fruit_data_with_colors_analise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução à Análise de Dados com Python\n",
    "\n",
    "**Objetivos**\n",
    "\n",
    "* Demonstração da leitura de arquivos em modo texto\n",
    "* Demonstração da tabela e informações básicas\n",
    "\n",
    "**Características do Data Set**\n",
    "\n",
    "* Linhas: 59\n",
    "* Colunas: 7\n",
    "* Formato do arquivo: txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importando a biblioteca pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ler tabela com o separador de tabulações usando a biblioteca pandas\n",
    "frutas = pd.read_table('fruit_data_with_colors.txt',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>80</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>80</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>76</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>178</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>166</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>154</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>164</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>152</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>156</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>156</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>168</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>162</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>162</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>160</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>156</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>140</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>170</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>342</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>356</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>362</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>204</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>140</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>160</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>158</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>210</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>selected_seconds</td>\n",
       "      <td>164</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>190</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>142</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>150</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>160</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>154</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>158</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>154</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>180</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>154</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>194</td>\n",
       "      <td>7.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>200</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>186</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>216</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>196</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>spanish_belsan</td>\n",
       "      <td>174</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>132</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>130</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>118</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>120</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>152</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>118</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit_label fruit_name     fruit_subtype  mass  width  height  color_score\n",
       "0             1      apple      granny_smith   192    8.4     7.3         0.55\n",
       "1             1      apple      granny_smith   180    8.0     6.8         0.59\n",
       "2             1      apple      granny_smith   176    7.4     7.2         0.60\n",
       "3             2   mandarin          mandarin    86    6.2     4.7         0.80\n",
       "4             2   mandarin          mandarin    84    6.0     4.6         0.79\n",
       "5             2   mandarin          mandarin    80    5.8     4.3         0.77\n",
       "6             2   mandarin          mandarin    80    5.9     4.3         0.81\n",
       "7             2   mandarin          mandarin    76    5.8     4.0         0.81\n",
       "8             1      apple          braeburn   178    7.1     7.8         0.92\n",
       "9             1      apple          braeburn   172    7.4     7.0         0.89\n",
       "10            1      apple          braeburn   166    6.9     7.3         0.93\n",
       "11            1      apple          braeburn   172    7.1     7.6         0.92\n",
       "12            1      apple          braeburn   154    7.0     7.1         0.88\n",
       "13            1      apple  golden_delicious   164    7.3     7.7         0.70\n",
       "14            1      apple  golden_delicious   152    7.6     7.3         0.69\n",
       "15            1      apple  golden_delicious   156    7.7     7.1         0.69\n",
       "16            1      apple  golden_delicious   156    7.6     7.5         0.67\n",
       "17            1      apple  golden_delicious   168    7.5     7.6         0.73\n",
       "18            1      apple       cripps_pink   162    7.5     7.1         0.83\n",
       "19            1      apple       cripps_pink   162    7.4     7.2         0.85\n",
       "20            1      apple       cripps_pink   160    7.5     7.5         0.86\n",
       "21            1      apple       cripps_pink   156    7.4     7.4         0.84\n",
       "22            1      apple       cripps_pink   140    7.3     7.1         0.87\n",
       "23            1      apple       cripps_pink   170    7.6     7.9         0.88\n",
       "24            3     orange     spanish_jumbo   342    9.0     9.4         0.75\n",
       "25            3     orange     spanish_jumbo   356    9.2     9.2         0.75\n",
       "26            3     orange     spanish_jumbo   362    9.6     9.2         0.74\n",
       "27            3     orange  selected_seconds   204    7.5     9.2         0.77\n",
       "28            3     orange  selected_seconds   140    6.7     7.1         0.72\n",
       "29            3     orange  selected_seconds   160    7.0     7.4         0.81\n",
       "30            3     orange  selected_seconds   158    7.1     7.5         0.79\n",
       "31            3     orange  selected_seconds   210    7.8     8.0         0.82\n",
       "32            3     orange  selected_seconds   164    7.2     7.0         0.80\n",
       "33            3     orange      turkey_navel   190    7.5     8.1         0.74\n",
       "34            3     orange      turkey_navel   142    7.6     7.8         0.75\n",
       "35            3     orange      turkey_navel   150    7.1     7.9         0.75\n",
       "36            3     orange      turkey_navel   160    7.1     7.6         0.76\n",
       "37            3     orange      turkey_navel   154    7.3     7.3         0.79\n",
       "38            3     orange      turkey_navel   158    7.2     7.8         0.77\n",
       "39            3     orange      turkey_navel   144    6.8     7.4         0.75\n",
       "40            3     orange      turkey_navel   154    7.1     7.5         0.78\n",
       "41            3     orange      turkey_navel   180    7.6     8.2         0.79\n",
       "42            3     orange      turkey_navel   154    7.2     7.2         0.82\n",
       "43            4      lemon    spanish_belsan   194    7.2    10.3         0.70\n",
       "44            4      lemon    spanish_belsan   200    7.3    10.5         0.72\n",
       "45            4      lemon    spanish_belsan   186    7.2     9.2         0.72\n",
       "46            4      lemon    spanish_belsan   216    7.3    10.2         0.71\n",
       "47            4      lemon    spanish_belsan   196    7.3     9.7         0.72\n",
       "48            4      lemon    spanish_belsan   174    7.3    10.1         0.72\n",
       "49            4      lemon           unknown   132    5.8     8.7         0.73\n",
       "50            4      lemon           unknown   130    6.0     8.2         0.71\n",
       "51            4      lemon           unknown   116    6.0     7.5         0.72\n",
       "52            4      lemon           unknown   118    5.9     8.0         0.72\n",
       "53            4      lemon           unknown   120    6.0     8.4         0.74\n",
       "54            4      lemon           unknown   116    6.1     8.5         0.71\n",
       "55            4      lemon           unknown   116    6.3     7.7         0.72\n",
       "56            4      lemon           unknown   116    5.9     8.1         0.73\n",
       "57            4      lemon           unknown   152    6.5     8.5         0.72\n",
       "58            4      lemon           unknown   118    6.1     8.1         0.70"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrando toda a tabela\n",
    "frutas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exibe (número de linhas, número de colunas)\n",
    "frutas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3            2   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4            2   mandarin      mandarin    84    6.0     4.6         0.79"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exibe as primeiras 5 linhas \n",
    "frutas.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Estatística de um Conjunto de Dados\n",
    "\n",
    "**Objetivos**\n",
    "\n",
    "* Demonstração das estatísticas do conjunto de dados\n",
    "* Demonstração da manipulação de linhas e colunas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>fruit_label</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.542373</td>\n",
       "      <td>163.118644</td>\n",
       "      <td>7.105085</td>\n",
       "      <td>7.693220</td>\n",
       "      <td>0.762881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.208048</td>\n",
       "      <td>55.018832</td>\n",
       "      <td>0.816938</td>\n",
       "      <td>1.361017</td>\n",
       "      <td>0.076857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.930000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fruit_label        mass      width     height  color_score\n",
       "count    59.000000   59.000000  59.000000  59.000000    59.000000\n",
       "mean      2.542373  163.118644   7.105085   7.693220     0.762881\n",
       "std       1.208048   55.018832   0.816938   1.361017     0.076857\n",
       "min       1.000000   76.000000   5.800000   4.000000     0.550000\n",
       "25%       1.000000  140.000000   6.600000   7.200000     0.720000\n",
       "50%       3.000000  158.000000   7.200000   7.600000     0.750000\n",
       "75%       4.000000  177.000000   7.500000   8.200000     0.810000\n",
       "max       4.000000  362.000000   9.600000  10.500000     0.930000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabela do subconjunto das colunas\n",
    "# Cada linha representa algo estatístico do conjunto de dados\n",
    "# Somente valores numéricos serão apresentados\n",
    "\n",
    "# count: número de elementos do conjunto de dados\n",
    "# mean: média dos valores encontrados no conjunto de dados para cada uma das colunas\n",
    "# std: desvio padrão (standard deviation), em média quanto os dados se afastam da média\n",
    "# min: o menor valor encontrado em cada coluna (limite inferior)\n",
    "# 25%: quartil em que 25% dos dados possuem valores menores que o valor representado\n",
    "# 50%: quartil em que 50% dos dados possuem valores menores que o valor representado (mediana)\n",
    "# 75%: quartil em que 75% dos dados possuem valores menores que o valor representado\n",
    "# max: o maior valor encontrado em cada coluna (limite superior) \n",
    "\n",
    "frutas.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     59.000000\n",
       "mean     163.118644\n",
       "std       55.018832\n",
       "min       76.000000\n",
       "25%      140.000000\n",
       "50%      158.000000\n",
       "75%      177.000000\n",
       "max      362.000000\n",
       "Name: mass, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pegando as estatísticas apenas da coluna da massa\n",
    "frutas.describe()['mass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pegando apenas o valor mínimo da massa\n",
    "frutas.describe()['mass']['min']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     192\n",
       "1     180\n",
       "2     176\n",
       "3      86\n",
       "4      84\n",
       "5      80\n",
       "6      80\n",
       "7      76\n",
       "8     178\n",
       "9     172\n",
       "10    166\n",
       "11    172\n",
       "12    154\n",
       "13    164\n",
       "14    152\n",
       "15    156\n",
       "16    156\n",
       "17    168\n",
       "18    162\n",
       "19    162\n",
       "20    160\n",
       "21    156\n",
       "22    140\n",
       "23    170\n",
       "24    342\n",
       "25    356\n",
       "26    362\n",
       "27    204\n",
       "28    140\n",
       "29    160\n",
       "30    158\n",
       "31    210\n",
       "32    164\n",
       "33    190\n",
       "34    142\n",
       "35    150\n",
       "36    160\n",
       "37    154\n",
       "38    158\n",
       "39    144\n",
       "40    154\n",
       "41    180\n",
       "42    154\n",
       "43    194\n",
       "44    200\n",
       "45    186\n",
       "46    216\n",
       "47    196\n",
       "48    174\n",
       "49    132\n",
       "50    130\n",
       "51    116\n",
       "52    118\n",
       "53    120\n",
       "54    116\n",
       "55    116\n",
       "56    116\n",
       "57    152\n",
       "58    118\n",
       "Name: mass, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pegando apenas a coluna da massa\n",
    "frutas['mass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>mass</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>80</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>76</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>178</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>172</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>166</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>172</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>154</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>164</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>152</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>156</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>156</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>168</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>162</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>162</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>160</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>156</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>140</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>170</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>342</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>356</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>362</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>204</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>140</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>160</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>158</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>210</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>164</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>190</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>142</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>150</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>160</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>154</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>158</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>144</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>154</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>180</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>154</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>194</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>200</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>186</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>216</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>196</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>174</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>132</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>130</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>116</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>118</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>120</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>116</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>116</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>116</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>152</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>118</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mass  color_score\n",
       "0    192         0.55\n",
       "1    180         0.59\n",
       "2    176         0.60\n",
       "3     86         0.80\n",
       "4     84         0.79\n",
       "5     80         0.77\n",
       "6     80         0.81\n",
       "7     76         0.81\n",
       "8    178         0.92\n",
       "9    172         0.89\n",
       "10   166         0.93\n",
       "11   172         0.92\n",
       "12   154         0.88\n",
       "13   164         0.70\n",
       "14   152         0.69\n",
       "15   156         0.69\n",
       "16   156         0.67\n",
       "17   168         0.73\n",
       "18   162         0.83\n",
       "19   162         0.85\n",
       "20   160         0.86\n",
       "21   156         0.84\n",
       "22   140         0.87\n",
       "23   170         0.88\n",
       "24   342         0.75\n",
       "25   356         0.75\n",
       "26   362         0.74\n",
       "27   204         0.77\n",
       "28   140         0.72\n",
       "29   160         0.81\n",
       "30   158         0.79\n",
       "31   210         0.82\n",
       "32   164         0.80\n",
       "33   190         0.74\n",
       "34   142         0.75\n",
       "35   150         0.75\n",
       "36   160         0.76\n",
       "37   154         0.79\n",
       "38   158         0.77\n",
       "39   144         0.75\n",
       "40   154         0.78\n",
       "41   180         0.79\n",
       "42   154         0.82\n",
       "43   194         0.70\n",
       "44   200         0.72\n",
       "45   186         0.72\n",
       "46   216         0.71\n",
       "47   196         0.72\n",
       "48   174         0.72\n",
       "49   132         0.73\n",
       "50   130         0.71\n",
       "51   116         0.72\n",
       "52   118         0.72\n",
       "53   120         0.74\n",
       "54   116         0.71\n",
       "55   116         0.72\n",
       "56   116         0.73\n",
       "57   152         0.72\n",
       "58   118         0.70"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Podemos pegar mais de uma coluna\n",
    "frutas[['mass','color_score']]"
   ]
  },
  {
   "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>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>166</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>154</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>164</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>152</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit_label fruit_name     fruit_subtype  mass  width  height  color_score\n",
       "10            1      apple          braeburn   166    6.9     7.3         0.93\n",
       "11            1      apple          braeburn   172    7.1     7.6         0.92\n",
       "12            1      apple          braeburn   154    7.0     7.1         0.88\n",
       "13            1      apple  golden_delicious   164    7.3     7.7         0.70\n",
       "14            1      apple  golden_delicious   152    7.6     7.3         0.69"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pegando um conjunto de linhas específicas\n",
    "frutas[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>166</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>154</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>164</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>152</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit_label fruit_name     fruit_subtype  mass  width  height  color_score\n",
       "10            1      apple          braeburn   166    6.9     7.3         0.93\n",
       "11            1      apple          braeburn   172    7.1     7.6         0.92\n",
       "12            1      apple          braeburn   154    7.0     7.1         0.88\n",
       "13            1      apple  golden_delicious   164    7.3     7.7         0.70\n",
       "14            1      apple  golden_delicious   152    7.6     7.3         0.69"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outra maneira de exibição de linhas\n",
    "i = 15\n",
    "frutas[i-5:i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>mass</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>156</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>156</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>168</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>162</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>162</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mass  color_score\n",
       "15   156         0.69\n",
       "16   156         0.67\n",
       "17   168         0.73\n",
       "18   162         0.83\n",
       "19   162         0.85"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pegando um conjunto de colunas e linhas específicas\n",
    "frutas[['mass','color_score']][i:i+5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise e Visualização de um Conjunto de Dados\n",
    "\n",
    "**Objetivos**\n",
    "\n",
    "* Demonstração do balanceamento dos dados\n",
    "* Demonstração da visualização de gráficos\n",
    "* Finalização da demonstração da funcionalidade básica de um notebook Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o módulo pyplot da biblioteca matplotlib\n",
    "# Pois primeiramente é preciso verificar o balanceamento dos dados\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple       19\n",
       "orange      19\n",
       "lemon       16\n",
       "mandarin     5\n",
       "Name: fruit_name, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leitura e contagem dos dados de acordo com seus nomes que estão na coluna 'nome da fruta'\n",
    "frequencia = frutas['fruit_name'].value_counts()\n",
    "frequencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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"
    }
   ],
   "source": [
    "# Gerar um gráfico que pode facilitar a análise deste conjunto de dados, no caso um gráfico de barras\n",
    "frequencia.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8      True\n",
       "9      True\n",
       "10     True\n",
       "11     True\n",
       "12     True\n",
       "13     True\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "20     True\n",
       "21     True\n",
       "22     True\n",
       "23     True\n",
       "24    False\n",
       "25    False\n",
       "26    False\n",
       "27    False\n",
       "28    False\n",
       "29    False\n",
       "30    False\n",
       "31    False\n",
       "32    False\n",
       "33    False\n",
       "34    False\n",
       "35    False\n",
       "36    False\n",
       "37    False\n",
       "38    False\n",
       "39    False\n",
       "40    False\n",
       "41    False\n",
       "42    False\n",
       "43    False\n",
       "44    False\n",
       "45    False\n",
       "46    False\n",
       "47    False\n",
       "48    False\n",
       "49    False\n",
       "50    False\n",
       "51    False\n",
       "52    False\n",
       "53    False\n",
       "54    False\n",
       "55    False\n",
       "56    False\n",
       "57    False\n",
       "58    False\n",
       "Name: fruit_name, dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifica quais frutas são maçãs \n",
    "frutas['fruit_name'] == 'apple'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>178</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>166</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>154</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>164</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>152</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>156</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>156</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>168</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>162</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>162</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>160</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>156</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>140</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>170</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit_label fruit_name     fruit_subtype  mass  width  height  color_score\n",
       "0             1      apple      granny_smith   192    8.4     7.3         0.55\n",
       "1             1      apple      granny_smith   180    8.0     6.8         0.59\n",
       "2             1      apple      granny_smith   176    7.4     7.2         0.60\n",
       "8             1      apple          braeburn   178    7.1     7.8         0.92\n",
       "9             1      apple          braeburn   172    7.4     7.0         0.89\n",
       "10            1      apple          braeburn   166    6.9     7.3         0.93\n",
       "11            1      apple          braeburn   172    7.1     7.6         0.92\n",
       "12            1      apple          braeburn   154    7.0     7.1         0.88\n",
       "13            1      apple  golden_delicious   164    7.3     7.7         0.70\n",
       "14            1      apple  golden_delicious   152    7.6     7.3         0.69\n",
       "15            1      apple  golden_delicious   156    7.7     7.1         0.69\n",
       "16            1      apple  golden_delicious   156    7.6     7.5         0.67\n",
       "17            1      apple  golden_delicious   168    7.5     7.6         0.73\n",
       "18            1      apple       cripps_pink   162    7.5     7.1         0.83\n",
       "19            1      apple       cripps_pink   162    7.4     7.2         0.85\n",
       "20            1      apple       cripps_pink   160    7.5     7.5         0.86\n",
       "21            1      apple       cripps_pink   156    7.4     7.4         0.84\n",
       "22            1      apple       cripps_pink   140    7.3     7.1         0.87\n",
       "23            1      apple       cripps_pink   170    7.6     7.9         0.88"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Armazena quais linhas são maçãs em uma variável\n",
    "macas = frutas['fruit_name'] == 'apple'\n",
    "\n",
    "frutas[macas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8      True\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18    False\n",
       "19    False\n",
       "20    False\n",
       "21    False\n",
       "22    False\n",
       "23    False\n",
       "24     True\n",
       "25     True\n",
       "26     True\n",
       "27     True\n",
       "28    False\n",
       "29    False\n",
       "30    False\n",
       "31     True\n",
       "32    False\n",
       "33     True\n",
       "34    False\n",
       "35    False\n",
       "36    False\n",
       "37    False\n",
       "38    False\n",
       "39    False\n",
       "40    False\n",
       "41     True\n",
       "42    False\n",
       "43     True\n",
       "44     True\n",
       "45     True\n",
       "46     True\n",
       "47     True\n",
       "48    False\n",
       "49    False\n",
       "50    False\n",
       "51    False\n",
       "52    False\n",
       "53    False\n",
       "54    False\n",
       "55    False\n",
       "56    False\n",
       "57    False\n",
       "58    False\n",
       "Name: mass, dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifica quais frutas possuem massa maior que 175\n",
    "frutas['mass'] > 175"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>178</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "8            1      apple      braeburn   178    7.1     7.8         0.92"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifica quais frutas são maçãs e possuem massa maior que 175\n",
    "pesadas = frutas['mass'] > 175\n",
    "frutas[macas & pesadas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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"
    }
   ],
   "source": [
    "# Aplicando índices para plotar gráfico pegando apenas comprimento e altura\n",
    "X1 = frutas[macas & pesadas]['width']\n",
    "X2 = frutas[macas & pesadas]['height']\n",
    "# Gráfico de dispersão\n",
    "plt.scatter(X1,X2)\n",
    "plt.xlabel('comprimento')\n",
    "plt.ylabel('altura')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYCklEQVR4nO3df5CkdX3g8feHZYmzJDICS05G1xXP2ijshYU5D8KFixCPaKy4onXBk6gkuhfPU2JVuEAdXq6uvCIWpi7JXVXMRo0YFY2AG7WiwJkS6/TculkWXRAog4YfsyjL7Q4qjLIsn/ujn1lm257p3ul5+tvdz/tVNbUzTz/dz2ee7v3ss5/+fD8dmYkkqTmOKR2AJGmwTPyS1DAmfklqGBO/JDWMiV+SGubY0gH04uSTT86NGzeWDkOSRsquXbsezcz17dtHIvFv3LiRmZmZ0mFI0kiJiPs7bbfUI0kNY+KXpIYx8UtSw5j4JalhTPyS1DAj0dUjtduxe5Zrb76XvXPznDo5wRUXbWLrlqnSYUkjwcSvkbNj9yxX3bSH+YOHAJidm+eqm/YAmPylHljq0ci59uZ7Dyf9BfMHD3HtzfcWikgaLSZ+jZy9c/NHtV3SkUz8GjmnTk4c1XZJRzLxa+RccdEmJtauOWLbxNo1XHHRpkIRSaPFN3c1lJbr2ln4064eaWVM/Bo6vXTtbN0yZaKXVshSj4aOXTtSvbzi19DppWvHBVzSynnFr6HTrWtnoRQ0OzdP8kwpaMfu2QFGKY0uE7+GTreuHUtBUn8s9WjodOvacQGX1B8Tv4bScl07J0ysZW7+YMftkrqz1KORE3F02yUdqbbEHxEfjohHIuLORdtOjIhbI+Lb1Z/Pqev4Gl9zT/z01f5y2yUdqc4r/o8Av9a27UrgS5n5YuBL1c/SUXFWj9Sf2hJ/Zn4F2N+2+TXAddX31wFb6zq+xpezeqT+DPrN3Z/PzIcBMvPhiDhlqR0jYhuwDWDDhg0DCk+jwFk9Un8iM+t78IiNwOcz84zq57nMnFx0+4HM7Frnn56ezpmZmdrilKRxFBG7MnO6ffugu3q+HxHPrQJ6LvDIgI8vSY036MT/WeDN1fdvBv52wMeXpMars53zeuD/AJsi4qGI+B3gj4BXRMS3gVdUP0uSBqi2N3cz8w1L3HRhXceUJHXnyl1Jahhn9WgkOY9fWjkTv0ZOLx/NKGlplno0cpzHL/XHxK+R4zx+qT8mfo0ch7RJ/THxa+Q4pE3qj2/uauQ4pE3qj4lfI2m5j2aUtDxLPZLUMF7xSxo7LvBbnolf0lhxgV93lnokjRUX+HVn4pc0Vlzg152JX9JYcYFfdyZ+SWPFBX7d+eauhlLdXRl2fYyv1VjgNwyvjzpjiMxclQeq0/T0dM7MzJQOQwPS3pUBrSu2ay7evCov/LofX6NtGF4fqxVDROzKzOn27ZZ6NHTq7sqw60PLGYbXR90xmPg1dOruyrDrQ8sZhtdH3TGY+DV06u7KsOtDyxmG10fdMZj4NXTq7sqw60PLGYbXR90xFOnqiYjLgbcBAfxlZv5JiTg0nOoeu+xYZy1nGF4fdccw8K6eiDgD+CTwMuBJ4IvA2zPz20vdx64eSTp6w9TV8xLg65n5RGY+BdwGvLZAHJLUSCUS/53A+RFxUkSsA14FPL99p4jYFhEzETGzb9++gQcpSeNq4Ik/M+8G3gfcSqvM8w3gqQ77bc/M6cycXr9+/YCjlKTxVaSrJzM/lJlnZeb5wH5gyfq+JGl1lerqOSUzH4mIDcDFwLkl4pCkJio1pO3GiDgJOAi8IzMPFIpDkhqnSOLPzF8ucVxJ0hiPZR6GsaqSNIzGMvH7YcuStLSxnNUzDGNVJWlYjWXiH4axqpI0rMYy8Q/DWFVJGlZjmfiHYayqJA2rsXxzdxBjVUt3DZU+vqTRNZaJH1rJv65EWLprqPTxJY22sSz11K1011Dp40sabWN7xd+v5UoppbuGSh9f0mjzir+DhVLK7Nw8yTOllB27Z4HyXUOljy9ptJn4O+hWSindNVT6+JJGm6WeDrqVUkp/GHPp40sabSb+Dk6dnGC2Q/JfXEqps2uoF6WPL2l0WerpwFKKpHHmFX8HW7dMMXP/fq7f+SCHMlkTwevO9gq7SVwgp3HmFX8HO3bPcuOuWQ5lAnAokxt3zR7u6tF469bVJY06E38HLpBqNp9/jbvGlnr6XaBlKWC0DfMCPalujUz83WbddOvqcVbOaOv3+ZdGXSNLPf0u0LIUMNqGfYGeVLciV/wR8W7grUACe4DLMvPHgzp+vwu0LAWMtkEs0LMUqGE28MQfEVPAu4CXZuZ8RPwNcAnwkUHFMLluLQeeONhx+4LlFkhZChhtdS/QsxSoYVeq1HMsMBERxwLrgL2DPHjVpdnz9naWAkZb3c+fpUANu4Ff8WfmbES8H3gAmAduycxb2veLiG3ANoANGzasagyPzf/01f5y29s5K6d/JUshvTx//cRnKVDDrkSp5znAa4AXAnPApyPi0sz82OL9MnM7sB1genq6x2vx3qxGqcZZOSs3DKWQ5Z6/fuOzFKhhV6LU86vAdzNzX2YeBG4CfmmQAViqKWvYSyH9xufrS8OuRFfPA8A5EbGOVqnnQmBmkAFYqilr2BfI9Vuq8fWlYVeixr8zIm4AbgeeAnZTlXQGyVJNOd26qkqXgk6YWMtch/d7TphY22Hvznx9aZgV6erJzD/MzF/IzDMy87cy8ycl4lAZ3bqqSpeCIo5uuzRqGjmyYRg0eYFPt66q0qWguQ7/G1luuzRqGjmyobSmj/3t9mHx3W6v+/z5YfYadyb+AkqXMkrr1vVSelaSXTkad5Z6Cmj6Ap+tW6b49MwDfPW+/Ye3nbXhhMOlmkHMSlquVGRXjsadib+Api/wuXrHniOSPsBX79vP1Tv28N6tm4F6ZyX10jVkV47GmaWeAppeSrh+54NHtb1dv+ev6aU2qacr/oh4FvA7wOnAsxa2Z+Zv1xRXcXV2jTS9lHBoiX7Opba36/f8le4akkrrtdTz18A9wEXAfwXeCNxdV1ClDWIBkaWE/vRz/rot0Cq9gEyqW6+lnn+ame8BHs/M64BfBzbXF1ZZlgLqtdQ6qEGtj+q2QMvnX+Ou18S/cHk0FxFnACcAG2uJaAg0veumbksVdFZ1BOsyui3Q8vnXuOu11LO9Gqd8NfBZ4GeB99QWVWFN77qB/mvcy91/TUTHev6aVZyJsNzxuz2/Pv8ad12v+CPiGOAHmXkgM7+Smadl5imZ+RcDiK+Ipnfd9Lsyttv93/Avnt/xfkttX+34+11AJo26rok/M58G/sMAYhkaW7dMcc3Fm5manCCAqckJrrl4c2Pe2Ou3xt3t/u/duplLz9lw+Ap/TQSXnrPhcA9/v7odv9vz2/TnX+Ov11LPrRHx+8CngMcXNmbm/qXvMtqa3HXTb7tjL/d/79bNfSX6fo/f5Oe3F7azjrdeE/9Cv/47Fm1L4LTVDUfDoFuNu1u7Y9018rqP3/R2zqb//k3QU1dPZr6ww5dJf0z1OySt7hp53cdvejtn03//Juh15e6bOm3PzI+ubjgaBv0OSVuNlcn9lHK6DYHrpuntnE3//Zug11LPP1/0/bNofU7u7YCJf0z1OyStnxp6v6WcXobALafp7ZxN//2boNdSzzsXfb0N2AIcV29oGlbDXsopPQRu1DX992+ClY5lfgJ48WoGosHqp2ujl1JOP4/fSyln5v79XL/zQQ5lsiaC1539zP8wehkC1+88/nHuemn6EMEm6LXG/zmeWVF/DPBS4NN1BaV6rUbXxnKlnH4fv5euoht3zR5O5IcyuXHXLNMvOJGtW6a6rgzudx5/E7pebHcdb73O6nk/8MfV1zXA+Zn5B7VFpVrV3bXR7+P321XUbWVw3QvUpGHXa+J/VWbeVn19NTMfioj3reSAEbEpIu5Y9PWDiPi9lTyWVqburo1+H3/rliled/bUESt7F5dyuj3+e7du5rwXnXjEbee96MTDb+z2G59dLxp1vSb+V3TY9sqVHDAz783MMzPzTOBsWu8XfGYlj6WVWao7Y7W6Nvp9/KVKOQuzdro9/o7ds9z+wGNH3Hb7A4/1fP9u6j5/Ut2WTfwR8faI2ANsiohvLvr6LvDNVTj+hcB9mXn/KjyWelR310bdC6hKLzCz60Wjrtubu58AvkCrrn/lou0/XKU5PZcA13e6ISK2AdsANmzYsAqH0oK6uzbq/mjE0gvM7HrRqItc5nNOI+LEJW+kvyFtEXEcsBc4PTO/v9y+09PTOTMzs9JDacSc90d/37GrZ2pygq9eeUHt95fGRUTsyszp9u3davy7gJnqq9P3/XglcHu3pK/msRQj1WvZUk9mvnDh++rq/8W0RjashjewRJlHzWYpZrwXiKm8ZUs9h3eKeCtwOfA84A7gHOBrmXnhig4asQ54EDgtMx/rtr+lHjVJ+wIxaP2PxQ+D0dFaaalnweW0BrXdn5kvpzWr59GVBpOZT2TmSb0kfalpXCCmuvWa+H+cmT8GiIifycx7AAumUg1cIKa69Tqk7aGImAR20PoYxgO0OnIkrTLHIqtuvY5lfm1mzmXmfwHeA3wI2FpjXFJj2ZWkuh31WObMvK2OQCS1jENXkobbSufxS6qRY5FVp17f3JUkjQkTvyQ1jIlfkhrGxC9JDWPil6SGMfFLUsOY+CWpYUz8ktQwJn5JahgTvyQ1jIlfkhrGxC9JDWPil6SGMfFLUsOY+CWpYUz8ktQwRRJ/RExGxA0RcU9E3B0R55aIQ5KaqNQncP0p8MXMfH1EHAesKxSHJDXOwBN/RDwbOB94C0BmPgk8Oeg4JKmpSpR6TgP2AX8VEbsj4oMRcXz7ThGxLSJmImJm3759g49SksZUicR/LHAW8OeZuQV4HLiyfafM3J6Z05k5vX79+kHHKEljq0Tifwh4KDN3Vj/fQOsfAknSAAw88Wfm94AHI2JTtelC4FuDjkOSmqpUV887gY9XHT3fAS4rFIckNU6RxJ+ZdwDTJY4tSU3nyl1JahgTvyQ1jIlfkhrGxC9JDWPil6SGMfFLUsOY+CWpYUz8ktQwJn5JahgTvyQ1jIlfkhrGxC9JDWPil6SGMfFLUsOY+CWpYUz8ktQwJn5JahgTvyQ1jIlfkhrGxC9JDWPil6SGObbEQSPiH4EfAoeApzJzukQcktRERRJ/5eWZ+WjB40tSI1nqkaSGKZX4E7glInZFxLZCMUhSI5Uq9ZyXmXsj4hTg1oi4JzO/sniH6h+EbQAbNmwoEaMkjaUiV/yZubf68xHgM8DLOuyzPTOnM3N6/fr1gw5RksbWwBN/RBwfET+38D3wr4E7Bx2HJDVViVLPzwOfiYiF438iM79YIA5JaqSBJ/7M/A7wi4M+riSpxXZOSWqYkgu4xtqO3bNce/O97J2b59TJCa64aBNbt0yVDkuSTPx12LF7lqtu2sP8wUMAzM7Nc9VNewBM/pKKs9RTg2tvvvdw0l8wf/AQ1958b6GIJOkZXvEvoZ9Szd65+aPaLkmD5BV/Bwulmtm5eZJnSjU7ds/2dP9TJyeOarskDZKJv4N+SzVXXLSJibVrjtg2sXYNV1y0adVilKSVstTTQb+lmoWSkF09koaRib+DUycnmO2Q5I+mVLN1y5SJXtJQstTTgaUaSePMK/4OLNVIGmcm/iVYqpE0riz1SFLDjO0Vf7cFWM7SkdRUY5n4u83KcZaOpCYby1JPtwVYztKR1GRjmfi7LcBylo6kJhvLxN9tVo6zdCQ12Vgm/m4LsFygJanJxvLN3W4LsFygJanJIjNLx9DV9PR0zszMlA5DkkZKROzKzOn27WNZ6pEkLa1YqSci1gAzwGxmvnq1H98FWpLUWcka/+XA3cCzV/uBXaAlSUsrUuqJiOcBvw58sI7Hd4GWJC2tVI3/T4D/CDy91A4RsS0iZiJiZt++fUf14C7QkqSlDTzxR8SrgUcyc9dy+2Xm9syczszp9evXH9UxXKAlSUsrccV/HvAbEfGPwCeBCyLiY6t5ABdoSdLSBp74M/OqzHxeZm4ELgH+PjMvXc1jbN0yxTUXb2ZqcoIApiYnuObizb6xK0mM6cpd8BO0JGkpRRN/Zn4Z+HLJGCSpaVy5K0kNY+KXpIYx8UtSw5j4JalhRmIsc0TsA+4vdPiTgUcLHbsXxtcf4+uP8fWvzhhfkJk/tQJ2JBJ/SREx02me9bAwvv4YX3+Mr38lYrTUI0kNY+KXpIYx8Xe3vXQAXRhff4yvP8bXv4HHaI1fkhrGK35JahgTvyQ1jIm/EhGTEXFDRNwTEXdHxLltt0dE/FlE/ENEfDMizhqy+H4lIh6LiDuqr/88wNg2LTruHRHxg4j4vbZ9ip2/HuMrdv6q4787Iu6KiDsj4vqIeFbb7aVff93iK33+Lq9iu6v9ua1uL33+usU32POXmX613ue4Dnhr9f1xwGTb7a8CvgAEcA6wc8ji+xXg80NwHtcA36O1cGRozl8P8RU7f8AU8F1govr5b4C3DMv56zG+kufvDOBOYB2ticP/C3jxEJ2/XuIb6Pnzih+IiGcD5wMfAsjMJzNzrm231wAfzZavA5MR8dwhim9YXAjcl5ntK62Lnb82S8VX2rHAREQcSytB7G27vfT56xZfSS8Bvp6ZT2TmU8BtwGvb9il5/nqJb6BM/C2nAfuAv4qI3RHxwYg4vm2fKeDBRT8/VG0blvgAzo2Ib0TEFyLi9AHF1u4S4PoO20uev8WWig8Knb/MnAXeDzwAPAw8lpm3tO1W7Pz1GB+Ue/3dCZwfESdFxDpaV/fPb9un5Ouvl/hggOfPxN9yLHAW8OeZuQV4HLiybZ/ocL9B9cL2Et/ttMoXvwj8D2DHgGI7LCKOA34D+HSnmztsG2gvcZf4ip2/iHgOrSvSFwKnAsdHRPvHkRY7fz3GV+z8ZebdwPuAW4EvAt8Anmrbrdj56zG+gZ4/E3/LQ8BDmbmz+vkGWom2fZ/F/0o/j8H9d7drfJn5g8z8UfX93wFrI+LkAcW34JXA7Zn5/Q63lTx/C5aMr/D5+1Xgu5m5LzMPAjcBv9S2T8nz1zW+0q+/zPxQZp6VmecD+4Fvt+1S9PXXLb5Bnz8TP5CZ3wMejIhN1aYLgW+17fZZ4E1Vd8A5tP67+/CwxBcR/yQiovr+ZbSe2/83iPgWeQNLl1GKnb9Floyv8Pl7ADgnItZVMVwI3N22T8nz1zW+0q+/iDil+nMDcDE//TwXff11i2/Q529sP2x9Bd4JfLwqB3wHuCwifhcgMz8A/B2t2tw/AE8Alw1ZfK8H3h4RTwHzwCVZtQsMQlW7fAXw7xZtG5rz10N8xc5fZu6MiBto/Xf/KWA3sH1Yzl+P8RV9/QE3RsRJwEHgHZl5YFjOX4/xDfT8ObJBkhrGUo8kNYyJX5IaxsQvSQ1j4pekhjHxS1LDmPilFYqI342IN9V8jK0R8dI6j6HmsZ1TWoGIOLYauFX3cT5Ca2rjDXUfS81h4tfIq666f5/W7JVvAlcDHwbW0xpud1lmPlAl0XngF4AX0FrE82bgXFpjet9SPd6PgL8AXg4coLWYZl9EfBn4GnAerZWgPwf8KDPfX922Gzi7Ou6bgKuAzcCnMvPq6rEvBd5Fa7T2TuDfZ+ah6ph/Cry6ivE1wIuAzwOPVV+vq475AVoTMu8DfjszD6zayVQjWOrRSKumGP4n4IJqwNXlwP+kNYL3nwEfB/5s0V2eA1wAvBv4HPDfgdOBzRFxZrXP8bRm+pxFa4TuHy66/2Rm/qvM/OMO4TxZzWL5APC3wDtozWJ/SzWZ8SXAbwLnZeaZwCHgjYuO+fXqd/gK8LbM/Bqtf2CuyMwzM/M+4KPAH1S/25622KSemPg16i4AbsjMRwEycz+tK/hPVLf/NfAvF+3/uWop/B7g+5m5JzOfBu4CNlb7PA18qvr+Y233/xRL+2z15x7grsx8ODN/QmvExvNpzbg5G/i/EXFH9fNp1X2epHV1D7BrUSyHRcQJtP7hua3adB2tz2mQjoqzejTqgu7jdRff/pPqz6cXfb/w81J/Hxbf//FljtPtsQO4LjOv6nDfg4tmsxxaJhapb17xa9R9Cfg31QAsIuJEWnX4S6rb3wj876N8zGNoDc0C+LcruP9SvgS8ftGkxhMj4gVd7vNDWnV9MvMx4EBE/HJ122/RKkVJR8WrCo20zLwrIv4bcFtEHKL1Buu7gA9HxBVUb+4e5cM+DpweEbtovan6m6sU67ci4mrglog4hmpSI7Dcx0B+EvjLiHgXrX+M3gx8oJo2+h0GP2VSY8CuHqlNRPwoM3+2dBxSXSz1SFLDeMUvSQ3jFb8kNYyJX5IaxsQvSQ1j4pekhjHxS1LD/H9TJCUZYOGrQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Refazendo com todos os conjuntos de frutas\n",
    "X1 = frutas['width']\n",
    "X2 = frutas['height']\n",
    "plt.scatter(X1,X2)\n",
    "plt.xlabel('comprimento')\n",
    "plt.ylabel('altura')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     1\n",
       "2     1\n",
       "3     2\n",
       "4     2\n",
       "5     2\n",
       "6     2\n",
       "7     2\n",
       "8     1\n",
       "9     1\n",
       "10    1\n",
       "11    1\n",
       "12    1\n",
       "13    1\n",
       "14    1\n",
       "15    1\n",
       "16    1\n",
       "17    1\n",
       "18    1\n",
       "19    1\n",
       "20    1\n",
       "21    1\n",
       "22    1\n",
       "23    1\n",
       "24    3\n",
       "25    3\n",
       "26    3\n",
       "27    3\n",
       "28    3\n",
       "29    3\n",
       "30    3\n",
       "31    3\n",
       "32    3\n",
       "33    3\n",
       "34    3\n",
       "35    3\n",
       "36    3\n",
       "37    3\n",
       "38    3\n",
       "39    3\n",
       "40    3\n",
       "41    3\n",
       "42    3\n",
       "43    4\n",
       "44    4\n",
       "45    4\n",
       "46    4\n",
       "47    4\n",
       "48    4\n",
       "49    4\n",
       "50    4\n",
       "51    4\n",
       "52    4\n",
       "53    4\n",
       "54    4\n",
       "55    4\n",
       "56    4\n",
       "57    4\n",
       "58    4\n",
       "Name: fruit_label, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Armazenando os rótulos das frutas\n",
    "y = frutas['fruit_label']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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"
    }
   ],
   "source": [
    "# c=y garantirá que as cores do gráfico serão de acordo com os rótulos das frutas\n",
    "plt.scatter(X1,X2,c=y)\n",
    "plt.xlabel('comprimento')\n",
    "plt.ylabel('altura')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}