td>9037</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-05</th>\n",
       "      <td>424154</td>\n",
       "      <td>570</td>\n",
       "      <td>454357</td>\n",
       "      <td>297945</td>\n",
       "      <td>9023</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-04</th>\n",
       "      <td>423584</td>\n",
       "      <td>872</td>\n",
       "      <td>453146</td>\n",
       "      <td>297222</td>\n",
       "      <td>9018</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-03</th>\n",
       "      <td>422712</td>\n",
       "      <td>37</td>\n",
       "      <td>451358</td>\n",
       "      <td>296180</td>\n",
       "      <td>9011</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2021-08-07           424983         406       456236       298889   \n",
       "2021-08-06           424577         423       455493       298554   \n",
       "2021-08-05           424154         570       454357       297945   \n",
       "2021-08-04           423584         872       453146       297222   \n",
       "2021-08-03           422712          37       451358       296180   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  \n",
       "data                                       \n",
       "2021-08-07              9049           12  \n",
       "2021-08-06              9037           14  \n",
       "2021-08-05              9023            5  \n",
       "2021-08-04              9018            7  \n",
       "2021-08-03              9011            8  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.iloc[50:55].drop('Letalidade', axis=1).astype('int') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Seleção de colunas com `loc` e `iloc`\n",
    "\n",
    "Podemos selecionar colunas utilizando os métodos `loc` e `iloc` utilizando um argumento adicional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>casosNovos</th>\n",
       "      <th>obitosNovos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-26</th>\n",
       "      <td>134</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-09-25</th>\n",
       "      <td>165</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>225</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>203</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>297</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>559 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosNovos  obitosNovos\n",
       "data                               \n",
       "2021-09-26         134          2.0\n",
       "2021-09-25         165          2.0\n",
       "2021-09-24         225          3.0\n",
       "2021-09-23         203          3.0\n",
       "2021-09-22         297          6.0\n",
       "...                ...          ...\n",
       "2020-03-20           0          0.0\n",
       "2020-03-19           0          0.0\n",
       "2020-03-18           0          0.0\n",
       "2020-03-17           0          0.0\n",
       "2020-03-16           0          0.0\n",
       "\n",
       "[559 rows x 2 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.loc[:,['casosNovos','obitosNovos']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-26</th>\n",
       "      <td>9294</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-25</th>\n",
       "      <td>9292</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>9290</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>9287</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>9284</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>559 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            obitosAcumulados  obitosNovos\n",
       "data                                     \n",
       "2021-09-26              9294          2.0\n",
       "2021-09-25              9292          2.0\n",
       "2021-09-24              9290          3.0\n",
       "2021-09-23              9287          3.0\n",
       "2021-09-22              9284          6.0\n",
       "...                      ...          ...\n",
       "2020-03-20                 0          0.0\n",
       "2020-03-19                 0          0.0\n",
       "2020-03-18                 0          0.0\n",
       "2020-03-17                 0          0.0\n",
       "2020-03-16                 0          0.0\n",
       "\n",
       "[559 rows x 2 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.iloc[:,4:6] # fatiamento na coluna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Seleção de linhas e colunas específicas com `loc` e `iloc`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usando o mesmo princípio de *fatiamento* aplicado a *arrays* do numpy, podemos selecionar linhas e colunas em um intervalo específico de forma a obter uma subtabela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-23</th>\n",
       "      <td>8453</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22</th>\n",
       "      <td>8433</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>8411</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-20</th>\n",
       "      <td>8381</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-19</th>\n",
       "      <td>8348</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            obitosAcumulados  obitosNovos\n",
       "data                                     \n",
       "2021-06-23              8453         20.0\n",
       "2021-06-22              8433         22.0\n",
       "2021-06-21              8411         30.0\n",
       "2021-06-20              8381         33.0\n",
       "2021-06-19              8348         32.0"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.iloc[95:100,4:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neste exemplo um pouco mais complexo, buscamos casos novos e óbitos novos em um período específico e ordenamos a tabela da data mais recente para a mais antiga."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>casosNovos</th>\n",
       "      <th>obitosNovos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-10</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09</th>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosNovos  obitosNovos\n",
       "2020-04-10           6          0.0\n",
       "2020-04-09          24          4.0\n",
       "2020-04-08          14          3.0\n",
       "2020-04-07           5          0.0\n",
       "2020-04-06           1          0.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.loc[pd.date_range('2020-04-06','2020-04-10'),['casosNovos','obitosNovos']].sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Suponha que o peso de Ana foi medido corretamente, mas registrado de maneira errônea no *DataFrame* `df_dict_series` como 55."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>162</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "      <td>23.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "      <td>24.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "      <td>24.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura    IMC\n",
       "Ana       20    55     162  20.96\n",
       "João      19    80     178  25.25\n",
       "Maria     21    62     162  23.62\n",
       "Pedro     22    67     165  24.61\n",
       "Túlio     20    73     171  24.96"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supondo que, na realidade, o valor é 65, alteramos a entrada específica com um simples `loc`. Em seguida, atualizamos a tabela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "df_dict_series.loc['Ana','Peso'] = 65\n",
    "\n",
    "df_dict_series = df_dict_series.assign(IMC=round(df_dict_series.eval('Peso/(Altura/100)**2'),2)) # O IMC mudou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>162</td>\n",
       "      <td>24.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "      <td>23.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "      <td>24.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "      <td>24.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura    IMC\n",
       "Ana       20    65     162  24.77\n",
       "João      19    80     178  25.25\n",
       "Maria     21    62     162  23.62\n",
       "Pedro     22    67     165  24.61\n",
       "Túlio     20    73     171  24.96"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Seleção de linhas através de critérios lógicos ou funções\n",
    "\n",
    "Vamos selecionar quais os dias em que houve mais de 40 mortes registradas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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>2021-06-18</th>\n",
       "      <td>371133</td>\n",
       "      <td>2555</td>\n",
       "      <td>328941</td>\n",
       "      <td>243203</td>\n",
       "      <td>8316</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-11</th>\n",
       "      <td>354715</td>\n",
       "      <td>2451</td>\n",
       "      <td>322301</td>\n",
       "      <td>235778</td>\n",
       "      <td>8058</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-10</th>\n",
       "      <td>352264</td>\n",
       "      <td>3911</td>\n",
       "      <td>231258</td>\n",
       "      <td>235329</td>\n",
       "      <td>8003</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-27</th>\n",
       "      <td>289319</td>\n",
       "      <td>1037</td>\n",
       "      <td>290111</td>\n",
       "      <td>204340</td>\n",
       "      <td>6722</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19</th>\n",
       "      <td>280967</td>\n",
       "      <td>904</td>\n",
       "      <td>286168</td>\n",
       "      <td>194644</td>\n",
       "      <td>6487</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-14</th>\n",
       "      <td>275301</td>\n",
       "      <td>1218</td>\n",
       "      <td>282121</td>\n",
       "      <td>193451</td>\n",
       "      <td>6316</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-12</th>\n",
       "      <td>272811</td>\n",
       "      <td>1486</td>\n",
       "      <td>279801</td>\n",
       "      <td>193188</td>\n",
       "      <td>6234</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>267008</td>\n",
       "      <td>1551</td>\n",
       "      <td>274623</td>\n",
       "      <td>189753</td>\n",
       "      <td>6050</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>265457</td>\n",
       "      <td>817</td>\n",
       "      <td>273026</td>\n",
       "      <td>186879</td>\n",
       "      <td>6001</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>264640</td>\n",
       "      <td>1059</td>\n",
       "      <td>272719</td>\n",
       "      <td>186670</td>\n",
       "      <td>5949</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-03</th>\n",
       "      <td>262500</td>\n",
       "      <td>789</td>\n",
       "      <td>272207</td>\n",
       "      <td>186334</td>\n",
       "      <td>5866</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>260484</td>\n",
       "      <td>1311</td>\n",
       "      <td>271069</td>\n",
       "      <td>186084</td>\n",
       "      <td>5791</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>259173</td>\n",
       "      <td>1553</td>\n",
       "      <td>270560</td>\n",
       "      <td>183668</td>\n",
       "      <td>5744</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-30</th>\n",
       "      <td>257620</td>\n",
       "      <td>1150</td>\n",
       "      <td>269399</td>\n",
       "      <td>183369</td>\n",
       "      <td>5671</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-29</th>\n",
       "      <td>256470</td>\n",
       "      <td>968</td>\n",
       "      <td>268985</td>\n",
       "      <td>183028</td>\n",
       "      <td>5601</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-28</th>\n",
       "      <td>255502</td>\n",
       "      <td>1203</td>\n",
       "      <td>268866</td>\n",
       "      <td>182873</td>\n",
       "      <td>5552</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-27</th>\n",
       "      <td>254299</td>\n",
       "      <td>1410</td>\n",
       "      <td>268309</td>\n",
       "      <td>182680</td>\n",
       "      <td>5509</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-26</th>\n",
       "      <td>252889</td>\n",
       "      <td>1611</td>\n",
       "      <td>266785</td>\n",
       "      <td>182404</td>\n",
       "      <td>5452</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-25</th>\n",
       "      <td>251278</td>\n",
       "      <td>1672</td>\n",
       "      <td>265533</td>\n",
       "      <td>180125</td>\n",
       "      <td>5404</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-24</th>\n",
       "      <td>249606</td>\n",
       "      <td>1154</td>\n",
       "      <td>264150</td>\n",
       "      <td>177812</td>\n",
       "      <td>5354</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-23</th>\n",
       "      <td>248452</td>\n",
       "      <td>1140</td>\n",
       "      <td>264146</td>\n",
       "      <td>177591</td>\n",
       "      <td>5313</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-20</th>\n",
       "      <td>245564</td>\n",
       "      <td>1300</td>\n",
       "      <td>264128</td>\n",
       "      <td>174980</td>\n",
       "      <td>5212</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-19</th>\n",
       "      <td>244264</td>\n",
       "      <td>1269</td>\n",
       "      <td>264122</td>\n",
       "      <td>174769</td>\n",
       "      <td>5167</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-18</th>\n",
       "      <td>242995</td>\n",
       "      <td>1403</td>\n",
       "      <td>258188</td>\n",
       "      <td>172789</td>\n",
       "      <td>5123</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-17</th>\n",
       "      <td>241592</td>\n",
       "      <td>1194</td>\n",
       "      <td>256574</td>\n",
       "      <td>172492</td>\n",
       "      <td>5080</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-16</th>\n",
       "      <td>240398</td>\n",
       "      <td>804</td>\n",
       "      <td>255491</td>\n",
       "      <td>172218</td>\n",
       "      <td>5036</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-15</th>\n",
       "      <td>239594</td>\n",
       "      <td>1025</td>\n",
       "      <td>255162</td>\n",
       "      <td>171911</td>\n",
       "      <td>4992</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-10</th>\n",
       "      <td>232771</td>\n",
       "      <td>1365</td>\n",
       "      <td>251742</td>\n",
       "      <td>170510</td>\n",
       "      <td>4797</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-04</th>\n",
       "      <td>225672</td>\n",
       "      <td>1385</td>\n",
       "      <td>245919</td>\n",
       "      <td>164712</td>\n",
       "      <td>4612</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-21</th>\n",
       "      <td>68844</td>\n",
       "      <td>1164</td>\n",
       "      <td>78605</td>\n",
       "      <td>25028</td>\n",
       "      <td>1558</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-15</th>\n",
       "      <td>63939</td>\n",
       "      <td>1477</td>\n",
       "      <td>74399</td>\n",
       "      <td>23695</td>\n",
       "      <td>1383</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-02</th>\n",
       "      <td>49536</td>\n",
       "      <td>1361</td>\n",
       "      <td>48272</td>\n",
       "      <td>16349</td>\n",
       "      <td>1044</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>46957</td>\n",
       "      <td>1900</td>\n",
       "      <td>43070</td>\n",
       "      <td>14930</td>\n",
       "      <td>977</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2021-06-18           371133        2555       328941       243203   \n",
       "2021-06-11           354715        2451       322301       235778   \n",
       "2021-06-10           352264        3911       231258       235329   \n",
       "2021-04-27           289319        1037       290111       204340   \n",
       "2021-04-19           280967         904       286168       194644   \n",
       "2021-04-14           275301        1218       282121       193451   \n",
       "2021-04-12           272811        1486       279801       193188   \n",
       "2021-04-07           267008        1551       274623       189753   \n",
       "2021-04-06           265457         817       273026       186879   \n",
       "2021-04-05           264640        1059       272719       186670   \n",
       "2021-04-03           262500         789       272207       186334   \n",
       "2021-04-01           260484        1311       271069       186084   \n",
       "2021-03-31           259173        1553       270560       183668   \n",
       "2021-03-30           257620        1150       269399       183369   \n",
       "2021-03-29           256470         968       268985       183028   \n",
       "2021-03-28           255502        1203       268866       182873   \n",
       "2021-03-27           254299        1410       268309       182680   \n",
       "2021-03-26           252889        1611       266785       182404   \n",
       "2021-03-25           251278        1672       265533       180125   \n",
       "2021-03-24           249606        1154       264150       177812   \n",
       "2021-03-23           248452        1140       264146       177591   \n",
       "2021-03-20           245564        1300       264128       174980   \n",
       "2021-03-19           244264        1269       264122       174769   \n",
       "2021-03-18           242995        1403       258188       172789   \n",
       "2021-03-17           241592        1194       256574       172492   \n",
       "2021-03-16           240398         804       255491       172218   \n",
       "2021-03-15           239594        1025       255162       171911   \n",
       "2021-03-10           232771        1365       251742       170510   \n",
       "2021-03-04           225672        1385       245919       164712   \n",
       "2020-07-21            68844        1164        78605        25028   \n",
       "2020-07-15            63939        1477        74399        23695   \n",
       "2020-07-02            49536        1361        48272        16349   \n",
       "2020-06-30            46957        1900        43070        14930   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade  \n",
       "data                                                   \n",
       "2021-06-18              8316         47.0      0.0224  \n",
       "2021-06-11              8058         55.0      0.0227  \n",
       "2021-06-10              8003         46.0      0.0227  \n",
       "2021-04-27              6722         46.0      0.0232  \n",
       "2021-04-19              6487         43.0      0.0231  \n",
       "2021-04-14              6316         42.0      0.0229  \n",
       "2021-04-12              6234         58.0      0.0229  \n",
       "2021-04-07              6050         49.0      0.0227  \n",
       "2021-04-06              6001         52.0      0.0226  \n",
       "2021-04-05              5949         43.0      0.0225  \n",
       "2021-04-03              5866         46.0      0.0223  \n",
       "2021-04-01              5791         47.0      0.0222  \n",
       "2021-03-31              5744         73.0      0.0222  \n",
       "2021-03-30              5671         70.0      0.0220  \n",
       "2021-03-29              5601         49.0      0.0218  \n",
       "2021-03-28              5552         43.0      0.0217  \n",
       "2021-03-27              5509         57.0      0.0217  \n",
       "2021-03-26              5452         48.0      0.0216  \n",
       "2021-03-25              5404         50.0      0.0215  \n",
       "2021-03-24              5354         41.0      0.0214  \n",
       "2021-03-23              5313         41.0      0.0214  \n",
       "2021-03-20              5212         45.0      0.0212  \n",
       "2021-03-19              5167         44.0      0.0212  \n",
       "2021-03-18              5123         43.0      0.0211  \n",
       "2021-03-17              5080         44.0      0.0210  \n",
       "2021-03-16              5036         44.0      0.0209  \n",
       "2021-03-15              4992         59.0      0.0208  \n",
       "2021-03-10              4797         50.0      0.0206  \n",
       "2021-03-04              4612         54.0      0.0204  \n",
       "2020-07-21              1558         41.0      0.0226  \n",
       "2020-07-15              1383         41.0      0.0216  \n",
       "2020-07-02              1044         42.0      0.0211  \n",
       "2020-06-30               977         46.0      0.0208  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.loc[dados_covid_PB['obitosNovos']>40]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Selecionando os dias com mais de 25 óbitos e mais de 1500 casos novos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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>2021-07-02</th>\n",
       "      <td>398440</td>\n",
       "      <td>1536</td>\n",
       "      <td>332530</td>\n",
       "      <td>262757</td>\n",
       "      <td>8654</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-24</th>\n",
       "      <td>385018</td>\n",
       "      <td>2391</td>\n",
       "      <td>332313</td>\n",
       "      <td>250519</td>\n",
       "      <td>8479</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>378064</td>\n",
       "      <td>2114</td>\n",
       "      <td>330969</td>\n",
       "      <td>249135</td>\n",
       "      <td>8411</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-20</th>\n",
       "      <td>375950</td>\n",
       "      <td>2178</td>\n",
       "      <td>330721</td>\n",
       "      <td>248667</td>\n",
       "      <td>8381</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-19</th>\n",
       "      <td>373772</td>\n",
       "      <td>2639</td>\n",
       "      <td>329903</td>\n",
       "      <td>248304</td>\n",
       "      <td>8348</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-18</th>\n",
       "      <td>371133</td>\n",
       "      <td>2555</td>\n",
       "      <td>328941</td>\n",
       "      <td>243203</td>\n",
       "      <td>8316</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-17</th>\n",
       "      <td>368578</td>\n",
       "      <td>2457</td>\n",
       "      <td>327929</td>\n",
       "      <td>242820</td>\n",
       "      <td>8269</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16</th>\n",
       "      <td>366121</td>\n",
       "      <td>2328</td>\n",
       "      <td>326517</td>\n",
       "      <td>242184</td>\n",
       "      <td>8237</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-15</th>\n",
       "      <td>363793</td>\n",
       "      <td>2358</td>\n",
       "      <td>325032</td>\n",
       "      <td>241749</td>\n",
       "      <td>8197</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14</th>\n",
       "      <td>361435</td>\n",
       "      <td>2575</td>\n",
       "      <td>324438</td>\n",
       "      <td>241293</td>\n",
       "      <td>8158</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-13</th>\n",
       "      <td>358860</td>\n",
       "      <td>2034</td>\n",
       "      <td>324327</td>\n",
       "      <td>236555</td>\n",
       "      <td>8128</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-12</th>\n",
       "      <td>356826</td>\n",
       "      <td>2111</td>\n",
       "      <td>323444</td>\n",
       "      <td>236186</td>\n",
       "      <td>8095</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-11</th>\n",
       "      <td>354715</td>\n",
       "      <td>2451</td>\n",
       "      <td>322301</td>\n",
       "      <td>235778</td>\n",
       "      <td>8058</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-10</th>\n",
       "      <td>352264</td>\n",
       "      <td>3911</td>\n",
       "      <td>231258</td>\n",
       "      <td>235329</td>\n",
       "      <td>8003</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-06</th>\n",
       "      <td>344174</td>\n",
       "      <td>1982</td>\n",
       "      <td>319055</td>\n",
       "      <td>230002</td>\n",
       "      <td>7849</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-05</th>\n",
       "      <td>342192</td>\n",
       "      <td>1992</td>\n",
       "      <td>318096</td>\n",
       "      <td>229790</td>\n",
       "      <td>7820</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-04</th>\n",
       "      <td>340200</td>\n",
       "      <td>2421</td>\n",
       "      <td>317390</td>\n",
       "      <td>229489</td>\n",
       "      <td>7791</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-03</th>\n",
       "      <td>337779</td>\n",
       "      <td>2660</td>\n",
       "      <td>316011</td>\n",
       "      <td>229062</td>\n",
       "      <td>7765</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-02</th>\n",
       "      <td>335119</td>\n",
       "      <td>2403</td>\n",
       "      <td>314538</td>\n",
       "      <td>225278</td>\n",
       "      <td>7735</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>332716</td>\n",
       "      <td>1751</td>\n",
       "      <td>314520</td>\n",
       "      <td>224912</td>\n",
       "      <td>7703</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-29</th>\n",
       "      <td>328771</td>\n",
       "      <td>2429</td>\n",
       "      <td>312011</td>\n",
       "      <td>224255</td>\n",
       "      <td>7617</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-28</th>\n",
       "      <td>326342</td>\n",
       "      <td>1917</td>\n",
       "      <td>311013</td>\n",
       "      <td>223896</td>\n",
       "      <td>7589</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-27</th>\n",
       "      <td>324425</td>\n",
       "      <td>1887</td>\n",
       "      <td>310639</td>\n",
       "      <td>223537</td>\n",
       "      <td>7556</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-26</th>\n",
       "      <td>322538</td>\n",
       "      <td>2262</td>\n",
       "      <td>309492</td>\n",
       "      <td>221134</td>\n",
       "      <td>7520</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-25</th>\n",
       "      <td>320276</td>\n",
       "      <td>2015</td>\n",
       "      <td>308275</td>\n",
       "      <td>220621</td>\n",
       "      <td>7484</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>267008</td>\n",
       "      <td>1551</td>\n",
       "      <td>274623</td>\n",
       "      <td>189753</td>\n",
       "      <td>6050</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>259173</td>\n",
       "      <td>1553</td>\n",
       "      <td>270560</td>\n",
       "      <td>183668</td>\n",
       "      <td>5744</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-26</th>\n",
       "      <td>252889</td>\n",
       "      <td>1611</td>\n",
       "      <td>266785</td>\n",
       "      <td>182404</td>\n",
       "      <td>5452</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-25</th>\n",
       "      <td>251278</td>\n",
       "      <td>1672</td>\n",
       "      <td>265533</td>\n",
       "      <td>180125</td>\n",
       "      <td>5404</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-13</th>\n",
       "      <td>237313</td>\n",
       "      <td>1694</td>\n",
       "      <td>254238</td>\n",
       "      <td>171130</td>\n",
       "      <td>4896</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>85760</td>\n",
       "      <td>1549</td>\n",
       "      <td>106500</td>\n",
       "      <td>38554</td>\n",
       "      <td>1901</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>82794</td>\n",
       "      <td>1686</td>\n",
       "      <td>96753</td>\n",
       "      <td>35971</td>\n",
       "      <td>1811</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>73104</td>\n",
       "      <td>2132</td>\n",
       "      <td>84047</td>\n",
       "      <td>28566</td>\n",
       "      <td>1618</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-10</th>\n",
       "      <td>59118</td>\n",
       "      <td>1504</td>\n",
       "      <td>69567</td>\n",
       "      <td>21481</td>\n",
       "      <td>1229</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-08</th>\n",
       "      <td>56344</td>\n",
       "      <td>1542</td>\n",
       "      <td>67549</td>\n",
       "      <td>19999</td>\n",
       "      <td>1171</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-07</th>\n",
       "      <td>54802</td>\n",
       "      <td>1651</td>\n",
       "      <td>64933</td>\n",
       "      <td>19373</td>\n",
       "      <td>1145</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>46957</td>\n",
       "      <td>1900</td>\n",
       "      <td>43070</td>\n",
       "      <td>14930</td>\n",
       "      <td>977</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-09</th>\n",
       "      <td>22452</td>\n",
       "      <td>1501</td>\n",
       "      <td>20650</td>\n",
       "      <td>4671</td>\n",
       "      <td>534</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2021-07-02           398440        1536       332530       262757   \n",
       "2021-06-24           385018        2391       332313       250519   \n",
       "2021-06-21           378064        2114       330969       249135   \n",
       "2021-06-20           375950        2178       330721       248667   \n",
       "2021-06-19           373772        2639       329903       248304   \n",
       "2021-06-18           371133        2555       328941       243203   \n",
       "2021-06-17           368578        2457       327929       242820   \n",
       "2021-06-16           366121        2328       326517       242184   \n",
       "2021-06-15           363793        2358       325032       241749   \n",
       "2021-06-14           361435        2575       324438       241293   \n",
       "2021-06-13           358860        2034       324327       236555   \n",
       "2021-06-12           356826        2111       323444       236186   \n",
       "2021-06-11           354715        2451       322301       235778   \n",
       "2021-06-10           352264        3911       231258       235329   \n",
       "2021-06-06           344174        1982       319055       230002   \n",
       "2021-06-05           342192        1992       318096       229790   \n",
       "2021-06-04           340200        2421       317390       229489   \n",
       "2021-06-03           337779        2660       316011       229062   \n",
       "2021-06-02           335119        2403       314538       225278   \n",
       "2021-06-01           332716        1751       314520       224912   \n",
       "2021-05-29           328771        2429       312011       224255   \n",
       "2021-05-28           326342        1917       311013       223896   \n",
       "2021-05-27           324425        1887       310639       223537   \n",
       "2021-05-26           322538        2262       309492       221134   \n",
       "2021-05-25           320276        2015       308275       220621   \n",
       "2021-04-07           267008        1551       274623       189753   \n",
       "2021-03-31           259173        1553       270560       183668   \n",
       "2021-03-26           252889        1611       266785       182404   \n",
       "2021-03-25           251278        1672       265533       180125   \n",
       "2021-03-13           237313        1694       254238       171130   \n",
       "2020-08-04            85760        1549       106500        38554   \n",
       "2020-07-31            82794        1686        96753        35971   \n",
       "2020-07-23            73104        2132        84047        28566   \n",
       "2020-07-10            59118        1504        69567        21481   \n",
       "2020-07-08            56344        1542        67549        19999   \n",
       "2020-07-07            54802        1651        64933        19373   \n",
       "2020-06-30            46957        1900        43070        14930   \n",
       "2020-06-09            22452        1501        20650         4671   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade  \n",
       "data                                                   \n",
       "2021-07-02              8654         26.0      0.0217  \n",
       "2021-06-24              8479         26.0      0.0220  \n",
       "2021-06-21              8411         30.0      0.0222  \n",
       "2021-06-20              8381         33.0      0.0223  \n",
       "2021-06-19              8348         32.0      0.0223  \n",
       "2021-06-18              8316         47.0      0.0224  \n",
       "2021-06-17              8269         32.0      0.0224  \n",
       "2021-06-16              8237         40.0      0.0225  \n",
       "2021-06-15              8197         39.0      0.0225  \n",
       "2021-06-14              8158         30.0      0.0226  \n",
       "2021-06-13              8128         33.0      0.0226  \n",
       "2021-06-12              8095         37.0      0.0227  \n",
       "2021-06-11              8058         55.0      0.0227  \n",
       "2021-06-10              8003         46.0      0.0227  \n",
       "2021-06-06              7849         29.0      0.0228  \n",
       "2021-06-05              7820         29.0      0.0229  \n",
       "2021-06-04              7791         26.0      0.0229  \n",
       "2021-06-03              7765         30.0      0.0230  \n",
       "2021-06-02              7735         32.0      0.0231  \n",
       "2021-06-01              7703         31.0      0.0232  \n",
       "2021-05-29              7617         28.0      0.0232  \n",
       "2021-05-28              7589         33.0      0.0233  \n",
       "2021-05-27              7556         36.0      0.0233  \n",
       "2021-05-26              7520         36.0      0.0233  \n",
       "2021-05-25              7484         36.0      0.0234  \n",
       "2021-04-07              6050         49.0      0.0227  \n",
       "2021-03-31              5744         73.0      0.0222  \n",
       "2021-03-26              5452         48.0      0.0216  \n",
       "2021-03-25              5404         50.0      0.0215  \n",
       "2021-03-13              4896         31.0      0.0206  \n",
       "2020-08-04              1901         31.0      0.0222  \n",
       "2020-07-31              1811         26.0      0.0219  \n",
       "2020-07-23              1618         37.0      0.0221  \n",
       "2020-07-10              1229         33.0      0.0208  \n",
       "2020-07-08              1171         26.0      0.0208  \n",
       "2020-07-07              1145         27.0      0.0209  \n",
       "2020-06-30               977         46.0      0.0208  \n",
       "2020-06-09               534         27.0      0.0238  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.loc[(dados_covid_PB.obitosNovos > 25) & (dados_covid_PB.casosNovos>1500)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Obs**.: Note que podemos utilizar o nome da coluna como um atributo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Vamos inserir uma coluna sobrenome no `df_dict_series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>IMC</th>\n",
       "      <th>Sobrenome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>162</td>\n",
       "      <td>24.77</td>\n",
       "      <td>Silva</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "      <td>25.25</td>\n",
       "      <td>PraDo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>162</td>\n",
       "      <td>23.62</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "      <td>24.61</td>\n",
       "      <td>MachadO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Túlio</th>\n",
       "      <td>20</td>\n",
       "      <td>73</td>\n",
       "      <td>171</td>\n",
       "      <td>24.96</td>\n",
       "      <td>Coutinho</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura    IMC Sobrenome\n",
       "Ana       20    65     162  24.77     Silva\n",
       "João      19    80     178  25.25     PraDo\n",
       "Maria     21    62     162  23.62     Sales\n",
       "Pedro     22    67     165  24.61   MachadO\n",
       "Túlio     20    73     171  24.96  Coutinho"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_series['Sobrenome'] = ['Silva', 'PraDo', 'Sales', 'MachadO', 'Coutinho']\n",
    "df_dict_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Vamos encontrar as linhas cujo sobrenome termina em \"do\". Para tanto, note que a função abaixo retorna `True` se o final é \"do\" e `False` caso contrário.\n",
    "\n",
    "```python\n",
    "def verifica_final_do(palavra):\n",
    "    return palavra.lower()[-2:] == 'do'\n",
    "```\n",
    "**Obs**.: Note que convertemos tudo para minúsculo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Agora vamos utilizar essa função para alcançar nosso objetivo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana      False\n",
       "João      True\n",
       "Maria    False\n",
       "Pedro     True\n",
       "Túlio    False\n",
       "Name: Sobrenome, dtype: bool"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'map' aplica a função lambda a cada elemento da *Series*\n",
    "df_dict_series['Sobrenome'].map(lambda palavra: palavra.lower()[-2:]=='do') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>Idade</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>IMC</th>\n",
       "      <th>Sobrenome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>João</th>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "      <td>25.25</td>\n",
       "      <td>PraDo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>165</td>\n",
       "      <td>24.61</td>\n",
       "      <td>MachadO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idade  Peso  Altura    IMC Sobrenome\n",
       "João      19    80     178  25.25     PraDo\n",
       "Pedro     22    67     165  24.61   MachadO"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# procurando no df inteiro\n",
    "df_dict_series.loc[df_dict_series['Sobrenome'].map(lambda palavra: palavra.lower()[-2:]=='do')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Vamos selecionar as linhas do mês 2 (fevereiro) usando `index.month`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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>2021-02-28</th>\n",
       "      <td>221115</td>\n",
       "      <td>1392</td>\n",
       "      <td>245686</td>\n",
       "      <td>158304</td>\n",
       "      <td>4496</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-27</th>\n",
       "      <td>219723</td>\n",
       "      <td>1416</td>\n",
       "      <td>245121</td>\n",
       "      <td>158132</td>\n",
       "      <td>4471</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-26</th>\n",
       "      <td>218307</td>\n",
       "      <td>1186</td>\n",
       "      <td>244356</td>\n",
       "      <td>157849</td>\n",
       "      <td>4451</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-25</th>\n",
       "      <td>217121</td>\n",
       "      <td>1594</td>\n",
       "      <td>243890</td>\n",
       "      <td>157586</td>\n",
       "      <td>4433</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-24</th>\n",
       "      <td>215527</td>\n",
       "      <td>1320</td>\n",
       "      <td>243253</td>\n",
       "      <td>157329</td>\n",
       "      <td>4419</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2021-02-28           221115        1392       245686       158304   \n",
       "2021-02-27           219723        1416       245121       158132   \n",
       "2021-02-26           218307        1186       244356       157849   \n",
       "2021-02-25           217121        1594       243890       157586   \n",
       "2021-02-24           215527        1320       243253       157329   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade  \n",
       "data                                                   \n",
       "2021-02-28              4496         25.0      0.0203  \n",
       "2021-02-27              4471         20.0      0.0203  \n",
       "2021-02-26              4451         18.0      0.0204  \n",
       "2021-02-25              4433         14.0      0.0204  \n",
       "2021-02-24              4419         15.0      0.0205  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.loc[dados_covid_PB.index.month==2].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Seleção de linhas com o método *query*\n",
    "\n",
    "Similarmente ao método `eval`, ao utilizarmos `query`, podemos criar expressões lógicas a partir de nomes das colunas do *DataFrame*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Assim, podemos reescrever o código\n",
    "\n",
    "```python\n",
    "dados_covid_PB.loc[(dados_covid_PB.obitosNovos>25) & \n",
    "                   (dados_covid_PB.casosNovos>1500)]\n",
    "```\n",
    "como"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>casosAcumulados</th>\n",
       "      <th>casosNovos</th>\n",
       "      <th>descartados</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>obitosAcumulados</th>\n",
       "      <th>obitosNovos</th>\n",
       "      <th>Letalidade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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>2021-07-02</th>\n",
       "      <td>398440</td>\n",
       "      <td>1536</td>\n",
       "      <td>332530</td>\n",
       "      <td>262757</td>\n",
       "      <td>8654</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-24</th>\n",
       "      <td>385018</td>\n",
       "      <td>2391</td>\n",
       "      <td>332313</td>\n",
       "      <td>250519</td>\n",
       "      <td>8479</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>378064</td>\n",
       "      <td>2114</td>\n",
       "      <td>330969</td>\n",
       "      <td>249135</td>\n",
       "      <td>8411</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-20</th>\n",
       "      <td>375950</td>\n",
       "      <td>2178</td>\n",
       "      <td>330721</td>\n",
       "      <td>248667</td>\n",
       "      <td>8381</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-19</th>\n",
       "      <td>373772</td>\n",
       "      <td>2639</td>\n",
       "      <td>329903</td>\n",
       "      <td>248304</td>\n",
       "      <td>8348</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-18</th>\n",
       "      <td>371133</td>\n",
       "      <td>2555</td>\n",
       "      <td>328941</td>\n",
       "      <td>243203</td>\n",
       "      <td>8316</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-17</th>\n",
       "      <td>368578</td>\n",
       "      <td>2457</td>\n",
       "      <td>327929</td>\n",
       "      <td>242820</td>\n",
       "      <td>8269</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16</th>\n",
       "      <td>366121</td>\n",
       "      <td>2328</td>\n",
       "      <td>326517</td>\n",
       "      <td>242184</td>\n",
       "      <td>8237</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-15</th>\n",
       "      <td>363793</td>\n",
       "      <td>2358</td>\n",
       "      <td>325032</td>\n",
       "      <td>241749</td>\n",
       "      <td>8197</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14</th>\n",
       "      <td>361435</td>\n",
       "      <td>2575</td>\n",
       "      <td>324438</td>\n",
       "      <td>241293</td>\n",
       "      <td>8158</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-13</th>\n",
       "      <td>358860</td>\n",
       "      <td>2034</td>\n",
       "      <td>324327</td>\n",
       "      <td>236555</td>\n",
       "      <td>8128</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-12</th>\n",
       "      <td>356826</td>\n",
       "      <td>2111</td>\n",
       "      <td>323444</td>\n",
       "      <td>236186</td>\n",
       "      <td>8095</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-11</th>\n",
       "      <td>354715</td>\n",
       "      <td>2451</td>\n",
       "      <td>322301</td>\n",
       "      <td>235778</td>\n",
       "      <td>8058</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-10</th>\n",
       "      <td>352264</td>\n",
       "      <td>3911</td>\n",
       "      <td>231258</td>\n",
       "      <td>235329</td>\n",
       "      <td>8003</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-06</th>\n",
       "      <td>344174</td>\n",
       "      <td>1982</td>\n",
       "      <td>319055</td>\n",
       "      <td>230002</td>\n",
       "      <td>7849</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-05</th>\n",
       "      <td>342192</td>\n",
       "      <td>1992</td>\n",
       "      <td>318096</td>\n",
       "      <td>229790</td>\n",
       "      <td>7820</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-04</th>\n",
       "      <td>340200</td>\n",
       "      <td>2421</td>\n",
       "      <td>317390</td>\n",
       "      <td>229489</td>\n",
       "      <td>7791</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-03</th>\n",
       "      <td>337779</td>\n",
       "      <td>2660</td>\n",
       "      <td>316011</td>\n",
       "      <td>229062</td>\n",
       "      <td>7765</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-02</th>\n",
       "      <td>335119</td>\n",
       "      <td>2403</td>\n",
       "      <td>314538</td>\n",
       "      <td>225278</td>\n",
       "      <td>7735</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>332716</td>\n",
       "      <td>1751</td>\n",
       "      <td>314520</td>\n",
       "      <td>224912</td>\n",
       "      <td>7703</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-29</th>\n",
       "      <td>328771</td>\n",
       "      <td>2429</td>\n",
       "      <td>312011</td>\n",
       "      <td>224255</td>\n",
       "      <td>7617</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-28</th>\n",
       "      <td>326342</td>\n",
       "      <td>1917</td>\n",
       "      <td>311013</td>\n",
       "      <td>223896</td>\n",
       "      <td>7589</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-27</th>\n",
       "      <td>324425</td>\n",
       "      <td>1887</td>\n",
       "      <td>310639</td>\n",
       "      <td>223537</td>\n",
       "      <td>7556</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-26</th>\n",
       "      <td>322538</td>\n",
       "      <td>2262</td>\n",
       "      <td>309492</td>\n",
       "      <td>221134</td>\n",
       "      <td>7520</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-25</th>\n",
       "      <td>320276</td>\n",
       "      <td>2015</td>\n",
       "      <td>308275</td>\n",
       "      <td>220621</td>\n",
       "      <td>7484</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>267008</td>\n",
       "      <td>1551</td>\n",
       "      <td>274623</td>\n",
       "      <td>189753</td>\n",
       "      <td>6050</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>259173</td>\n",
       "      <td>1553</td>\n",
       "      <td>270560</td>\n",
       "      <td>183668</td>\n",
       "      <td>5744</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-26</th>\n",
       "      <td>252889</td>\n",
       "      <td>1611</td>\n",
       "      <td>266785</td>\n",
       "      <td>182404</td>\n",
       "      <td>5452</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-25</th>\n",
       "      <td>251278</td>\n",
       "      <td>1672</td>\n",
       "      <td>265533</td>\n",
       "      <td>180125</td>\n",
       "      <td>5404</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-13</th>\n",
       "      <td>237313</td>\n",
       "      <td>1694</td>\n",
       "      <td>254238</td>\n",
       "      <td>171130</td>\n",
       "      <td>4896</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>85760</td>\n",
       "      <td>1549</td>\n",
       "      <td>106500</td>\n",
       "      <td>38554</td>\n",
       "      <td>1901</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>82794</td>\n",
       "      <td>1686</td>\n",
       "      <td>96753</td>\n",
       "      <td>35971</td>\n",
       "      <td>1811</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>73104</td>\n",
       "      <td>2132</td>\n",
       "      <td>84047</td>\n",
       "      <td>28566</td>\n",
       "      <td>1618</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-10</th>\n",
       "      <td>59118</td>\n",
       "      <td>1504</td>\n",
       "      <td>69567</td>\n",
       "      <td>21481</td>\n",
       "      <td>1229</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-08</th>\n",
       "      <td>56344</td>\n",
       "      <td>1542</td>\n",
       "      <td>67549</td>\n",
       "      <td>19999</td>\n",
       "      <td>1171</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-07</th>\n",
       "      <td>54802</td>\n",
       "      <td>1651</td>\n",
       "      <td>64933</td>\n",
       "      <td>19373</td>\n",
       "      <td>1145</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>46957</td>\n",
       "      <td>1900</td>\n",
       "      <td>43070</td>\n",
       "      <td>14930</td>\n",
       "      <td>977</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-09</th>\n",
       "      <td>22452</td>\n",
       "      <td>1501</td>\n",
       "      <td>20650</td>\n",
       "      <td>4671</td>\n",
       "      <td>534</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casosAcumulados  casosNovos  descartados  recuperados  \\\n",
       "data                                                                \n",
       "2021-07-02           398440        1536       332530       262757   \n",
       "2021-06-24           385018        2391       332313       250519   \n",
       "2021-06-21           378064        2114       330969       249135   \n",
       "2021-06-20           375950        2178       330721       248667   \n",
       "2021-06-19           373772        2639       329903       248304   \n",
       "2021-06-18           371133        2555       328941       243203   \n",
       "2021-06-17           368578        2457       327929       242820   \n",
       "2021-06-16           366121        2328       326517       242184   \n",
       "2021-06-15           363793        2358       325032       241749   \n",
       "2021-06-14           361435        2575       324438       241293   \n",
       "2021-06-13           358860        2034       324327       236555   \n",
       "2021-06-12           356826        2111       323444       236186   \n",
       "2021-06-11           354715        2451       322301       235778   \n",
       "2021-06-10           352264        3911       231258       235329   \n",
       "2021-06-06           344174        1982       319055       230002   \n",
       "2021-06-05           342192        1992       318096       229790   \n",
       "2021-06-04           340200        2421       317390       229489   \n",
       "2021-06-03           337779        2660       316011       229062   \n",
       "2021-06-02           335119        2403       314538       225278   \n",
       "2021-06-01           332716        1751       314520       224912   \n",
       "2021-05-29           328771        2429       312011       224255   \n",
       "2021-05-28           326342        1917       311013       223896   \n",
       "2021-05-27           324425        1887       310639       223537   \n",
       "2021-05-26           322538        2262       309492       221134   \n",
       "2021-05-25           320276        2015       308275       220621   \n",
       "2021-04-07           267008        1551       274623       189753   \n",
       "2021-03-31           259173        1553       270560       183668   \n",
       "2021-03-26           252889        1611       266785       182404   \n",
       "2021-03-25           251278        1672       265533       180125   \n",
       "2021-03-13           237313        1694       254238       171130   \n",
       "2020-08-04            85760        1549       106500        38554   \n",
       "2020-07-31            82794        1686        96753        35971   \n",
       "2020-07-23            73104        2132        84047        28566   \n",
       "2020-07-10            59118        1504        69567        21481   \n",
       "2020-07-08            56344        1542        67549        19999   \n",
       "2020-07-07            54802        1651        64933        19373   \n",
       "2020-06-30            46957        1900        43070        14930   \n",
       "2020-06-09            22452        1501        20650         4671   \n",
       "\n",
       "            obitosAcumulados  obitosNovos  Letalidade  \n",
       "data                                                   \n",
       "2021-07-02              8654         26.0      0.0217  \n",
       "2021-06-24              8479         26.0      0.0220  \n",
       "2021-06-21              8411         30.0      0.0222  \n",
       "2021-06-20              8381         33.0      0.0223  \n",
       "2021-06-19              8348         32.0      0.0223  \n",
       "2021-06-18              8316         47.0      0.0224  \n",
       "2021-06-17              8269         32.0      0.0224  \n",
       "2021-06-16              8237         40.0      0.0225  \n",
       "2021-06-15              8197         39.0      0.0225  \n",
       "2021-06-14              8158         30.0      0.0226  \n",
       "2021-06-13              8128         33.0      0.0226  \n",
       "2021-06-12              8095         37.0      0.0227  \n",
       "2021-06-11              8058         55.0      0.0227  \n",
       "2021-06-10              8003         46.0      0.0227  \n",
       "2021-06-06              7849         29.0      0.0228  \n",
       "2021-06-05              7820         29.0      0.0229  \n",
       "2021-06-04              7791         26.0      0.0229  \n",
       "2021-06-03              7765         30.0      0.0230  \n",
       "2021-06-02              7735         32.0      0.0231  \n",
       "2021-06-01              7703         31.0      0.0232  \n",
       "2021-05-29              7617         28.0      0.0232  \n",
       "2021-05-28              7589         33.0      0.0233  \n",
       "2021-05-27              7556         36.0      0.0233  \n",
       "2021-05-26              7520         36.0      0.0233  \n",
       "2021-05-25              7484         36.0      0.0234  \n",
       "2021-04-07              6050         49.0      0.0227  \n",
       "2021-03-31              5744         73.0      0.0222  \n",
       "2021-03-26              5452         48.0      0.0216  \n",
       "2021-03-25              5404         50.0      0.0215  \n",
       "2021-03-13              4896         31.0      0.0206  \n",
       "2020-08-04              1901         31.0      0.0222  \n",
       "2020-07-31              1811         26.0      0.0219  \n",
       "2020-07-23              1618         37.0      0.0221  \n",
       "2020-07-10              1229         33.0      0.0208  \n",
       "2020-07-08              1171         26.0      0.0208  \n",
       "2020-07-07              1145         27.0      0.0209  \n",
       "2020-06-30               977         46.0      0.0208  \n",
       "2020-06-09               534         27.0      0.0238  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_covid_PB.query('obitosNovos>25 and casosNovos>1500') # note que 'and' é usado em vez de '&'"
   ]
  }
 ],
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   "affiliation": "Departamento de Computação Científica / UFPB",
   "author": "Gustavo Oliveira e Andréa Rocha",
   "date": "Julho de 2020",
   "title": "Aula 6A - Manipulação de Dados"
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   "header": "Gustavo Oliveira / Andrea Rocha",
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