{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 9</font>\n",
    "\n",
    "## Download: http://github.com/dsacademybr\n",
    "\n",
    "## Exercício: Análise Exploratória de Dados com Python\n",
    "\n",
    "Neste exercício, você vai realizar uma análise exploratória em um dos mais famosos datasets para Machine Learning, o dataset iris com informações sobre 3 tipos de plantas. Esse dataset é comumente usado em problemas de Machine Learning de classificação, quando nosso objetivo é prever a classe dos dados. No caso deste dataset, prever a categoria de uma planta a partir de medidas da planta (sepal e petal).\n",
    "\n",
    "Em cada célula, você encontra a tarefa a ser realizada. Faça todo o exercício e depois compare com a solução proposta.\n",
    "\n",
    "Dataset (já disponível com o Scikit-Learn): https://archive.ics.uci.edu/ml/datasets/iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "%matplotlib inline\n",
    "\n",
    "fontsize = 14\n",
    "ticklabelsize = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando o dataset\n",
    "iris = load_iris()\n",
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "print(len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extração e Transformação de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imprima os valores numéricos da Variável target (o que queremos prever), \n",
    "# uma de 3 possíveis categorias de plantas: setosa, versicolor ou virginica\n",
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imprima os valores numéricos da Variável target (o que queremos prever), \n",
    "# uma de 3 possíveis categorias de plantas: 0, 1 ou 2\n",
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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",
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       "    <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "  species  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adicione ao dataset uma nova coluna com os nomes das espécies, pois é isso que vamos tentar prever (variável target)\n",
    "df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "      <th>target</th>\n",
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       "    <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",
       "      <td>0</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",
       "      <td>0</td>\n",
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       "    <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",
       "      <td>0</td>\n",
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       "      <td>3.1</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "  species  target  \n",
       "0  setosa       0  \n",
       "1  setosa       0  \n",
       "2  setosa       0  \n",
       "3  setosa       0  \n",
       "4  setosa       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inclua no dataset uma coluna com os valores numéricos da variável target\n",
    "df['target'] = iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
       "       'petal width (cm)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraia as features (atributos) do dataset e imprima \n",
    "features = df.columns[:4]\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>5.006</td>\n",
       "      <td>5.936</td>\n",
       "      <td>6.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>3.418</td>\n",
       "      <td>2.770</td>\n",
       "      <td>2.974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>1.464</td>\n",
       "      <td>4.260</td>\n",
       "      <td>5.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>0.244</td>\n",
       "      <td>1.326</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                 0      1      2\n",
       "sepal length (cm)  5.006  5.936  6.588\n",
       "sepal width (cm)   3.418  2.770  2.974\n",
       "petal length (cm)  1.464  4.260  5.552\n",
       "petal width (cm)   0.244  1.326  2.026"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcule a média de cada feature para as 3 classes\n",
    "df.groupby('target').mean().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploração de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>9</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>5.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "                        0       1       2       3       4       5       6  \\\n",
       "sepal length (cm)     5.1     4.9     4.7     4.6       5     5.4     4.6   \n",
       "sepal width (cm)      3.5       3     3.2     3.1     3.6     3.9     3.4   \n",
       "petal length (cm)     1.4     1.4     1.3     1.5     1.4     1.7     1.4   \n",
       "petal width (cm)      0.2     0.2     0.2     0.2     0.2     0.4     0.3   \n",
       "species            setosa  setosa  setosa  setosa  setosa  setosa  setosa   \n",
       "target                  0       0       0       0       0       0       0   \n",
       "\n",
       "                        7       8       9  \n",
       "sepal length (cm)       5     4.4     4.9  \n",
       "sepal width (cm)      3.4     2.9     3.1  \n",
       "petal length (cm)     1.5     1.4     1.5  \n",
       "petal width (cm)      0.2     0.2     0.1  \n",
       "species            setosa  setosa  setosa  \n",
       "target                  0       0       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imprima uma Transposta do dataset (transforme linhas e colunas e colunas em linhas)\n",
    "df.head(10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      "sepal length (cm)    150 non-null float64\n",
      "sepal width (cm)     150 non-null float64\n",
      "petal length (cm)    150 non-null float64\n",
      "petal width (cm)     150 non-null float64\n",
      "species              150 non-null category\n",
      "target               150 non-null int64\n",
      "dtypes: category(1), float64(4), int64(1)\n",
      "memory usage: 6.2 KB\n"
     ]
    }
   ],
   "source": [
    "# Utilize a função Info do dataset para obter um resumo sobre o dataset \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</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",
       "      <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",
       "      <td>1.000000</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",
       "      <td>0.819232</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</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",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.054000           3.758667   \n",
       "std             0.828066          0.433594           1.764420   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)      target  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.198667    1.000000  \n",
       "std            0.763161    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Faça um resumo estatístico do dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "species              0\n",
       "target               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifique se existem valores nulos no dataset\n",
    "df.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    10\n",
       "6.3     9\n",
       "5.1     9\n",
       "6.7     8\n",
       "5.7     8\n",
       "5.5     7\n",
       "5.8     7\n",
       "6.4     7\n",
       "6.0     6\n",
       "4.9     6\n",
       "6.1     6\n",
       "5.4     6\n",
       "5.6     6\n",
       "6.5     5\n",
       "4.8     5\n",
       "7.7     4\n",
       "6.9     4\n",
       "5.2     4\n",
       "6.2     4\n",
       "4.6     4\n",
       "7.2     3\n",
       "6.8     3\n",
       "4.4     3\n",
       "5.9     3\n",
       "6.6     2\n",
       "4.7     2\n",
       "7.6     1\n",
       "7.4     1\n",
       "4.3     1\n",
       "7.9     1\n",
       "7.3     1\n",
       "7.0     1\n",
       "4.5     1\n",
       "5.3     1\n",
       "7.1     1\n",
       "Name: sepal length (cm), dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Faça uma contagem de valores de sepal length\n",
    "df['sepal length (cm)'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Histograma')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6ff198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Histograma de sepal length\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "df['sepal length (cm)'].value_counts().hist(bins=range(20))\n",
    "plt.xlabel('Sepal length (cm)', fontsize=fontsize)\n",
    "plt.ylabel('Frequência', fontsize=fontsize)\n",
    "plt.title('Histograma', fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Gráfico de Dispersão dos Atributos, colorido por marcadores da classe alvo')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6ff048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Gráficos de Dispersão (scatter Plot) da variável sepal length versus número da linha, \n",
    "# colorido por marcadores da variável target\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "plt.scatter(range(len(df)), df['petal width (cm)'], c=df['target'])\n",
    "plt.xlabel('Número da Linha', fontsize=fontsize)\n",
    "plt.ylabel('Sepal length (cm)', fontsize=fontsize)\n",
    "plt.title('Gráfico de Dispersão dos Atributos, colorido por marcadores da classe alvo', fontsize=fontsize)\n",
    "#plt.title('Scatter plot of features, colored by target labels', fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'petal width (cm)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e926f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Scatter Plot de 2 Features (atributos)\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "plt.scatter(df['petal length (cm)'], df['petal width (cm)'], c=df['target'])\n",
    "plt.xlabel('petal length (cm)', fontsize=fontsize)\n",
    "plt.ylabel('petal width (cm)', fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10edaea20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f39f630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f3d7550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f4115c0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10f44d5c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f44d5f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f4aff60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f4e9f60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10f51ff60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f506780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f590b00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f5caa90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10f603a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f63afd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f6705c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f6aa5c0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106c46940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Scatter Matrix das Features (atributos)\n",
    "attributes = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
    "pd.plotting.scatter_matrix(df[attributes], figsize=(16, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a15d1b9b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a15ded588>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a160ae470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a160e8470>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a16122470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a161224a8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15d4b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Histograma de todas as features\n",
    "df.hist(figsize=(12,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conheça a Formação Cientista de Dados, um programa completo, 100% online e 100% em português, com 340 horas, mais de 1.200 aulas em vídeos e 26 projetos, que vão ajudá-lo a se tornar um dos profissionais mais cobiçados do mercado de análise de dados. Clique no link abaixo, faça sua inscrição, comece hoje mesmo e aumente sua empregabilidade:\n",
    "\n",
    "https://www.datascienceacademy.com.br/pages/formacao-cientista-de-dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Fim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obrigado - Data Science Academy - <a href=\"http://facebook.com/dsacademybr\">facebook.com/dsacademybr</a>"
   ]
  }
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