matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Alt text](https://gistcdn.githack.com/Gbecdox/ff5c3452d2941bc856a4594b17b63b09/raw/8487b04f1cb234eaba6b0fcb256f5cd280f62b94/HeaderMAT.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesse notebook veremos sobre:\n",
    "\n",
    "* **Gráfico de Linha**\n",
    "* **Customização de Gráficos**\n",
    " * Título\n",
    " * Legendas\n",
    " * Grade\n",
    " * Tamanho de figuras\n",
    " * Ticks\n",
    " * Textos/Marcações\n",
    " * Cores\n",
    "* **União de Curvas e Gráficos**\n",
    "* **Gráfico de Dispersão**\n",
    "* **Gráfico de Setores** (*pizza*)\n",
    "* **Gráfico de Barras**\n",
    "* **Histogramas**\n",
    "* **Diagrama de Caixa** (*boxplot*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando e entendendo o funcionamento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando estamos em um notebook, como esse, é usual (e recomendado) não só importar a biblioteca, mas também adicinar a linha `%matplotlib inline`. Isto é:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mas por que fazer isso? Antes de tudo, é importe saber que funções iniciadas por `%` ou `%%` pertecem a uma categoria especial dentro do IPython, são as chamadas **funções mágicas** — não confundir com os *métodos mágicos* ou *dunders* nativos do Python (como `__int__`). Simplificadamente, essas são funções que permitem controlar a forma como o IPython opera, caso tenha interesse em saber mais, experimente o comando `%magic`.\n",
    "\n",
    "Agora que já sabemos disso, utilizar `%matplotlib inline` faz com que os gráficos que iremos plotar apareçam no próprio notebook, logo abaixo da célula na qual o código foi escrito e não em uma nova janela, por exemplo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construindo Gráficos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficos de Linha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Começaremos com um modelo simples: um gráfico de linha. Para isso, usaremos `plt.plot()` para construir a curva e `plt.show()` para exibi-la."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 2, 3, 4, 5]\n",
    "y = [2, 1, 3, 2, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y) # Cria o gráfico y versus x\n",
    "plt.show()     # Exibe o gráfico criado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note que passamos como argumentos para `plt.plot()` duas listas (**x** e **y**) que contêm as coordenadas, nos respectivos eixos, dos pontos que queremos exibir. Na verdade, qualquer estrutura de dados que seja considerada um `array_like` pode ser utilizada, desde que as dimensões de **x** e **y** sejam iguais.\n",
    "\n",
    "Além do que já citamos, usar `%matplotlib` nos traz outra vantagem: o comando `plt.show()` passa a ser dispensável, isto é, os gráficos sempre são exibidos após a célula, quer ela contenha o comando `plt.show()`, quer não."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incrementando a visualização: título, legendas, grade e tamanho da figura"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos recorrer a paramêtros e funções auxiliares que tornem a leitura do gráfico mais clara ou que elucidem pontos importantes.\n",
    "\n",
    "Começaremos inserindo informações pertinentes a todo gráfico:\n",
    "\n",
    "* `plt.title()` insere um título na figura;\n",
    "* `plt.xlabel()` insere a legenda para o eixo horizontal;\n",
    "* `plt.ylabel()` insere a legenda para o eixo vertical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ordenadas')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "\n",
    "plt.title(\"Título\")     # Título do gráfico\n",
    "plt.xlabel(\"Abscissas\") # Legenda do eixo x\n",
    "plt.ylabel(\"Ordenadas\") # Legenda do eixo y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando temos muitos dados a visualização dos pontos pode ficar prejudicada, para resolver isso, podemos inserir uma grade de fundo com `plt.grid()` e alterar o tamanho da figura através de `plt.figure()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6)) # Altera o tamanho da figura; default: (6.4, 4.8)\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.title(\"Título\")\n",
    "plt.xlabel(\"Abscissas\")\n",
    "plt.ylabel(\"Ordenadas\")\n",
    "\n",
    "plt.grid() # Adiciona uma grade ao fundo do gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perceba que quando utilizamos a função `plt.figure()` é necessário um cuidado especial com a ordem na qual as linhas do código estão dispotas, para garantir que essa esteja **antes** das demais. Isso porque para cada gráfico é gerada automaticamente uma figura e o comando `plt.figure()` força a criação de uma nova, que contém as informações dos parâmetros passados e das **linhas abaixo** da sua, parando ao encontrar o final da célula ou um outro `plt.figure()`. Para checar os demais parâmetros da função é uma boa prática visitar a [documentação](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.figure.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incrementando a visualização: ticks, texto e marcações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Você notou que em nenhum momento definimos o intervalo de valores para cada eixo? Isso porque a função `plt.plot()` já o faz automaticamente, mas utilizando `plt.xticks()` e `plt.yticks()` passa a ser nós que determinamos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x15fb467fe08>,\n",
       "  <matplotlib.axis.YTick at 0x15fb467f408>,\n",
       "  <matplotlib.axis.YTick at 0x15fb467d148>,\n",
       "  <matplotlib.axis.YTick at 0x15fb46b7508>,\n",
       "  <matplotlib.axis.YTick at 0x15fb46b7308>,\n",
       "  <matplotlib.axis.YTick at 0x15fb46bb8c8>,\n",
       "  <matplotlib.axis.YTick at 0x15fb46b7448>],\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "\n",
    "plt.title(\"Título\")\n",
    "plt.xlabel(\"Abscissas\")\n",
    "plt.ylabel(\"Ordenadas\")\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.xticks([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5]) # Valores exibidos no eixo x\n",
    "plt.yticks([0, 1, 2, 3, 4, 5, 6])                       # Valores exibidos no eixo y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma forma muito conveniente para determinar o intervalo dos eixos é com auxílio da biblioteca NumPy, podemos obter esse mesmo resultado anterior fazendo:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "\n",
    "plt.xticks(np.arange(0, 5.5, 0.5))\n",
    "plt.yticks(np.arange(0, 7, 1))\n",
    "```\n",
    "\n",
    "Além disso, através das funções `plt.text()` e `plt.annotate()` conseguimos inserir informações complementares diretamente nos gráficos. Primeiro, vamos nos preocupar em exbir um texto numa região da figura e, para facilitar a leitura, colocaremos um fundo colorido. Inicialmente, podemos criar uma variável que contenha as propriedades dessa nossa caixa de texto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "caixa = dict(boxstyle=\"round\", fc=\"green\") # Propriedades da caixa de texto (arredondada e verde)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora basta recorrer a função `plt.text()` e ao seu argumento `bbox` para inserirmos o texto e a caixa, respectivamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(2, 5, 'Texto em caixa')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "\n",
    "plt.title(\"Título\")\n",
    "plt.xlabel(\"Abscissas\")\n",
    "plt.ylabel(\"Ordenadas\")\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "# Insere o texto centralizado nas coordenadas (1,5) e com tamanho de fonte 12\n",
    "plt.text(2, 5, \"Texto em caixa\", ha=\"center\", fontsize=12, bbox=caixa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mas e se um ponto em específico merecer destaque? Como poderíamos marcá-lo? Com `plt.annotate()` podemos indicar pontos utilizando textos e setas. Primeiro, semelhante ao que fizemos com a caixa de texto, definimos as propriedades da nossa seta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# arrowstyle: estilo da seta/marcador; connectionstyle: forma do corpo, nesse caso, curvado (\"angle3\")\n",
    "seta = dict(arrowstyle=\"->\", connectionstyle=\"angle3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feito isso, usamos `plt.annotate()` e seu argumento `arrowprops`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3.5, 4, 'Ponto (3,3)')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "\n",
    "plt.title(\"Título\")\n",
    "plt.xlabel(\"Abscissas\")\n",
    "plt.ylabel(\"Ordenadas\")\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.text(2, 5, \"Texto em caixa\", ha=\"center\", fontsize=12, bbox=caixa)\n",
    "\n",
    "# Respectivamente, os três primeiros argumentos são: texto, coordenadas do ponto, coordenadas do texto.\n",
    "plt.annotate(\"Ponto (3,3)\", (3,3), (3.5,4), ha=\"center\", arrowprops=seta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O Matplotlib oferece pelo menos uma dezena de alternativas para os argumentos que mostramos nessa seção, por isso, recomendamos visitar o [tutorial de anotações](https://matplotlib.org/3.1.1/tutorials/text/annotations.html) para conferir todas elas — atente-se para as tabelas seguidas por imagens!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## União: curvas em um mesmo sistema de coordenadas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso precisemos expressar duas ou mais curvas em um mesmo gráfico basta usar a função `plt.plot()` repetidas vezes e, se necessário, `plt.show()`. Além disso, o uso de `plt.legend()` permite a identificação de cada curva."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coordenadas dos pontos da nova curva a ser plotada\n",
    "x2 = [1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5] \n",
    "y2 = [5, 4, 5, 4, 4, 3, 3, 2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15fb457ee08>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)   # Cria a curva usada anteriormente (y versus x)\n",
    "plt.plot(x2, y2) # Cria a nova curva (y2 versus x2)\n",
    "\n",
    "plt.legend((\"(x,y)\",\"(x2,y2)\")) # Recebe uma tupla contendo o nome desejado para a legenda, na ordem dos plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perceba que se usarmos `plt.show()` entre as funções `plt.plot()` estaremos exibindo a figura criada até ali e, consequentemente, será iniciada uma nova figura para as linhas seguintes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15fb4803588>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.plot(x2, y2)\n",
    "plt.show() # Exibe a figura criada até essa linha, NÃO encerrando a execução da célula.\n",
    "\n",
    "plt.plot(x2,y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Já para facilitar a leitura, as curvas quando juntas possuem cores distintas, mesmo assim, ainda podemos fazer alterações com os argumentos:\n",
    "* `color` ou `c`, para definer uma cor para a linha;\n",
    "* `linewidth` ou `lw`, para definir uma espessura para a linha;\n",
    "* `linestyle` ou `ls`, para definir o estilo de linha."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15fb487bf08>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, color=\"cyan\", linewidth=2, linestyle=\"--\") # Coloração ciano, linha tracejada\n",
    "plt.plot(x2, y2, c=\"magenta\", lw=3, ls=\"-.\")              # Coloração magenta, linha traço e ponto\n",
    "\n",
    "plt.legend((\"(x,y)\",\"(x2,y2)\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Não se esqueça de verificar a [tabela de cores](https://matplotlib.org/2.0.2/_images/named_colors.png) e a [lista de estilos](https://matplotlib.org/_images/sphx_glr_line_styles_reference_001.png)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## União: gráficos em uma mesma figura"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mas e se precisarmos exibir múltiplos gráficos em uma mesma figura? Para isso utilizamos a função `plt.subplot()`. Seu funcionamento é um pouco mais sofisticado, primeiramente, considere que queremos exibir **dois gráficos lado a lado**, então podemos passar os valores `n`, `m` e `i` tais que:\n",
    "\n",
    "* `n`: número de linhas,\n",
    "* `m`: número de colunas,\n",
    "* `i`: índice;\n",
    "\n",
    "de duas formas equivalentes:\n",
    "\n",
    "* `plt.subplot(m, n, i)` ou\n",
    "* `plt.subplot(mni)`.\n",
    "\n",
    "Para esclarecer, pense em uma matriz com essas dimensões (**n** por **m**) na qual seus elementos possuem um índice (**i**) que começa igual a 1 no canto superior esquerdo e é crescente da esquerda para a direita, de cima para baixo. Então:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(1, 2, 1) # Campo à esquerda (índice 1), receberá um gráfico\n",
    "ax2 = plt.subplot(1, 2, 2) # Campo à direita (índice 2), receberá outro gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, basta entrar com os gráficos desejados através da função `ax.plot()` e usar `ax.set_title()` para definir um título."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Índice 2')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(1, 2, 1)\n",
    "ax2 = plt.subplot(1, 2, 2)\n",
    "\n",
    "ax1.plot(x, y)             # Plota a curva y versus x\n",
    "ax1.set_title(\"Índice 1\")  # Adiciona o título\n",
    "\n",
    "ax2.plot(x2, y2)           # Plota a curva y2 versus x2\n",
    "ax2.set_title(\"Índice 2\")  # Adiciona o título"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dessa forma, independemente da dimensão dessa \"matriz\" a construção será análoga, tome cuidado apenas com a sobreposição das informações: use `plt.tight_layout()` para justapor os gráficos corretamente. No exemplo abaixo, temos gráficos dispostos tais como em uma matriz 2x2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(221) # Campo de índice 1 na \"matriz\" 2x2\n",
    "ax2 = plt.subplot(222) # Campo de índice 2 na \"matriz\" 2x2\n",
    "ax3 = plt.subplot(223) # Campo de índice 3 na \"matriz\" 2x2\n",
    "ax4 = plt.subplot(224) # Campo de índice 4 na \"matriz\" 2x2\n",
    "\n",
    "ax1.plot(x, y)\n",
    "ax1.set_title(\"Índice 1\")\n",
    "\n",
    "ax2.plot(x2, y2)\n",
    "ax2.set_title(\"Índice 2\")\n",
    "\n",
    "ax3.plot(x2, y2)\n",
    "ax3.set_title(\"Índice 3\")\n",
    "\n",
    "ax4.plot(x, y)\n",
    "ax4.set_title(\"Índice 4\")\n",
    "\n",
    "plt.tight_layout() # Garante o espaçamento adequado entre os gráficos (impede sobreposição)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Infelizmente, a ideia intuitiva de imaginar os gráficos dispostos em uma matriz não fica tão evidente quando queremos linhas ou colunas com diferentes números de gráficos entre si, por exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(2, 2, 1) \n",
    "ax2 = plt.subplot(2, 2, 2)\n",
    "ax5 = plt.subplot(2, 1, 2) # Índice 2 pois a primeira linha (índice 1 para uma matriz 2x1) já está ocupada\n",
    "\n",
    "ax1.plot(x, y)\n",
    "ax1.set_title(\"Índice 1 em uma matriz 2x2\")\n",
    "\n",
    "ax2.plot(x2, y2)\n",
    "ax2.set_title(\"Índice 2 em uma matriz 2x2\")\n",
    "\n",
    "ax5.plot(x2, y2)\n",
    "ax5.set_title(\"Índice 2 em uma matriz 2x1\")\n",
    "\n",
    "plt.tight_layout() # Garante o espaçamento adequado entre os gráficos (evita sobreposição)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficos de dispersão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os gráficos de dispersão (ou *scatter plots*, em inglês) são úteis quanto buscamos verificar como dois dados se relacionam, isto é, quanto um dado afeta o outro. Vamos usar a função `plt.scatter()` e criar duas listas com valores fictícios para dar um exemplo:\n",
    "* seja **i** a idade (em meses) de bebês;\n",
    "* seja **h** a altura (em centímetros) dos bebês."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = [1, 1.3, 1.7, 2.2, 2.5, 3, 3.2, 3.1, 3.3, 3.7, 4, 4.2, 4.4, 5, 5.6, 5.2, 5.3, 6, 6, 6, 6] # Idades\n",
    "h = [52, 50, 51, 56, 55, 59, 57, 55, 56, 61, 65, 62, 63, 61, 60, 66, 63, 64, 60, 68, 69]      # Alturas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Altura (cm)')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(i, h) # Cria o gráfico de dispersão (altura por idade)\n",
    "\n",
    "plt.title(\"Relação entre altura e idade para bebês\") # Título do gráfico\n",
    "plt.xlabel(\"Idade (meses)\")                          # Legenda do eixo x\n",
    "plt.ylabel(\"Altura (cm)\")                            # Legenda do eixo y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perceba que, diversas vezes, muitas funções e argumentos que já vimos aplicam-se a qualquer gráfico, todavia, no caso de um *scatter plot*, é interessante ressaltar que:\n",
    "* `c` define a cor, contudo, pode ser passado um `array_like` (e.g. uma lista) de dimensão igual a dos dados plotados para mapear cores;\n",
    "* `s` é um `array_like` usado para alterar o tamanho dos marcadores;\n",
    "* `marker` define o marcador a ser utilizado, confira [aqui](https://matplotlib.org/3.1.1/api/markers_api.html) a lista de marcadores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Altura (cm)')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tamanho = [20, 40, 80] # Três possíveis tamanhos para o marcador\n",
    "\n",
    "plt.scatter(i, h, c=i, s=tamanho, marker=\"p\")\n",
    "\n",
    "plt.title(\"Relação entre altura e idade para bebês\") # Título do gráfico\n",
    "plt.xlabel(\"Idade (meses)\")                          # Legenda do eixo x\n",
    "plt.ylabel(\"Altura (cm)\")                            # Legenda do eixo y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficos de setores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para criar gráficos de setores, também chamados de gráficos de pizza ou *pie charts*, usaremos `plt.pie()`. Para exemplificar, vamos definir um dicionário que possui quatro modelos de carros como *keys* (`\"A\"`, `\"B\"`, `\"C\"` e `\"D\"`) e seus respectivos números de vendas (em um dado período, por exemplo, **setembro**) como *values*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "carros = {\n",
    "    \"A\":7500,\n",
    "    \"B\":10000,\n",
    "    \"C\":3200,\n",
    "    \"D\":2010\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, além de indicar quais dados queremos plotar (i.e., o número de vendas), é importante que passemos os argumentos:\n",
    "* `labels`: a legenda de cada setor;\n",
    "* `autopct`: o valor numérico a ser exibido em cada setor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x15fb2a34d08>,\n",
       "  <matplotlib.patches.Wedge at 0x15fb303ba88>,\n",
       "  <matplotlib.patches.Wedge at 0x15fb2f3b688>,\n",
       "  <matplotlib.patches.Wedge at 0x15fb302dfc8>],\n",
       " [Text(0.5591988451498489, 0.9472574368053678, 'A'),\n",
       "  Text(-1.0452651569607554, -0.3426671149144718, 'B'),\n",
       "  Text(0.595461042065891, -0.9248925058523305, 'C'),\n",
       "  Text(1.0577505872352349, -0.3019332628305733, 'D')],\n",
       " [Text(0.3050175518999175, 0.5166858746211097, '33.03%'),\n",
       "  Text(-0.5701446310695029, -0.1869093354078937, '44.03%'),\n",
       "  Text(0.3247969320359405, -0.5044868213739985, '14.09%'),\n",
       "  Text(0.5769548657646735, -0.16469087063485816, '8.85%')])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x381.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5.3))                  # Altera o tamanho da figura\n",
    "plt.title(\"Percentual de vendas por modelo\") # Título do gráfico\n",
    "\n",
    "plt.pie(carros.values(), labels=carros.keys(), autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Obs:** a *string* `'%1.2f%%'` obedece a uma sintaxe mais antiga do Python, basicamente:\n",
    "* `1` é o número mínimo de dígitos esperado para a *string*;\n",
    "* `.2f` exibe o número até a sua segunda casa decimal;\n",
    "* `%%` exibe o símbolo **%**.\n",
    "\n",
    "Lembre-se que você pode conhecer mais sobre outros argumentos através da [documentação](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.pie.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficos de barras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma forma alternativa de visualizarmos os dados anteriores é através de um gráfico de barras, fazemos isso através de `plt.bar()` e de modo análogo ao gráfico de setores, veja:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Número de vendas de automóveis em setembro\") # Título do gráfico\n",
    "plt.xlabel(\"Modelo\")                                    # Legenda do eixo x\n",
    "plt.ylabel(\"Unidades vendidas\")                         # Legenda do eixo y\n",
    "\n",
    "plt.bar(carros.keys(), carros.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma alternativa para permitir construções mais sofisticadas consiste em utilizar valores numéricos para o **eixo x** e, posteriormente, substitui-los por uma legenda. Imagine que precisamos representar as vendas dos modelos em dois períodos diferentes: em **setembro** e em **outubro**, por exemplo. Vamos primeiro definir um novo dicionário com os dados de outubro:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "carros2 = {\n",
    "    \"A\":8800,\n",
    "    \"B\":7930,\n",
    "    \"C\":3000,\n",
    "    \"D\":3020\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seria interesssante então exibir os dois períodos juntos (i.e., em um mesmo gráfico) para facilitar comparações. Para isso, especificaremos as posições de cada barra e suas espessuras através do argumento `width` (para evitar sobreposições). Façamos isso com os dados de setembro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HgN923cgsFfDNJNvb16fMd28B9gJfaFNy1yY5uuumZmE1cEPXTcxEVf0A+AzwFLAbeKGqvtltVy8yDGYpyeuBrwIfqaofd93PTFTVb6rqT+l9svv0JG/vuqfpJHkvsKeqtnfdywDOqqrT6H0D76VJ3tV1QwcxApwGXFNVpwI/AxbEV8i3Ka33Af/WdS8zkWQxvS/gPAl4M3B0kr/qtqsXGQaz0ObcvwpcX1U3d93PbLXL/28BKztu5WDOAt7X5t9vBN6d5F+7bWlmquqZ9r4H+Bq9b+SdzyaAib6rxS30wmEhOBe4v6qe7bqRGXoP8ERV7a2qXwM3A+/suKffMQxmqN2I3QjsqKrPdt3PTCUZTbKoLR9J7w/k97rtanpVdXlVLa2qMXrTAHdV1bz5DWoqSY5uDxfQplrOBh7utqvpVdUPgaeTnNxKK4B5/VBEnw+wQKaImqeAM5Mc1f49WUHv3uO8MC++jmKBOAv4IPBQm38H+HhV3d5hTzNxArC5PXXxGuCmqlowj2ouMMcDX+v9PWcE+HJVfaPblmbkw8D1bdrlceDijvs5qCRHAX8B/E3XvcxUVd2bZAtwP7AfeIB59NUUPloqSXKaSJJkGEiSMAwkSRgGkiQMA0kShoEkCcNAkgT8HwwObc8UcwxSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([2, 4, 6, 8], carros.values(), width=0.7) # Exibe as barras nas coordenas passadas e com 0.7 de espessura"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repetimos esse processo com os dados de outubro, mas considerando que as coordenadas do novo gráfico devem levar em conta a espessura das barras já plotadas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([2, 4, 6, 8], carros.values(), width=0.7)\n",
    "plt.bar([2.7, 4.7, 6.7, 8.7], carros2.values(), width=0.7) # coordenadas outubro = coordenadas setembro + width"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pronto, só precisamos dos ajustes finais:\n",
    "\n",
    "* `align`: altera o alinhamento entre as barras e os valores do **eixo x** — alteramos os dos dois gráficos de `\"center\"` (*default*) para `\"edge\"`, assim a legenda ficará entre ambas as barras.\n",
    "* `tick_label`: legenda que irá sobrepor os valores numéricos do **eixo x**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15fb48e1a48>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "leg = list(carros.keys()) # Cria uma lista dos modelos, será utilizada no lugar dos números do eixo x\n",
    "\n",
    "plt.bar([2, 4, 6, 8], carros.values(), width=0.7, align=\"edge\")\n",
    "plt.bar([2.7, 4.7, 6.7, 8.7], carros2.values(), width=0.7, tick_label=leg, align=\"edge\")\n",
    "\n",
    "plt.title(\"Número de vendas de automóveis\") # Título do gráfico\n",
    "plt.xlabel(\"Modelo\")                        # Legenda do eixo x\n",
    "plt.ylabel(\"Unidades vendidas\")             # Legenda do eixo y\n",
    "plt.legend((\"Setembro\",\"Outubro\"))          # Identificação de cada período"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso prefira, `plt.barh()` nos permite exibir essas mesmas informações, contudo, utilizando barras horizontais. Temos que tomar cuidado, entretanto, com os argumentos. Primeiro, passamos a legenda (uma lista dos modelos de carros, no nosso caso) e depois `width`, que passa a ser o valor correspondente a cada coluna (uma lista com o número de unidades vendidas por modelo, no nosso caso)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(leg, width=list(carros.values())) # leg = list(carros.keys()); a lista dos modelos usada a cima"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogramas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribuições de frequências relativas, os histogramas podem ser construídos através da função `plt.hist()`. Para demonstrar seu funcionamento, imaginemos as notas de prova de uma turma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "notas = [0, 1, 1.2, 1.5, 2, 2.6, 2.7, 3, 3.1, 3.2, 3.3, 4, 4.4, 4.5, 5, 5.3, 6.1, 7, 7.1, 9.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 3., 3., 4., 3., 2., 1., 2., 0., 1.]),\n",
       " array([0.  , 0.92, 1.84, 2.76, 3.68, 4.6 , 5.52, 6.44, 7.36, 8.28, 9.2 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(notas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um parâmetro de fundamental importância, `bins`, é usado para controlar o número de colunas do histograma. Note que não há um valor ideal pré-determinado, dependendo então de quais são os dados e da quantidade desses. Nesse exemplo, aumentando de 10 (*default*) para 13, pudemos notar a presença de um vale ao redor do valor 4 e de um pico no entorno de 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 2., 2., 2., 4., 1., 2., 2., 1., 1., 1., 0., 1.]),\n",
       " array([0.        , 0.70769231, 1.41538462, 2.12307692, 2.83076923,\n",
       "        3.53846154, 4.24615385, 4.95384615, 5.66153846, 6.36923077,\n",
       "        7.07692308, 7.78461538, 8.49230769, 9.2       ]),\n",
       " <a list of 13 Patch objects>)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(notas, bins=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagrama de caixa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trata-se de um método prático e muito eficiente de exibição e leitura dos dados. No diagrama são representados os limites superior e inferior da amostra — respectivamente, o maior e o menor valor (desconsiderando os *outliers*); os três **quartis** e os **outliers**. Antes de continuarmos, é importante conhecer os termos citados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quartis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simplificadamente, são valores que dividem uma dada amostra em pontos específicos. São três ao todo, a saber:\n",
    "* **Segundo Quartil:** a mediana, separa a \"metade menor\" da \"metade maior\" de uma amostra.\n",
    "* **Primeiro Quartil:** divide a \"metade menor\" ao meio.\n",
    "* **Terceiro Quartil:** divide a \"metade maior\" ao meio.\n",
    "\n",
    "Se considerarmos a sequência (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) saberemos os quartis determinando:\n",
    "* o valor central da amostra (mediana), que é equidistante ao 1 (primeiro termo) e ao 10 (último termo), portanto, **5.5** (média aritmética entre 5 e 6);\n",
    "* o valor central entre 1 (primeiro termo) e 5.5 (mediana), portanto, **3**;\n",
    "* o valor central entre 5.5 (mediana) e 10 (último termo), portanto, **8**.\n",
    "\n",
    "**Obs:** o método a cima é uma alternativa prática para a determinação dos quartis. Contudo, ao longo dos anos foram criados [diferentes métodos](https://i.ibb.co/W2pTMCM/Untitled.png) que, inclusive, não necessariamente resultam em valores iguais. Para um grande volume de dados a diferença é irrisória, mas talvez seja importante se preocupar com ela para pequenos *datasets*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Outliers*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trata-se de uma observação que se diferencia drasticamente das demais, são \"pontos fora da curva\", informações inconsistentes ou discrepantes. Geralmente eles dificultam e prejudicam a criação de modelos, mas isso nem sempre. Por exemplo, uma medição de 37°C é um *outlier* que prejudicaria o desempenho de um modelo destinado a prever temperaturas em uma cidade que registra máximas de até 24°C. Por outro lado, a busca por atletas olímpicos ou de altíssimo desempenho pode ser encarada como, na verdade, uma procura por *outliers*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, vamos usar a função `plt.boxplot()` para criar um diagrama com as notas da turma da seção anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x15fb5d785c8>,\n",
       "  <matplotlib.lines.Line2D at 0x15fb5d78d88>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x15fb5d78f08>,\n",
       "  <matplotlib.lines.Line2D at 0x15fb5d7cd08>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x15fb5d72bc8>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x15fb5d7ce88>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x15fb5d80c48>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(notas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Embora possa parecer não muito convencional, a leitura do diagrama é simples, na imagem abaixo temos uma legenda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: left;\" src=https://i.ibb.co/9VPdq2G/Picture1.png, width=410, height=537>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Obs:** os demarcadores que partem da caixa até cada um dos limites são chamados *whiskers*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mas não se esqueça que é sempre possível customizar os plots, para isso, usaremos:\n",
    "\n",
    "* `patch_artist`: habilita (quando `True`) ou desabilita (quando `False`, *default*) o  preenchimento da caixa com uma cor;\n",
    "* `boxprops`: propriedades da caixa;\n",
    "* `medianprops`: propriedades da mediana;\n",
    "* `whiskerprops`: propriedades dos *whiskers*;\n",
    "* `capprops`: propriedades dos demacadores dos limites;\n",
    "* `flierprops`: propriedades dos marcadores de *outliers*.\n",
    "\n",
    "Mas antes de passar os argumentos para a função, definiremos suas propriedades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "box = dict(linewidth=0, facecolor=\"dodgerblue\")                         # Sem contorno e azul\n",
    "mediana = dict(linewidth=2, color=\"red\")                                # Linha espessa em vermelho\n",
    "whisker = limites = dict(linewidth=2.5)                                 # Linhas espessas\n",
    "outliers = dict(marker=\"o\", markerfacecolor=\"darkorange\", markersize=8) # Círcular, laranja e com tamanho 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, basta plotar e, nesse caso, aplicar alguns ajustes para enriquecer a leitura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x15fb5dee588>,\n",
       "  <matplotlib.lines.Line2D at 0x15fb5deee48>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x15fb5deefc8>,\n",
       "  <matplotlib.lines.Line2D at 0x15fb5df3ac8>],\n",
       " 'boxes': [<matplotlib.patches.PathPatch at 0x15fb5dee188>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x15fb5de35c8>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x15fb5de0bc8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x381.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5.3))                    # Altera o tamanho da figura\n",
    "plt.grid(axis=\"y\")                             # Exibe a grade somente para o eixo y\n",
    "plt.yticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # Valores exibidos no eixo y\n",
    "\n",
    "plt.boxplot(notas, patch_artist=True, boxprops=box, medianprops=mediana, whiskerprops=whisker, capprops=limites, flierprops=outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Alt text](https://gistcdn.githack.com/Gbecdox/178a8d0cd024d5e52d63e3c11a2cdfd7/raw/3f32b8a31509ec3b2b20ebb202113b2da0e9c751/FooterGM.svg)\n",
    "***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}