Introduction_to_Machine_Learning_and_Toolkit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "# Introdução a Aprendizado de Máquina"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "## Introdução\n",
    "\n",
    "Utilizaremos a base de dados iris para esse tutorial. Essa é uma base de dados bastante conhecida contendo medidas das pétalas e sépalas da flor de íris. Os dados estão contidos em um arquivo chamado `Iris_Data.csv` encontrado no diretório [data](data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "data_path = ['data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "## Exercício 1\n",
    "\n",
    "Carregue os dados do arquivo usando as técnicas do tutorial *Introduction_to_Machine_Learning_and_Toolkit*. Examine o arquivo.\n",
    "\n",
    "Indique o seguinte:\n",
    "\n",
    "* O número de pontos (linhas) (*Dica:* verifique o atributo `.shape` do dataframe.)\n",
    "* O nome das colunas. (*Dica:* verifique o atributo `.columns` do dataframe.)\n",
    "* Os tipos associados a cada coluna. (*Dica:* verifique o atributo `.dtypes` do dataframe.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(12,9)})\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-19T23:17:21.954025Z",
     "start_time": "2017-03-19T19:17:21.936220-04:00"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [],
   "source": [
    "#Lendo o arquivo\n",
    "filepath = 'data/Iris_Data.csv'\n",
    "data = pd.read_csv(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O número de linhas do dataset é 150\n",
      "As colunas do dataset são: sepal_length, sepal_width, petal_length, petal_width, species\n",
      "Os tipos de valores no dataset são: float64, float64, float64, float64, object\n"
     ]
    }
   ],
   "source": [
    "#numero de linhas\n",
    "numberLines=data.shape[0]\n",
    "print 'O número de linhas do dataset é '+str(numberLines)\n",
    "\n",
    "#nome das colunas\n",
    "columnNames=data.columns.values\n",
    "print 'As colunas do dataset são: '+', '.join(map(str, columnNames))\n",
    "\n",
    "#tipos de valores\n",
    "print 'Os tipos de valores no dataset são: '+', '.join(map(str, data.dtypes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "## Exercício 2\n",
    "\n",
    "Examine os nomes das espécies e note que todas elas começam com 'Iris-'. Remova essa parte do nome para tornar o nome mais curto. \n",
    "\n",
    "*Dica:* tem várias maneiras de fazer isso, você pode usar o [string processing methods](http://pandas.pydata.org/pandas-docs/stable/text.html) ou o [apply method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.apply.html), por exemplo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-19T23:17:53.678316Z",
     "start_time": "2017-03-19T19:17:53.660202-04:00"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [],
   "source": [
    "data['species']=data.species.str.replace('Iris-', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "## Exercício 3\n",
    "\n",
    "Determine:\n",
    "* A quantidade de cada espécie presente na base. (*Dica:* verifique o método `.value_counts` da série.)\n",
    "* A média, mediana, quantis e faixas (máximo-mínimo) para cada medida de pétala e sépala.\n",
    "\n",
    "*Dica:* para a última parte, o método `.describe` tem mediana, mas é chamada de *50%* quantile. `.describe` não tem faixa, portanto você precisará criar uma nova entrada na tabela com `max - min`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-19T23:20:16.483215Z",
     "start_time": "2017-03-19T19:20:16.472865-04:00"
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     "marked": true
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    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa        50\n",
      "versicolor    50\n",
      "virginica     50\n",
      "Name: species, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#quantidade de cada especie presente na base\n",
    "print data.species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>range</th>\n",
       "      <td>3.600000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "max        7.900000     4.400000      6.900000     2.500000\n",
       "range      3.600000     2.400000      5.900000     2.400000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concat into the table \n",
    "#A média, mediana, quantis e faixas (máximo-mínimo) para cada medida de pétala e sépala.\n",
    "#Creating dataframe with description\n",
    "dataDescription=data.describe(percentiles=[])\n",
    "dataDescription.loc['range']=dataDescription.apply(lambda x: x.loc['max']-x.loc['min'])\n",
    "dataDescription"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "## Exercício 4\n",
    "\n",
    "Calcule **para cada espécie** em um dataframe separado:\n",
    "\n",
    "* A média de cada medida (sepal_length, sepal_width, petal_length, e petal_width).\n",
    "* A mediana dessas medidas.\n",
    "\n",
    "*Dica:* você pode usar o método [`groupby`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) do Pandas para agrupar as espécies antes de calcular as estatísticas.\n",
    "\n",
    "Se você terminar ambos os cálculos, tente calcular a média e mediana em uma única tabela (com uma única chamada de groupby). Veja a seção da documentação [applying multiple functions at once](http://pandas.pydata.org/pandas-docs/stable/groupby.html#applying-multiple-functions-at-once) para algumas dicas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-19T23:22:07.306171Z",
     "start_time": "2017-03-19T19:22:07.286965-04:00"
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    "scrolled": true
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   "outputs": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">sepal_length</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sepal_width</th>\n",
       "      <th colspan=\"2\" halign=\"left\">petal_length</th>\n",
       "      <th colspan=\"2\" halign=\"left\">petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</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>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.418</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.464</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.770</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.260</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.326</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.974</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.552</td>\n",
       "      <td>5.55</td>\n",
       "      <td>2.026</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sepal_length        sepal_width        petal_length         \\\n",
       "                   mean median        mean median         mean median   \n",
       "species                                                                 \n",
       "setosa            5.006    5.0       3.418    3.4        1.464   1.50   \n",
       "versicolor        5.936    5.9       2.770    2.8        4.260   4.35   \n",
       "virginica         6.588    6.5       2.974    3.0        5.552   5.55   \n",
       "\n",
       "           petal_width         \n",
       "                  mean median  \n",
       "species                        \n",
       "setosa           0.244    0.2  \n",
       "versicolor       1.326    1.3  \n",
       "virginica        2.026    2.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A média de cada medida (sepal_length, sepal_width, petal_length, e petal_width)\n",
    "\n",
    "data.groupby('species').agg([np.mean, np.median])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 5\n",
    "\n",
    "Faça um scatter plot de `sepal_length` vs `sepal_width` usando Matplotlib. Coloque um `label` nos eixos e dê um título ao gráfico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-19T23:23:36.914375Z",
     "start_time": "2017-03-19T19:23:36.661322-04:00"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef740d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.sepal_length, data.sepal_width, c='g')\n",
    "plt.xlabel('sepal_length')\n",
    "plt.ylabel('sepal_width')\n",
    "plt.title('sepal_length vs sepal_width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 6\n",
    "\n",
    "Faça um histograma de qualquer um dos quatro atributos. Nomeie os eixos e dê um título apropriado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5e8c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data.petal_length, color='r')\n",
    "plt.xlabel('petal_length')\n",
    "plt.ylabel('amount')\n",
    "plt.title('Distribution of petal_length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 7\n",
    "\n",
    "Agora crie um único gráfico com histogramas para cada atributo (`petal_width`, `petal_length`, `sepal_width`, `sepal_length`) sobrepostos. \n",
    "\n",
    "Dicas para fazer isso com os métodos de plotagem do Pandas se encontram em [visualization guide](http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ef74810>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD3CAYAAAAQYlNPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAHfBJREFUeJzt3Xl0FFX6//F3QgKYsEMgLA6IyHXBbVxRNjdcEWd+M8pP/Yqi4AIzOl+OKAgDKiruiogIsqrjOoKgMDIuIMwog4OIIDwMogFlEUJwWLOQfP/oJoaQkO5Od3XT9Xmdk3Oqqyv3PqnuztP3VtVTKSUlJYiIiH+lxjsAERGJLyUCERGfUyIQEfE5JQIREZ9TIhAR8bm0eAcQiS1bdoR8qlPDhhnk5e2OZTgRU2yRUWyRUWyRSabYsrLqplS0PulHBGlpNeIdQqUUW2QUW2QUW2T8EFtMRwTOubOAR82sm3OuHTAFKAGWA/3NrNg5Nxy4HCgC7jKzf8UyJhEROVDMRgTOuUHAS0Dt4KqngKFm1hlIAXo6534NdAXOAnoBz8cqHhERqVgsRwTfAr8FXg4+Pg2YH1yeA3QHDJhrZiXAOudcmnMuy8y2HKrhhg0zwhoSZWXVDTd2zyi2yCi2yCi2yCR7bDFLBGb2V+dcmzKrUoL/8AF2APWBekBumW32rz9kIgjz4AhbtuwIeXsvKbbIKLbIKLbIJFNslSUNLw8WF5dZrgtsB/4bXC6/XkREPOJlIvjSOdctuHwpsAD4B3Cxcy7VOfcrINXMtnoYk4iI73l5HcFAYIJzriawEnjbzPY55xYAnxFISv09jEdERIhxIjCz74Gzg8urCZwhVH6bEcCIWMYhIollxoK1BzzOzKzFrl35Ebd3Vee21Q3J15L+gjIRkVh46KERfP75Pyt9fsCAfuTkfB+Vvr79dg1Lly4B4He/60F+fuRJsyKHZYkJOTy8v3ZuTNrN2FyL3VV8e7y8bfeY9C0SD/PmfUTjxo055ZRfx6R9JQIRSXrr1uXwyCP3U6NGGsXFxQwfPpLp09/mq6++pLi4mGuuuY7zz7+QAQP60bp1m9Jv8vff/zCNGmUwatSD/PTTZnJzt3LuuV3o1++OkPveuXMno0Y9wM8//wzAXXfdzdFHt6NXr99w4okns25dDo0aNWLkyMcoKirkwQeHk5u7haZNm7F06Ze89NI05sx5j7S0dNq3PxaAJ58cxYYNP1KzZhojRoyiXr161do/mhoSkaS3ePEijjvuBJ55Ziw333wrCxbMY+PGH3nhhYmMHj2OadMmsWNH4Hz8Dh1OYsyY8Zx//kW8/PJkNm7cyAknnMhTT41h/PipvPvuX8Pqe9q0SZx22pk899yLDBp0H0888QgAGzb8yC233MaLL05m+/Y8Vq78hnffnU6LFi144YVJ9OlzK3l528jKasqll15Br17XcvzxHQC4/PKejBkznpYtW7J48aJq7x+NCEQk6V1xRU9efXUqAwf+gczMOhxzTHvMVjFgQD8AioqK2LRpAwCnnXYGACeeeBILF86nQYMGrFy5giVLviAzM5OCgsKw+l67dg1LlnzBRx8Fpkp37PgvAPXrN6BZs2wAmjZtRkFBPjk533HWWecA0Lp1Gxo0aFhhm8ceGxgZNGnShPz8vWHFUxElAhFJegsXzufkk0+lT59+/P3vf2P8+LGcfvpZ3HPPfRQXFzNlyku0bNkKALOVNG3ajGXLvuKoo9ryzjvvUKdOXQYNuo8ffljPzJnTKSkJuRI+rVu3oXv34+ne/RLy8rYxa9YMAFJSDq4I3bbt0SxfvowuXbrx448/8PPPgetrU1NTKS4u22eF1aQjpkQgIp4rf7pnrMs4HHvs8YwcOZypUydSXFzMgw8+yt//Poc77riFPXt206XLeWRkZAIwe/Z7vPHGX6hduzbDhj1AScleXn/9TVas+Jr09HRatTqSrVsPWQXnADfc0IdRox5k5sx32L17F3369Kt02yuu6MlDD91P//59yc7OpmbNmgA4dxxjxz5LmzZHVW9HVCIlnMyWKMK5MU0y1QnxUjRii9lZQ5mJe9ZQsr+msZIosQ0Y0I+77x5C69ZtStd5GdvXX3/Fnj17OPPMs1m/fh0DB/6BN998t9LtI6g1VOFQQiMCEZEIbdq0iZEj/3zQ+lNPPY2bb7417PZatGjJiBH3MXnyeIqKivjf/70nGmFWSYlARCRozJjxYW2fnZ0d9u8cSuPGTXjuuRej1l6odPqoiIjPKRGIiPicEoGIiM/pGIGIeK78GWWh1I86FNWWqh6NCEREIlBV9dFDmT17FgsXzj9o/ZVXXgwEzkZauPBTILpVTCujEYGIiMcuu6zHIZ9fsmQxOTnf06lTF0/iUSIQkaTndfXR//xnNRMmjOWxx57hww8/4OWXJzN16ussW7aUOXPep0mTJjRu3JgePX7DY489xHffraVly1YUFBSwb98+XnllCnv37uXEE08CYNKk8eTlbWPPnj2MGPFQaTmMaNHUkIgkPa+rjx5zTHs2bdpIQUEBn3/+T1JSUtm2LZeFC+fTtet5pdt9+uknFBQUMH78FG69dQD5+XupUaMG119/IxdddAmdOgVu6njOOZ0YPXocZ599DvPmfRT1/aMRgYgkvXhUHz3zzI4sWfIFP/20me7dL+GLL/7FV18tpV+//qxY8TUA69ev47jjTgACF6c1bdqswracOw6Axo0bk5ubG/mOqIRGBCKS9PZXH3322Rc477wLmD17Fqeeejpjxoxn9OhxnH/+hQdUHwUOqj46fPhIevW6nvz8vSFVH+3SpRuvvDKFo48+hjPP7Mjbb79Bq1atSEv75ft3mzZtWbFiGQBbt25hy5ZAMbuUlBRKSopLt6uoUmk0aUQgIp4rf7pnMlYf7dDhJNavz+G6626gXbtj2Lx5E9dd1/uAbTp37srixYvo27c32dnNadCgAQBHH92OadMmld6RLNZUfTSOkj02VR9NLIqtavGuPhouVR8VEYmzaFcfjRclAhGRoHhXH40XHSwWEfE5JQIREZ9TIhAR8TkdIxARz219d/oBj/dk1mJXNaqPNun5m+qG5GsaEYiIBOXn5zNr1oxDbvO73/UgP7/ypLW/gmg0zJ//CVu3bmHjxg3063dj1NotT4lARCRo27bcKhOBl9566zV27doV8340NSQiSW/27FksWDCP3bt3s337dm666Rbq12/A+PFjqVGjBi1atGTQoPuYNm0S33//HZMnT+Dyy6/kiSdGAfvYuHETffveQZcu3ULu89tv1/DMM49TUlJC/fr1GTx4OKtXr+LVV6eRnp7Ghg0/csEF3end+2Z++GE9Dz00grS0NLKzm7Nx4wauvfYG1qxZzciRf2bYsAfZvj2PwYMHsnXrVtq1O4Z77hkatf2jRCAivrBnzx6efvp5tm/Po2/f3qSm1mD8+Mk0bNiICRNeYPbsWdxwQx++/XYNN93Ul8WLF9Gr13VcfPF5fPzxQiZOfDGsRPDooyMZPPjPHHVUW957bwavvjqVM844i82bNzJlymsUFhZy1VWX0Lv3zTz//LPccMNNdOzYiZkzp7Nx4wbOOacT7dq15+67h5Cens7u3bsYPHg4derU4ZprfkNe3jaysupGZd8oEYiIL5xyyq9JTU2lUaPGHHHEEaxfv45hw+4FAscGzjjjrAO2b9y4CVOnTuTDD2eTn19EUVFRWP3l5HzHk0+OAmDfviJatfoVAG3btiMtLY20tDRq1apdum2HDicDcPLJpzJ37pyD2mvevCX16tUDoGHDhuzduzeseA5FiUBEfMFsFRA4DpCfX0CrVkcyatRT1KlTh4UL53PEERmkpKSWVv186aVx9OhxFVdeeQlTprzKnDnvhdXfr37VmqFDHyA7O5tly5aSm7sVgIoKibZtezTLly+jY8dzS0tUA6SmplJcXBz8vdhVIPU0ETjn0oGpQBtgH9AXKAKmACXAcqC/mRVX0oSIJIHyp3t6Udht27Zc7rzzdnbu3MnAgfeQmprK3XffSUlJCRkZmQwbdj8ZGZkUFhYxduxozjvvAp5//lneeONlGjRozPbt28Pqb+DAwYwc+Wf27dtHSkoK9947rNKqpbff/kceeeQBXn/9FTIz65SWqu7Q4SRGjhzOoEH3VfvvPxRPq48653oC15nZ1c65i4DbgHTgKTOb55wbB3xgZtMP1Y6qj8aeqo9GJtlf01iJdWyzZ88iJ+d7br/9D2H/rhf7be7cORx/fAdatTqSWbNm8PXXXzFkyPCox5Yo1UdXA2nOuVSgHlAInA3MDz4/B+gOHDIRiIjE08KF83n99VcPWv/73///A25FGaqmTZsxfPgQateuTWpqKvfeOywaYYbM6xHBkcC7QB2gCXAF8LaZtQg+fz7Qx8yuP1Q7RUX7StLSasQ6XKmmN5eHN6caTVd3uCJufYsksIQYEfyJwNTP4GBS+BioWeb5ukCVE3F5ebtD7tDPw+HqiEZsVU3fRCqUqaF47ddkf01jRbFFJoKpoQrXe31lcR7wc3B5G4HjA18657oF110KLPA4JhERX/N6RPA0MMk5t4DASGAI8AUwwTlXE1gJvO1xTCIivuZpIjCzncDVFTzV1cs4RCS+Fi/47oDHoUz3HcoZnY+qbki+pqJzIiJB0ag+eijPPvskmzZtOmBdTs73DBjQD4ClS5ewZs1/gOhWMa2KEoGISFCsq4/eeedAsrOzK33+/fdnVnrRWSypxISIJD2vqo+++eZrFBUVce21/8Pjjz9Meno6d911N1OnTqR585bMnPkOd989hMzMOjzwwFBKSkpo1KgxAKtWrWTRos9YvXoVbdq0paCggBEj7mPz5k3Ur1+fkSMfK73iONo0IhARX9hfffTpp8fw3HNP8/DDD/Dww48zZsx4srKallYfbdPmKG66qS85Od/Tq9d1TJ48mUGD7uOdd96sso8uXc5j0aLPAFi3LocVK5YDsGjRZ5x7bqfS7aZNm8iFF17Mc8/9UtH02GOP46yzOnL77X8kOzubPXt2c+ut/XnhhYns3LmT1atXRX+nBGlEICK+4EX10ezsbPLz9/LNN8tp3foofvppEytXrqBOnTpkZtYp3W79+nX06BGot3TiiSczffrBJ0vWq1ef5s1bBGNpHNVqo+UpEYiIL3hVfbRjx3MZO3Y0V199LZs3b+Lppx/nyiuvOmCbNm3asmLFMo45pj0rV35Tuj4lJaW0/1hWGy1PiUBEPFf+dM9kqj7atev5TJo0nkcffYrc3K2MGfM0nTo9c8A2vXvfzAMPDOXDD+fSokXL0vXHH9+BcePG0Lx5y/LNxpSntYaiRdVHY0/VRyOT7K9prPi9+mikDtfqoyIih71oVx+NNyUCEUl6l13WI6rtderUlU6dkqcggk4fFRHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERn1MiEBHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERn1MiEBHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERn1MiEBHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERn0sLZSPn3GxgMjDDzApjG5KIiHgppEQAjAJ6A487594HppjZ4kg6dM4NBq4EagJjgfnAFKAEWA70N7PiSNoWEZHwhTQ1ZGafmtnNwHHA58BfnXPLnXN3OedqhdqZc64bcA5wLtAVOBJ4ChhqZp2BFKBneH+CiIhUR8jHCIL/xMcADwN/A/4INANmhtHfxcDXwHRgFvAecBqBUQHAHODCMNoTEZFqCvUYQQ6wlsBxggFmtie4fh7wRRj9NQFaA1cARxFIIqlmVhJ8fgdQv6pGGjbMIC2tRsidZmXVDSNEbyVzbBmbQx4sht925qHbjud+TebXNJYUW2SiEVuoxwjOB3aY2U/OuSOcc+3MbE1wLv/XYfSXC6wyswLAnHN7CUwP7VcX2F5VI3l5u0PuMCurLlu27AgjRO8ke2y7d+VHKZoDZWTWqrLteO3XZH9NY0WxRSbc2CpLGqFODV1OYDoIoCkwyznXL+Tef7EQuMQ5l+KcawFkAh8Fp50ALgUWRNCuiIhEKNRE0A/oDGBmOQTm9f8Qbmdm9h7wJfAvAscI+gMDgfudc58ROJPo7XDbFRGRyIU6NZQOlB2LFxA43TNsZjaogtVdI2lLRESqL9REMAP42Dn3ZvDxbwnvbCEREUlQoV5HcA8wGnBAW2C0mQ2NZWAiIuKNcGoNrQTeJDA62Oac6xKbkERExEuhXkfwPNAD+LbM6hICp5WKiMhhLNRjBN0Bt/9CMhERSR6hTg2tJVAHSEREkkyoI4JtwDfOuX8Ce/evNLM+MYlKREQ8E2oi+Bu/XFksIiJJJKREYGZTnXNtgBOAD4Ajzey7WAYmIiLeCOkYgXPuGgIlIZ4FGgGfOeeuj2VgIiLijVAPFt9D4IYyO8zsJ+BUYHDMohIREc+Emgj2mVlprVMz2wjodpIiIkkg1IPFK5xzA4B059wpwB3A0tiFlXwWLzj4kEoodfWj4YzOR8W8DxE5fIU6IugPtAT2AJOA/xJIBiIicpgL9ayhXQSOCei4gIhIkgm11lAxB99/YKOZtYp+SCIi4qVQRwSlU0jOuXTgKqBjrIKKpYrm6kVE/CycMtQAmFmhmb2FKo+KiCSFUKeGbijzMIXAFcYFMYlIRA4rMxasrXYbmZm12BXBGXRXdW5b7b4l9NNHzyuzXAJsBa6JfjgiIuK1UI8R3BTrQESkeqr6Zh7pt25JfqFODX3HwWcNQWCaqMTMND4TETlMhTo19BcgH5gAFALXAWcA98UoLhER8UioieBiMzu9zONnnXP/NrOcWAQlIiLeCfX00RTn3IX7HzjnriBQZkJERA5zoY4I+gHTnHPZBI4VrAJ6xywqERHxTKhnDf0bOME51wTYa2Y7YxuWiIh4JdSzhloDLwFtgM7OuZlAHzP7PnahSbREUlYjKiWyW1bv10XEG6EeI3gReBzYCWwGXgOmxSooERHxTqjHCJqY2Vzn3KNmVgJMcM71j2VgyWa3rTpoXWF6GoWFRTHvO8MdG/M+ROTwFeqIYI9zrhXBi8qcc50IXFcgIiKHuVBHBH8C3gOOds4tBRoBv49ZVCIi4plQE0EzAlcStwdqAKvMTNVHRUSSQKiJ4DEzex9YEctgRETEe6Emgm+dc5OARQRuYA+AmenMIRGRw9whE4FzrqWZ/QjkEqg0enaZp0uI8BRS51xT4N/ARUARMCXY3nKgv5kVR9KuiIiEr6oRwSzg12Z2k3NuoJk9Wd0Og/c8fpFfRhZPAUPNbJ5zbhzQE5he3X5ERCQ0VSWClDLL1wHVTgTAE8A4YHDw8WnA/ODyHKA7VSSChg0zSEurEXKHWVl1S5czMmuFEWr0FKZXvKvTK1kfTZH+zdXdV7Hc11W1XfY191q8+s4MYX+Hsk28RBKbV/s6nu+nqkQjtqr+C5W9GU1KpVuFyDl3I7DFzD5wzu1PBCnBi9QAdgD1q2onL293yH1mZdVly5YdpY+rXTYhQhVdOJbu0QVlkfzN0SgxEat9HUpsZV9zL5V/v3mpqruPJfIdyiKNzYt9Hc/XtCrhxlZZ0gjn62hFdygLVx+gJFjS+hQCxxialnm+LrA9Cv2IiEiIqkoEJzjn9t8ItWWZ5YhuUWlmXfYvO+fmAbcBjzvnupnZPOBS4JNw2hQRkeqpKhG09yCGgQRqF9UEVgJve9CniIgEHTIRxPJWlGbWrczDrrHqR/zp/bVz49LvjVn/Ly79ilRHqEXnREQkSSkRiIj4nBKBiIjPKRGIiPicEoGIiM8pEYiI+JwSgYiIz8W+4pmIj6x77Y341fNpdHJ8+pXDnkYEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLiczp9VEQOWzMWrK16o2qq6DaaV3UO655cCU8jAhERn9OIQCRJZC2dd8jn09PTyCgsinq/W07pFvU2xVsaEYiI+JwSgYiIzykRiIj4nBKBiIjPKRGIiPicEoGIiM8pEYiI+JwSgYiIzykRiIj4nBKBiIjPKRGIiPicEoGIiM8pEYiI+JwSgYiIzykRiIj4nBKBiIjPKRGIiPicp3coc86lA5OANkAtYCTwDTAFKAGWA/3NrNjLuERE/MzrEcH1QK6ZdQYuAcYATwFDg+tSgJ4exyQi4mteJ4K3gGHB5RSgCDgNmB9cNwe40OOYRER8zdOpITPbCeCcqwu8DQwFnjCzkuAmO4D6VbXTsGEGaWk1Qu43K6tu6XJGZq0wIo6ewvSKd3V6JeujKdK/ubr7Kpb7Ol6vY9WKyIxTbKG8l2LxfmuxYmFU2qnyg1+Bn8+8ICp9V6X8a1r2f0q8RSMWTxMBgHPuSGA6MNbM/uKce6zM03WB7VW1kZe3O+T+srLqsmXLjtLHu3flhx5sFBUWFh20Lj09rcL10RbJ35yRWava+ypW+zoascXSrgR6j5Xl1fstEpHG5sW+zsysdVA/Zf+nxFP5/2+hbF8RT6eGnHPNgLnAPWY2Kbj6S+dct+DypcACL2MSEfE7r0cEQ4CGwDDn3P5jBXcCo51zNYGVBKaMRETEI14fI7iTwD/+8rp6GYeIiPxCF5SJiPicEoGIiM8pEYiI+JwSgYiIzykRiIj4nBKBiIjPKRGIiPicEoGIiM8pEYiI+JwSgYiIzykRiIj4nBKBiIjPKRGIiPicEoGIiM95focy8d7qvLVh/076rhoUFuyrVr8NWlbr1w9LX6/ZmrB3AZPombEg/M9UNFzVuW1M2tWIQETE5zQikKTU6NNlceq5aZz6FYmcRgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nO6oMwHjsjZHPbv1KiRStq+4up13KFJ9X5fRDyhEYGIiM9pRCAih62spfNi3kd6ehoZ5QoJbjmlW8z79ZJGBCIiPqcRgUgUbS36gX3F1Ty2EqFGqS3i0q8c/jQiEBHxOY0IJGa2L68Rk3Z31SyhsODQbTeKSc8iyUkjAhERn1MiEBHxOSUCERGfS4hjBM65VGAscDKQD9xiZmviG5WIiD8kRCIArgJqm1lH59zZwJNAzzjHJNUUSWmLUIRU/qJuTLoWSUqJMjXUCfgbgJl9Dpwe33BERPwjUUYE9YCfyzze55xLM7OiijbOyqqbEk7jWVm/fD287LcnRRRg9cWrXxFJZmX/v0UqUUYE/+XAwXxqZUlARESiK1ESwT+AywCCxwi+jm84IiL+kShTQ9OBi5xz/wRSgJviHI+IiG+klJSUxDsGERGJo0SZGhIRkThRIhAR8TklAhERn0uUg8VRl+hlK5xzZwGPmlm3eMdSlnMuHZgEtAFqASPNbGZcgwpyztUAJgAOKAFuM7Pl8Y3qQM65psC/gYvMbFW849nPObeEwGnaAN+ZWcKckOGcGwxcCdQExprZxDiHBIBz7kbgxuDD2sApQLaZbY9XTPsFP6dTCXxO9wF9q/N+S+YRQWnZCuBeAmUrEoJzbhDwEoE3V6K5Hsg1s87AJcCYOMdTVg8AMzsXGAo8FN9wDhT8cL4I7Il3LGU552oDKWbWLfiTSEmgG3AOcC7QFTgyrgGVYWZT9u8zAsn9j4mQBIIuA9LM7BzgAar5WUjmRJDIZSu+BX4b7yAq8RYwLLicAiTMhX1mNgPoF3zYGkiUD+V+TwDjgA3xDqSck4EM59xc59zHwWt1EsXFBK4bmg7MAt6LbzgHc86dDpxgZuPjHUsZq4G04MxHPaCwOo0lcyKosGxFvIIpy8z+SjVfuFgxs51mtsM5Vxd4m8A374RhZkXOuanAc8Cr8Y5nv+A0whYz+yDesVRgN4EkdTFwG/BqonwWgCYEvqT9nl9iC6uEjAeGAPfHO4hydhKYFlpFYLp0dHUaS+ZEoLIVEXLOHQl8ArxsZn+JdzzlmVlvoD0wwTmXGe94gvoQuChyHoG55GnOuez4hlRqNfCKmZWY2WogF2ge55j2ywU+MLMCMzNgL5AV55hKOecaAM7MPol3LOX8icB+a09gxDc1OAUYkUT5VhAL/yAwp/ymylaEzjnXDJgLDDCzj+IdT1nOuf8BWpnZIwS+5RYHf+LOzLrsXw4mg9vMbFP8IjpAH+BE4A7nXAsCo+WN8Q2p1ELgTufcUwSSUyaB5JAougAJ9TkIyuOXWYVtQDoQ8U3CkzkRqGxFZIYADYFhzrn9xwouNbNEOAD6DjDZOfcpgTf+XQkSV6KbCExxzi0kcLZVn0QZHZvZe865LsC/CMxQ9DezfXEOqywHrI13EBV4GpjknFtA4GyrIWa2K9LGVGJCRMTnkvkYgYiIhECJQETE55QIRER8TolARMTnlAhERHxOiUBExOeUCEREfO7/ACBIfNyXSRAkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f647390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.hist(alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 8\n",
    "\n",
    "Usando Pandas, faça um boxplot de cada medida da pétala e sépala. Veja a documentação para algumas dicas [Pandas boxplot method](http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html#visualization-box)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f0cfd10>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f45e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = dict(boxes='Purple', whiskers='DarkOrange',medians='DarkBlue', caps='Gray')\n",
    "data.plot.box(color=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 9\n",
    "\n",
    "Agora faça um único boxplot onde os atributos estão separados no eixo-x e as espécies são coloridas por tons de cores diferentes.\n",
    "\n",
    "*Dica:* veja a documentação para [Seaborn boxplots](http://seaborn.pydata.org/generated/seaborn.boxplot.html). \n",
    "\n",
    "Note que o Seaborn é bem exigente quanto ao formato dos dados--para esse plot funcionar, o dataframe de entrada precisa ser manipulado para que cada linha contenha um único ponto (uma espécie, um tipo de medida e um valor da medida). Veja o Pandas [stack](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.stack.html) como uma dica.\n",
    "\n",
    "Um exemplo desse formato para essa base:\n",
    "\n",
    "|   | species | measurement  | size |\n",
    "| - | ------- | ------------ | ---- |\n",
    "| 0\t| setosa  | sepal_length | 5.1  |\n",
    "| 1\t| setosa  | sepal_width  | 3.5  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFormatted=data.set_index('species').stack().reset_index()\n",
    "#renaming dataframe\n",
    "dataFormatted.rename(columns={'level_1': 'measurement', 0: 'size'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe54910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"species\", y=\"size\", hue='measurement',data=dataFormatted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 10\n",
    "\n",
    "Faça um  [pairplot](http://seaborn.pydata.org/generated/seaborn.pairplot.html) com o Seaborn para examinar a correlação entre cada uma das medidas.\n",
    "\n",
    "*Dica:* esse plot pode parecer complicado, mas é na verdade uma única linha de código. Esse é o poder de expressabilidade do Seaborn junto do Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x10fccc710>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fccc790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data, hue=\"species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}