2017-08-21_Jupyter/Mini-Curso_Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mini-curso de Jupyter Notebook @ SciPy-SP\n",
    "\n",
    "por Danilo J. S. Bellini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Máximas do curso**:\n",
    "\n",
    "* Usou o mouse? Tente refazer sem utilizar o mouse (quando possível)!\n",
    "* Acabou? Ajude o colega que ainda não acabou!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 0 - O que é o Jupyter Notebook?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É uma alternativa aos REPL (*run-eval-print loop*) em linha de comando de diferentes linguagens para trabalhar com código interativo, utilizando \"células\" re-editáveis e re-ordenáveis, renderização de Markdown e $\\LaTeX$, entre outros recursos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello SciPy-SP!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello Jupyter!\".replace(\"Jupyter\", \"SciPy-SP\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Características gerais:\n",
    "\n",
    "* Aplicação web ao invés de CLI\n",
    "* *Spin-off* do IPython (`ipython notebook`)\n",
    "* Open-source (BSD 3 cláusulas)\n",
    "* Multi-linguagens (Python, R, Julia, Scala, Haskell, Erlang, ...)\n",
    "\n",
    "Permite criar e compartilhar documentos que contém:\n",
    "\n",
    "* Código a ser rodado \"ao vivo\" (Tanto *hands-on* como pequenas edições)\n",
    "* Equações $\\LaTeX$ via MathJax\n",
    "* Visualizações (e.g. gráficos e tabelas)\n",
    "* Texto explicativo\n",
    "\n",
    "Exemplos de casos de uso:\n",
    "\n",
    "* Limpeza e transformação de dados\n",
    "* Simulação numérica\n",
    "* Modelagem estatística\n",
    "* Machine learning\n",
    "* Alternativa a *slides* em apresentações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemplo: https://try.jupyter.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 1 - Instalar e rodar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Ambiente|Instalação|Execução|\n",
    "|:-|:-|:-|\n",
    "|pyenv/virtualenv|`pip install jupyter`|`jupyter notebook`|\n",
    "|Arch Linux|`pacman -S jupyter`|`jupyter notebook`|\n",
    "|Debian|`apt install jupyter-notebook`|`jupyter-notebook`|\n",
    "|Fedora|`dnf install notebook`|`jupyter-notebook`|\n",
    "\n",
    "Também é possível rodar o Jupyter no Docker, e.g. https://hub.docker.com/r/jupyter/datascience-notebook/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O *Jupyter Notebook* inicia um servidor web com acesso limitado ao *current working directory*, que pode ser acessado no navegador em http://localhost:8888 (ou em outra porta, caso a 8888 já esteja em uso).\n",
    "\n",
    "Em algum terminal, crie o diretório `~/curso_jupyter`, e abra o Jupyter quando estiver dentro desse diretório."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na aba *Files* do Jupyter Notebook, criamos um notebook indo em *New $\\rightarrow$ [Notebooks] Python 3*. Isso deve criar uma nova aba no navegador.\n",
    "\n",
    "Na parte superior dessa nova aba, encontram-se da esquerda para a direita, como barra de \"cabeçalho\":\n",
    "\n",
    "* Logo do Jupyter, um link que restaura a aba com a árvore de arquivos\n",
    "* Nome do arquivo (sem caminho e sem extensão), editável (basta clicar)\n",
    "* Descrição sobre o último checkpoint\n",
    "* Logo da linguagem utilizada no kernel\n",
    "\n",
    "Em *View $\\rightarrow$ Toggle Header* pode-se alternar a visualização dessa barra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Criar o arquivo `~/curso_jupyter/Células.ipynb` no Jupyter Notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 2 - Cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](cells.png)\n",
    "\n",
    "Não, não estamos falando de Dragon Ball Z..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### O que são cells/células?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Cells* são o que compõe o seu notebook: cada notebook é uma sequência de cells / células."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As células são os objetos editáveis, e, similar ao `vim`, o editor web dos notebooks é modal!\n",
    "\n",
    "Há dois \"modos\", explícitos na a barra à esquerda da célula selecionada:\n",
    "\n",
    "|Cor|Modo|Tecla para entrar no modo|Operações atuam sobre|\n",
    "|:-:|:-:|:-:|:-|\n",
    "|<font color=\"#42a5f5\">**┃ (azul)**</font>|<font color=\"#42a5f5\">**Comando**</font>|<font color=\"#42a5f5\">**Esc**</font>|Células / layout|\n",
    "|<font color=\"#66bb6a\">**┃ (verde)**</font>|<font color=\"#66bb6a\">**Edição**</font>|<font color=\"#66bb6a\">**Enter**</font>|Conteúdo de uma célula em específico|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modo de comando"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O comando `h` *help* (ajuda) exibe a lista de atalhos, que pode ser fechada com `Esc`. O mesmo encontra-se em *Help $\\rightarrow$ Keyboard Shortcuts*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alguns comandos úteis:\n",
    "\n",
    "|Comando|Descrição|\n",
    "|:-:|:-|\n",
    "|`a`|Insere uma célula *above* (acima) da selecionada|\n",
    "|`b`|Insere uma célula *below* (abaixo) da selecionada, como o botão <i class=\"fa fa-plus\"></i>|\n",
    "|`x`|Remove (recorta) as células selecionadas|\n",
    "|`dd`|Remove as células selecionadas|\n",
    "\n",
    "Mantendo o `Shift` pressionado, é possível selecionar um conjunto de células. A última célula da seleção é a utilizada nas operações que afetam apenas uma célula."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Além do mouse, para escolher a célula selecionada (mover o \"cursor\"), pode-se usar as setinhas do teclado para baixo/cima, ou respectivamente as teclas `j` e `k`, como no `vim`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Inserir 7 células no notebook, cada uma contendo um caractere da palavra `Jupyter`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tipo de células"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos mudar o tipo de uma célula com um comando (menu *Cell $\\rightarrow$ Cell Type*):\n",
    "\n",
    "|Comando|Descrição|\n",
    "|:-:|:-|\n",
    "|`r`|Célula *raw*, texto puro sem formatação|\n",
    "|`y`|Célula com código para rodar em um kernel (Python, no nosso caso)|\n",
    "|`m`|Célula com código em *Markdown* para ser renderizado|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para rodar o código ou renderizar o Markdown de uma célula, utilize (em qualquer modo):\n",
    "\n",
    "|Comando|Descrição|\n",
    "|:-:|:-|\n",
    "|`Ctrl + Enter`|Roda/renderiza a célula atual|\n",
    "|`Shift + Enter`|Idêntico à sequência `Ctrl + Enter`, `j`|\n",
    "|`Alt + Enter`|Idêntico à sequência `Ctrl + Enter`, `b`|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Triplicar as 7 células para que cada uma delas exista como:\n",
    "\n",
    "* Header 3 do Markdown (e.g. `### a`, caso a letra fosse `a`)\n",
    "* Texto puro sem formatação\n",
    "* Código Python que imprime o valor ASCII do caractere em hexadecimal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordem das células"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As células selecionadas podem ser movidas através dos botões <i class=\"fa fa-arrow-up\"></i> <i class=\"fa fa-arrow-down\"></i> ou pelo menu *Edit $\\rightarrow$ Move Cell Up/Down*. Embora essa operação não possua um comando pelo teclado, há uma alternativa para evitar o uso do mouse: o fatídico *cut and paste*! Para ordenar sem o mouse, basta recortar e colar no lugar desejado.\n",
    "\n",
    "|Comando|Descrição|Botão equivalente|\n",
    "|:-:|:-|:-:|\n",
    "|`x`|Recorta|<i class=\"fa fa-cut\"></i>|\n",
    "|`c`|Copia|<i class=\"fa fa-copy\"></i>|\n",
    "|`v`|Cola abaixo da célula selecionada|<i class=\"fa fa-paste\"></i>|\n",
    "|`Shift + v`|Cola acima da célula selecionada|N/A|\n",
    "\n",
    "No menu *Edit* há ainda uma opção para colar e sobrescrever (*Paste Cells & Replace*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Colocar as células em ordem alfabética (*case insensitive*), mas agrupadas por tipo: primeiro todas as células em texto puro, então todas as células Markdown, e por último todas as com código Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split & Merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* *Split*: Com `Ctrl + Shift + -` (`-` é a tecla *menos*) no modo de edição, a célula é dividida em duas, em que o cursor da edição é a fronteira entre o conteúdo de cada uma das duas novas células, e a edição continua na segunda célula da divisão realizada\n",
    "* *Merge*: Com `Shift + m` no modo de comando, as células selecionadas (ou a única célula selecionada e a abaixo desta) são mescladas em uma única célula\n",
    "\n",
    "Também é possível realizar essas operações usando o *Edit $\\rightarrow$ Split Cell*, o *Edit $\\rightarrow$ Merge Cell Above*, e o *Edit $\\rightarrow$ Merge Cell Below*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Agrupar as células para que exista apenas uma célula de cada tipo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Undo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No modo de edição, o *undo* (desfazer) tem uma pilha para cada célula, isto é, ao utilizar o `Ctrl + z` em uma célula, a última operação realizada nessa célula é desfeita, não a última operação de edição realizada em alguma célula.\n",
    "\n",
    "No modo de comando, o atalho é `z` e serve para desfazer uma operação de remoção de célula (*Edit $\\rightarrow$ Undo Delete Cells*), não qualquer operação."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Salvar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O comando `s` (no modo de comando) e o atalho `Ctrl + s` (em qualquer modo) salva da mesma forma que o botão <i class=\"fa fa-save\"></i>. Ao salvar, um *checkpoint* é armazenado no diretório `.ipynb_checkpoints/`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 3 - Execução no kernel Python 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os comandos são executados por algum interpretador, o núcleo (kernel) do notebook. Em nosso caso, o kernel é o do IPython (Python 3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordem de execução"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apesar da ordem das células ser manipulável, a ordem de execução é linear, como se estivéssemos desde o início em um REPL como o IPython."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Criar o arquivo `~/curso_jupyter/IPython.ipynb` no Jupyter Notebook, e:\n",
    "\n",
    "* Criar 3 células de código, 2 com atribuições `flag = True` e `flag = False`, e uma terceira apenas com o `flag`, nessa ordem.\n",
    "* Rodar as células 1, 2 e 3, depois rodar 1 e 3, e depois rodar 2 e 3 novamente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expressão final da célula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso o último statement da célula de código seja uma expressão, e caso essa expressão devolva um valor que não seja `None`, a representação do resultado dessa expressão é exibida como $n$-ésima saída (associada à $n$-ésima entrada). Por exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isso é diferente de um `print`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n"
     ]
    }
   ],
   "source": [
    "print(3 ** 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como no IPython, o `_` é uma variável que armazena o último resultado de uma célula (o valor associado ao `Out[n]` de maior `n`, não o último valor impresso, nem o resultado da última expressão interna a uma célula)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "print(_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Mudar as atribuições para que elas exibam o valor da constante `flag` sem exigir um `print`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursos do IPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Acesso à $n$-ésima saída pelo número $n$, com a variável `_n`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.],\n",
       "       [ 0.,  1.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np # Uma célula pode ter múltiplos statements!\n",
    "np.eye(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.,  0.],\n",
       "       [ 0.,  2.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_5 * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Acesso à história de comandos com `%hist` ou `%history`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "print(\"Hello Jupyter!\".replace(\"Jupyter\", \"SciPy-SP\"))\n",
      "3 ** 2\n",
      "print(3 ** 3)\n",
      "print(_)\n",
      "import numpy as np # Uma célula pode ter múltiplos statements!\n",
      "np.eye(2)\n",
      "_5 * 2\n",
      "%history\n"
     ]
    }
   ],
   "source": [
    "%history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Obtenção de ajuda com `?` e acesso ao código-fonte com `??`\n",
    "\n",
    "```python\n",
    "np.arange?      # Exibe a docstring e outras informações úteis\n",
    "np.linalg.det?? # Exibe o código fonte\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Também é possível obter um popup de ajuda com `Shift + TAB` ao invés de rodar o comando com o ponto de interrogação (ampliável apertando o `TAB` duas vezes)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Code completion com TAB\n",
    "\n",
    "![](tab_completion.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para uma lista mais detalhada do que o IPython oferece, rode uma célula com o comando mágico `%quickref`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reiniciando o kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como \"voltar\" / \"resetar\" os números de In/Out das células de código?\n",
    "\n",
    "*Resposta*: Reiniciando o kernel, por exemplo no menu *Kernel $\\rightarrow$ Restart & Run All*.\n",
    "\n",
    "É possível fazer o reinício (*Kernel $\\rightarrow$ Restart*, ou *Kernel $\\rightarrow$ Restart & Clear Output*) separadamente de uma nova execução (*Cell $\\rightarrow$ Run All*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Forçar o seguinte histórico no seu notebook (sem lançar exceções!):\n",
    "\n",
    "```python\n",
    "flag = True\n",
    "flag\n",
    "str(_)\n",
    "flag = False\n",
    "flag\n",
    "flag\n",
    "True\n",
    "assert _1\n",
    "assert _2 == \"True\"\n",
    "assert not _3\n",
    "assert not _4\n",
    "assert _5\n",
    "%history\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 4 - Configurando o Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há uma configuração geral do Jupyter para o usuário que o executou, a qual tipicamente fica no diretório `~/.jupyter/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/danilo/.jupyter'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jupyter_core.paths import jupyter_config_dir\n",
    "jupyter_config_dir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A configuração é realizada por meio do arquivo `nbconfig/notebook.json` localizado nesse diretório. Pelo próprio Jupyter é possível ler e alterar essa configuração global do usuário, usando um dicionário Python ao invés de um JSON."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por exemplo, <font color=\"red\">para aqueles que também se incomodam com os `()[]{}` \"automáticos\" que ignoram o digitado ou ficam duplicados quando não deveriam</font>, é possível configurar o Jupyter para desabilitar esse recurso `autoCloseBrackets`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from notebook.services.config import ConfigManager\n",
    "ConfigManager().update(\"notebook\", {\"CodeCell\": {\"cm_config\": {\"autoCloseBrackets\": False}}});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alterações somente são aplicadas ao fechar e abrir a janela (não é necessário encerrar o kernel).\n",
    "\n",
    "Há também uma configuração do sistema, tipicamente localizada em `/etc/jupyter/notebook.json`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CodeCell': {'cm_config': {'autoCloseBrackets': False, 'dragDrop': False}},\n",
       " 'load_extensions': {'jupyter-js-widgets/extension': True}}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ConfigManager().get(\"notebook\") # Exibe toda a configuração"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os recursos `cm_config` do `CodeCell` são do CodeMirror: https://codemirror.net/doc/manual.html\n",
    "\n",
    "Em particular, isso significa que a seleção múltipla no modo de edição é naturalmente habilitada ao clicar em múltiplos pontos de uma única célula enquanto o `Ctrl` é mantido pressionado.\n",
    "\n",
    "Podemos mudar outros valores além do `autoCloseBrackets`, por exemplo:\n",
    "\n",
    "* `autoCloseBrackets` (default é `True`)\n",
    "* `indentUnit` (default é `4`)\n",
    "* `tabSize` (default é `4`)\n",
    "* `indentWithTabs` (default é `False`)\n",
    "* `dragDrop` (default é `True`)\n",
    "\n",
    "Além do `notebook.json` há arquivos para outras seções (e.g. `editor.json`, `tree.json`, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Atribuir o valor desejado para alguma dessas 5 configurações."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encerrando o Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fechar a aba ou o navegador não encerra o kernel utilizado pelo notebook, muito menos o servidor web.\n",
    "\n",
    "Para encerrar um kernel, vá na árvore (clicar no logo do Jupyter ou ir em http://localhost:8888/tree ou o equivalente para sua configuração), e na aba *Running* há botões para *Shutdown* dos notebooks abertos.\n",
    "\n",
    "Caso queira encerrar o servidor web do Jupyter Notebook, pressione `Ctrl + c` no console em que este se encontra aberto e confirme com `y`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Encerrar todos os notebooks abertos, o servidor, e abrir novamente o servidor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 5 - Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Além de obviamente importar o próprio Matplotlib,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "para que o Matplotlib renderize os gráficos no notebook, é necessário rodar o seguinte comando \"mágico\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tal comando \"mágico\" também é necessário para permitir que outras bibliotecas (e.g. Pandas) plote com o Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1b999e4160>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SymoPz32Sw99X5RDWKohnf3oWVw3tpkFf4giVu4gXbNx1mCmLUsneX8LVZ3XjofGD6ahB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\npAhWEjlVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b99d0dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2], [1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repare no `[<matplotlib.lines.Line2D at 0x...>]` resultante. Isso ocorre por ter sido o resultado da expressão `plt.plot(...)`, mas não queremos que tal texto apareça. Podemos simplesmente forçar que o último statement seja vazio utilizando um `;` ao final da linha da expressão, quando esta é a final:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SymoPz32Sw99X5RDWKohnf3oWVw3tpkFf4giVu4gXbNx1mCmLUsneX8LVZ3XjofGD6ahB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\npAhWEjlVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b999afa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2], [1, 2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Copiar o gráfico abaixo e criar um novo para a função logaritmo natural para $x$ de $0.5$ a $5$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0bAlnnw2ffQZ33w0LLVR0MkmSJNV106bBqafCZZfB3nvnObfzzz/n71P5qeoK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\niyDlRP9fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b999dda90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2, 2, 50)\n",
    "y = x ** 2\n",
    "fig, ax = plt.subplots(1, 1, figsize=(16, 5))\n",
    "ax.plot(x, y, color=\"blue\")\n",
    "ax.set(title=\"Gráfico de $x^2$\",\n",
    "       xlabel=\"Abscissas\",\n",
    "       ylabel=\"Ordenadas\",\n",
    "       xlim=[-2, 2],\n",
    "       ylim=[0, 4]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há várias outras formas para se realizar plots com o Matplotlib, mas um aprofundamento nessa direção foge do escopo deste mini-curso."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 6 - Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Markdown é uma linguagem para a escrita de textos para serem renderizados, convertendo marcações no texto puro em formatações.\n",
    "\n",
    "Por exemplo, o *itálico* (ou *com ênfase*) pode ser obtido a partir do uso de asteriscos: `*itálico*`; e o **negrito** (ou **bold face**) pode ser obtido a partir do uso de $2$ asteriscos: `**negrito**`.\n",
    "\n",
    "Títulos podem ser criados a partir do uso de caracteres `#` de prefixo, na forma:\n",
    "\n",
    "* `# Header 1`\n",
    "* `## Header 2`\n",
    "* `### Header 3`\n",
    "\n",
    "Isso equivale ao `<h1>`, `<h2>` e `<h3>` do HTML. Em geral, quanto menor o número, maior o tamanho do texto resultante, como ocorre com o título deste notebook (header 1) em contraste com o das partes numeradas (header 2) e o título das subseções das partes (header 3).\n",
    "\n",
    "Os números no modo de comando do Jupyter mudam a célula para uma Markdown já com o prefixo do header do respectivo número digitado (um dos motivos pelos quais é comum que o título tenha uma célula exclusivamente para si)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O uso de `---` (três traços) cria uma linha horizontal, como esta:\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Listas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Não-ordenadas, basta usar `\"* \"` (asterisco-espaço) como prefixo:\n",
    "\n",
    "- Exemplo\n",
    "- de lista\n",
    "  - aninhada\n",
    "\n",
    "Também é válido usar `-` no lugar de `*`, e prefixos numerados como `1.` se mantém:\n",
    "\n",
    "1. Exemplo\n",
    "2. com\n",
    "3. números\n",
    "\n",
    "O conteúdo de cada item pode ocupar mais de uma linha, a indentação é o que garante a pertinência do conteúdo ao item."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Código"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para colocar código como texto inline, usa-se backticks como em \\``texto`\\`. Os backticks fazem o texto interno aparecer monoespaçado e sem a possível formatação de um código em Markdown.\n",
    "\n",
    "Triplos backticks \\`\\`\\` criam um bloco multi-linhas para o código, e se o bloco inicial tiver o nome da linguagem, esta será renderizada com highlighting (se houver highlighter).\n",
    "\n",
    "**Exercício**: Copiar o código abaixo em um bloco de texto em markdown.\n",
    "\n",
    "\\`\\`\\``python`\n",
    "\n",
    "`print(\"Hello Markdown!\")`\n",
    "\n",
    "\\`\\`\\`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Código em HTML é considerado Markdown válido. Em particular isso permite a inserção de imagens, links e tabelas a partir do HTML, mas o Markdown também possui esses recursos nativamente.\n",
    "\n",
    "Links podem ser criados com a sintaxe `[Texto do link](URL)`, e imagens com `![](URL)`, além do uso da sintaxe do HTML.\n",
    "\n",
    "Um recurso em que o HTML4 pode ser útil é para colocar cores em fontes: `<font color=\"#RRGGBB\">Texto</font>` é o que permite que o texto em uma célula do Jupyter fique colorido. Ao invés do componente `#RRGGBB`, pode-se utilizar o nome da cor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Faça um lista com as cores <font color=\"red\">vermelho</font>, <font color=\"blue\">azul</font>, <font color=\"green\">verde</font>, <font color=\"gray\">cinza</font> e <font color=\"yellow\">amarelo</font> (e outras mais, caso queira)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tabelas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tabelas em Markdown são criadas a partir de símbolos `|` como separadores, início e fim. A primeira linha contém os títulos, a segunda deve conter hífens/menos `-` como conteúdo, e da terceira em diante os valores da tabela. Na segunda linha, é possível controlar se o texto de cada coluna é orientado à esquerda (`|:-|`), à direita (`|-:|`) ou centralizado (`|:-:|`).\n",
    "\n",
    "**Exercício**: Copie a tabela abaixo.\n",
    "\n",
    "|Lado|Área|Perímetro|\n",
    "|-:|:-:|:-|\n",
    "|1|1|*quatro*|\n",
    "|2|*quatro*|8|\n",
    "|3|9|**UMA DÚZIA!**|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### LaTeX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com o símbolo de `$` é possível criar blocos em $\\LaTeX$, por exemplo `$2 + 2$` para criar uma equação inline: $2 + 2$\n",
    "\n",
    "E também entre duplo cifrão, representando uma linha isolada como em:\n",
    "\n",
    "$$\\frac{2}{\\sqrt{\\pi}} \\cdot \\frac{3}{\\sqrt{\\pi}} \\cdots \\frac{15}{\\sqrt{\\pi}}$$\n",
    "\n",
    "Cujo código é\n",
    "\n",
    "```latex\n",
    "$$\\frac{2}{\\sqrt{\\pi}} \\cdot \\frac{3}{\\sqrt{\\pi}} \\cdots \\frac{15}{\\sqrt{\\pi}}$$\n",
    "```\n",
    "\n",
    "Os comandos do $\\LaTeX$ são iniciados pela contrabarra, e blocos podem ser agrupados por chaves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Copiar o que consta entre as barras horizontais (inclusive as mesmas):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Há diferentes formas de encontrar uma aproximação do valor de pi, por exemplo:\n",
    "\n",
    "\n",
    "\n",
    "1. A série de Madhava-Gregory-Leibniz\n",
    "\n",
    "  $$\\frac{\\pi}{4} = 1 - \\frac{1}{3} + \\frac{1}{5} - \\frac{1}{7} + \\cdots$$\n",
    "\n",
    "2. A série de Nilakantha\n",
    "\n",
    "  $$\\pi = 3 + \\frac{4}{2 \\cdot 3 \\cdot 4} - \\frac{4}{5 \\cdot 6 \\cdot 7} + \\frac{4}{8 \\cdot 9 \\cdot 10} - \\cdots$$\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graças ao MathJax, é possível usar recursos que iriam exigir a importação de pacotes no $\\LaTeX$, para esses casos usa-se o comando não-padrão `\\require{nome_pacote}` no início da expressão. É uma exceção, já que a importação dos pacotes no LaTeX em geral não ocorre dentro de uma expressão. O `\\cancel{conteúdo}` para fazer um corte exige o uso do pacote `cancel`, permitindo coisas do tipo $\\require{cancel}2 + \\cancel{1 - 1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Multiplicar a série de Madhava-Gregory-Leibniz por 4 dos 2 lados, e cortar o número 4 no numerador e denominador do lado esquerdo da expressão."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 7 - Pandas e resultados longos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os DataFrames do Pandas têm seu conteúdo renderizado em tabelas no Jupyter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$H_0$ (Hipótese nula)</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$H_A$ (Hipótese alternativa / Experimento)</th>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             A   B   C   D\n",
       "$H_0$ (Hipótese nula)                        7   2   5   3\n",
       "$H_A$ (Hipótese alternativa / Experimento)  18  15  13  10"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame([[7, 2, 5, 3], [18, 15, 13, 10]],\n",
    "                    columns=list(\"ABCD\"),\n",
    "                    index=[\"$H_0$ (Hipótese nula)\",\n",
    "                           \"$H_A$ (Hipótese alternativa / Experimento)\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alguns detalhes:\n",
    "\n",
    "* As enumeração das linhas do código são acionadas com o comando `l` (modo comando)\n",
    "* O resultado pode ser exibido/escondido com `o`, mostrando uma barra com reticências ao invés do resultado\n",
    "* O código em LaTeX nos índices do DataFrame também são renderizados quando dentro das tabelas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício**: Vá em https://github.com/danilobellini/scientific-literature e peguem o arquivo `1936-Fisher/iris.csv` com o Iris dataset. Mostre o conteúdo do arquivo usando a função `pd.read_csv`. Depois:\n",
    "\n",
    "* Clique na região do `Out` (parte inferior esquerda) para habilitar/desabilitar a barra de rolagem\n",
    "* Utilize `Shift + o` para realizar a mesma tarefa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 8 - Extras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shapely"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qualquer biblioteca pode ter sua própria renderização de objetos no Jupyter. A título de exemplo, a *Shapely* exibe seus objetos renderizando um SVG para o Jupyter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<shapely.geometry.multipolygon.MultiPolygon at 0x7f1b99928a90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shapely.geometry import Polygon, Point\n",
    "Polygon([(1, 1), (2, 1), (1, 2)]).symmetric_difference(Point(1.5, 1.5).buffer(.25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JavaScript"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apesar do kernel ser Python, é possível rodar código em JavaScript usando o interpretador do navegador:\n",
    "\n",
    "```js\n",
    "%%javascript\n",
    "alert(\"Hello World!\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fim! Use tudo de uma só vez!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tarefa final**: Reconstruir este notebook sem copiar o código do mesmo!"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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