data analysis fastfood.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of fast food restaurants using pandas, matplotlib and seaborn libraries.\n",
    " - Dropping columns\n",
    " - Checking null values\n",
    " - Summarizing data\n",
    " - Cross tabulation\n",
    " - Subsets\n",
    " - Feature Engineering\n",
    " - Function lambda\n",
    " - Histograms\n",
    " - Boxplot\n",
    "\n",
    " \n",
    " raulvalerio@gmail.com  # youtube.com/c/raulvaleriostatistics   En Español\n",
    " \n",
    " inforvstats@gmail.com  # Conctact me here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>restaurant</th>\n",
       "      <th>item</th>\n",
       "      <th>calories</th>\n",
       "      <th>cal_fat</th>\n",
       "      <th>total_fat</th>\n",
       "      <th>sat_fat</th>\n",
       "      <th>trans_fat</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>sodium</th>\n",
       "      <th>total_carb</th>\n",
       "      <th>fiber</th>\n",
       "      <th>sugar</th>\n",
       "      <th>protein</th>\n",
       "      <th>vit_a</th>\n",
       "      <th>vit_c</th>\n",
       "      <th>calcium</th>\n",
       "      <th>salad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Artisan Grilled Chicken Sandwich</td>\n",
       "      <td>380</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95</td>\n",
       "      <td>1110</td>\n",
       "      <td>44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Single Bacon Smokehouse Burger</td>\n",
       "      <td>840</td>\n",
       "      <td>410</td>\n",
       "      <td>45</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>130</td>\n",
       "      <td>1580</td>\n",
       "      <td>62</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Double Bacon Smokehouse Burger</td>\n",
       "      <td>1130</td>\n",
       "      <td>600</td>\n",
       "      <td>67</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>220</td>\n",
       "      <td>1920</td>\n",
       "      <td>63</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Grilled Bacon Smokehouse Chicken Sandwich</td>\n",
       "      <td>750</td>\n",
       "      <td>280</td>\n",
       "      <td>31</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>155</td>\n",
       "      <td>1940</td>\n",
       "      <td>62</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Crispy Bacon Smokehouse Chicken Sandwich</td>\n",
       "      <td>920</td>\n",
       "      <td>410</td>\n",
       "      <td>45</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>120</td>\n",
       "      <td>1980</td>\n",
       "      <td>81</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Big Mac</td>\n",
       "      <td>540</td>\n",
       "      <td>250</td>\n",
       "      <td>28</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80</td>\n",
       "      <td>950</td>\n",
       "      <td>46</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Cheeseburger</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>40</td>\n",
       "      <td>680</td>\n",
       "      <td>33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Classic Chicken Sandwich</td>\n",
       "      <td>510</td>\n",
       "      <td>210</td>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>1040</td>\n",
       "      <td>49</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Double Cheeseburger</td>\n",
       "      <td>430</td>\n",
       "      <td>190</td>\n",
       "      <td>21</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85</td>\n",
       "      <td>1040</td>\n",
       "      <td>35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Double Quarter Pounder® with Cheese</td>\n",
       "      <td>770</td>\n",
       "      <td>400</td>\n",
       "      <td>45</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>175</td>\n",
       "      <td>1290</td>\n",
       "      <td>42</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>51.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Filet-O-Fish®</td>\n",
       "      <td>380</td>\n",
       "      <td>170</td>\n",
       "      <td>18</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>640</td>\n",
       "      <td>38</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Garlic White Cheddar Burger</td>\n",
       "      <td>620</td>\n",
       "      <td>300</td>\n",
       "      <td>34</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>95</td>\n",
       "      <td>790</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Grilled Garlic White Cheddar Chicken Sandwich</td>\n",
       "      <td>530</td>\n",
       "      <td>180</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125</td>\n",
       "      <td>1150</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>42.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Crispy Garlic White Cheddar Chicken Sandwich</td>\n",
       "      <td>700</td>\n",
       "      <td>300</td>\n",
       "      <td>34</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85</td>\n",
       "      <td>1190</td>\n",
       "      <td>67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11</td>\n",
       "      <td>33.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Hamburger</td>\n",
       "      <td>250</td>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>480</td>\n",
       "      <td>31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Lobster Roll</td>\n",
       "      <td>290</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>630</td>\n",
       "      <td>35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Maple Bacon Dijon 1/4 lb Burger</td>\n",
       "      <td>640</td>\n",
       "      <td>330</td>\n",
       "      <td>36</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>110</td>\n",
       "      <td>1260</td>\n",
       "      <td>40</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Grilled Maple Bacon Dijon Chicken Sandwich</td>\n",
       "      <td>580</td>\n",
       "      <td>190</td>\n",
       "      <td>21</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>1890</td>\n",
       "      <td>50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Crispy Maple Bacon Dijon Chicken Sandwich</td>\n",
       "      <td>740</td>\n",
       "      <td>310</td>\n",
       "      <td>35</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1780</td>\n",
       "      <td>69</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>McChicken</td>\n",
       "      <td>350</td>\n",
       "      <td>130</td>\n",
       "      <td>15</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>600</td>\n",
       "      <td>40</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 restaurant                                           item  \\\n",
       "0            1  Mcdonalds               Artisan Grilled Chicken Sandwich   \n",
       "1            2  Mcdonalds                 Single Bacon Smokehouse Burger   \n",
       "2            3  Mcdonalds                 Double Bacon Smokehouse Burger   \n",
       "3            4  Mcdonalds      Grilled Bacon Smokehouse Chicken Sandwich   \n",
       "4            5  Mcdonalds       Crispy Bacon Smokehouse Chicken Sandwich   \n",
       "5            6  Mcdonalds                                        Big Mac   \n",
       "6            7  Mcdonalds                                   Cheeseburger   \n",
       "7            8  Mcdonalds                       Classic Chicken Sandwich   \n",
       "8            9  Mcdonalds                            Double Cheeseburger   \n",
       "9           10  Mcdonalds            Double Quarter Pounder® with Cheese   \n",
       "10          11  Mcdonalds                                  Filet-O-Fish®   \n",
       "11          12  Mcdonalds                    Garlic White Cheddar Burger   \n",
       "12          13  Mcdonalds  Grilled Garlic White Cheddar Chicken Sandwich   \n",
       "13          14  Mcdonalds   Crispy Garlic White Cheddar Chicken Sandwich   \n",
       "14          15  Mcdonalds                                      Hamburger   \n",
       "15          16  Mcdonalds                                   Lobster Roll   \n",
       "16          17  Mcdonalds                Maple Bacon Dijon 1/4 lb Burger   \n",
       "17          18  Mcdonalds     Grilled Maple Bacon Dijon Chicken Sandwich   \n",
       "18          19  Mcdonalds      Crispy Maple Bacon Dijon Chicken Sandwich   \n",
       "19          20  Mcdonalds                                      McChicken   \n",
       "\n",
       "    calories  cal_fat  total_fat  sat_fat  trans_fat  cholesterol  sodium  \\\n",
       "0        380       60          7      2.0        0.0           95    1110   \n",
       "1        840      410         45     17.0        1.5          130    1580   \n",
       "2       1130      600         67     27.0        3.0          220    1920   \n",
       "3        750      280         31     10.0        0.5          155    1940   \n",
       "4        920      410         45     12.0        0.5          120    1980   \n",
       "5        540      250         28     10.0        1.0           80     950   \n",
       "6        300      100         12      5.0        0.5           40     680   \n",
       "7        510      210         24      4.0        0.0           65    1040   \n",
       "8        430      190         21     11.0        1.0           85    1040   \n",
       "9        770      400         45     21.0        2.5          175    1290   \n",
       "10       380      170         18      4.0        0.0           40     640   \n",
       "11       620      300         34     13.0        1.5           95     790   \n",
       "12       530      180         20      7.0        0.0          125    1150   \n",
       "13       700      300         34      9.0        0.0           85    1190   \n",
       "14       250       70          8      3.0        0.0           30     480   \n",
       "15       290       50          5      1.5        0.0           65     630   \n",
       "16       640      330         36     14.0        1.5          110    1260   \n",
       "17       580      190         21      8.0        0.0          135    1890   \n",
       "18       740      310         35      9.0        0.5           95    1780   \n",
       "19       350      130         15      3.5        0.0           40     600   \n",
       "\n",
       "    total_carb  fiber  sugar  protein  vit_a  vit_c  calcium  salad  \n",
       "0           44    3.0     11     37.0    4.0   20.0     20.0  Other  \n",
       "1           62    2.0     18     46.0    6.0   20.0     20.0  Other  \n",
       "2           63    3.0     18     70.0   10.0   20.0     50.0  Other  \n",
       "3           62    2.0     18     55.0    6.0   25.0     20.0  Other  \n",
       "4           81    4.0     18     46.0    6.0   20.0     20.0  Other  \n",
       "5           46    3.0      9     25.0   10.0    2.0     15.0  Other  \n",
       "6           33    2.0      7     15.0   10.0    2.0     10.0  Other  \n",
       "7           49    3.0      6     25.0    0.0    4.0      2.0  Other  \n",
       "8           35    2.0      7     25.0   20.0    4.0     15.0  Other  \n",
       "9           42    3.0     10     51.0   20.0    6.0     20.0  Other  \n",
       "10          38    2.0      5     15.0    2.0    0.0     15.0  Other  \n",
       "11          48    3.0     11     32.0   10.0   10.0     35.0  Other  \n",
       "12          48    3.0     11     42.0   10.0   20.0     35.0  Other  \n",
       "13          67    5.0     11     33.0   10.0   15.0     35.0  Other  \n",
       "14          31    2.0      6     13.0    2.0    2.0      4.0  Other  \n",
       "15          35    2.0      3     24.0    4.0    6.0     15.0  Other  \n",
       "16          40    3.0     10     37.0    6.0   15.0     15.0  Other  \n",
       "17          50    3.0     14     48.0    4.0   30.0     30.0  Other  \n",
       "18          69    5.0     14     39.0    4.0   20.0    290.0  Other  \n",
       "19          40    2.0      5     15.0    2.0    2.0      4.0  Other  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood = pd.read_csv('fast_food.csv')\n",
    "fastfood.head(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delete unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'restaurant', 'item', 'calories', 'cal_fat', 'total_fat',\n",
       "       'sat_fat', 'trans_fat', 'cholesterol', 'sodium', 'total_carb', 'fiber',\n",
       "       'sugar', 'protein', 'vit_a', 'vit_c', 'calcium', 'salad'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastfood = fastfood.drop( columns = ['Unnamed: 0'],axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>calories</th>\n",
       "      <th>cal_fat</th>\n",
       "      <th>total_fat</th>\n",
       "      <th>sat_fat</th>\n",
       "      <th>trans_fat</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>sodium</th>\n",
       "      <th>total_carb</th>\n",
       "      <th>fiber</th>\n",
       "      <th>sugar</th>\n",
       "      <th>protein</th>\n",
       "      <th>vit_a</th>\n",
       "      <th>vit_c</th>\n",
       "      <th>calcium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>503.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>305.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>530.912621</td>\n",
       "      <td>238.813592</td>\n",
       "      <td>26.590291</td>\n",
       "      <td>8.153398</td>\n",
       "      <td>0.465049</td>\n",
       "      <td>72.456311</td>\n",
       "      <td>1246.737864</td>\n",
       "      <td>45.664078</td>\n",
       "      <td>4.137177</td>\n",
       "      <td>7.262136</td>\n",
       "      <td>27.891051</td>\n",
       "      <td>18.857143</td>\n",
       "      <td>20.170492</td>\n",
       "      <td>24.852459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>282.436147</td>\n",
       "      <td>166.407510</td>\n",
       "      <td>18.411876</td>\n",
       "      <td>6.418811</td>\n",
       "      <td>0.839644</td>\n",
       "      <td>63.160406</td>\n",
       "      <td>689.954278</td>\n",
       "      <td>24.883342</td>\n",
       "      <td>3.037460</td>\n",
       "      <td>6.761301</td>\n",
       "      <td>17.683921</td>\n",
       "      <td>31.384330</td>\n",
       "      <td>30.592243</td>\n",
       "      <td>25.522073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2430.000000</td>\n",
       "      <td>1270.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>805.000000</td>\n",
       "      <td>6080.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>290.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          calories      cal_fat   total_fat     sat_fat   trans_fat  \\\n",
       "count   515.000000   515.000000  515.000000  515.000000  515.000000   \n",
       "mean    530.912621   238.813592   26.590291    8.153398    0.465049   \n",
       "std     282.436147   166.407510   18.411876    6.418811    0.839644   \n",
       "min      20.000000     0.000000    0.000000    0.000000    0.000000   \n",
       "25%     330.000000   120.000000   14.000000    4.000000    0.000000   \n",
       "50%     490.000000   210.000000   23.000000    7.000000    0.000000   \n",
       "75%     690.000000   310.000000   35.000000   11.000000    1.000000   \n",
       "max    2430.000000  1270.000000  141.000000   47.000000    8.000000   \n",
       "\n",
       "       cholesterol       sodium  total_carb       fiber       sugar  \\\n",
       "count   515.000000   515.000000  515.000000  503.000000  515.000000   \n",
       "mean     72.456311  1246.737864   45.664078    4.137177    7.262136   \n",
       "std      63.160406   689.954278   24.883342    3.037460    6.761301   \n",
       "min       0.000000    15.000000    0.000000    0.000000    0.000000   \n",
       "25%      35.000000   800.000000   28.500000    2.000000    3.000000   \n",
       "50%      60.000000  1110.000000   44.000000    3.000000    6.000000   \n",
       "75%      95.000000  1550.000000   57.000000    5.000000    9.000000   \n",
       "max     805.000000  6080.000000  156.000000   17.000000   87.000000   \n",
       "\n",
       "          protein       vit_a       vit_c     calcium  \n",
       "count  514.000000  301.000000  305.000000  305.000000  \n",
       "mean    27.891051   18.857143   20.170492   24.852459  \n",
       "std     17.683921   31.384330   30.592243   25.522073  \n",
       "min      1.000000    0.000000    0.000000    0.000000  \n",
       "25%     16.000000    4.000000    4.000000    8.000000  \n",
       "50%     24.500000   10.000000   10.000000   20.000000  \n",
       "75%     36.000000   20.000000   30.000000   30.000000  \n",
       "max    186.000000  180.000000  400.000000  290.000000  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(515, 17)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restaurant       0\n",
       "item             0\n",
       "calories         0\n",
       "cal_fat          0\n",
       "total_fat        0\n",
       "sat_fat          0\n",
       "trans_fat        0\n",
       "cholesterol      0\n",
       "sodium           0\n",
       "total_carb       0\n",
       "fiber           12\n",
       "sugar            0\n",
       "protein          1\n",
       "vit_a          214\n",
       "vit_c          210\n",
       "calcium        210\n",
       "salad            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##  Any null values in data?   inspecting by factor/feature\n",
    "fastfood.isnull().sum(axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restaurant</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Arbys</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Burger King</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Chick Fil-A</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dairy Queen</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sonic</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Subway</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Taco Bell</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0        count\n",
       "restaurant        \n",
       "Arbys           55\n",
       "Burger King     70\n",
       "Chick Fil-A     27\n",
       "Dairy Queen     42\n",
       "Mcdonalds       57\n",
       "Sonic           53\n",
       "Subway          96\n",
       "Taco Bell      115"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(fastfood['restaurant'], columns='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restaurant\n",
       "Arbys           55\n",
       "Burger King     70\n",
       "Chick Fil-A     27\n",
       "Dairy Queen     42\n",
       "Mcdonalds       57\n",
       "Sonic           53\n",
       "Subway          96\n",
       "Taco Bell      115\n",
       "Name: item, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood.groupby(['restaurant'])['item'].count()  ## what does the output mean?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1 Piece Chick-n-Strips</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1/2 lb. FlameThrower® GrillBurger</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1/2 lb. GrillBurger with Cheese</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1/2 lb.* Cheesy Potato Burrito</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1/2 lb.* Combo Burrito</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WHOPPER w/o Cheese</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wild Naked Chicken Chalupa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XXL Grilled Stuft Burrito - Beef</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XXL Grilled Stuft Burrito - Chicken</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XXL Grilled Stuft Burrito - Steak</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0                                count\n",
       "item                                      \n",
       "1 Piece Chick-n-Strips                   1\n",
       "1/2 lb. FlameThrower® GrillBurger        1\n",
       "1/2 lb. GrillBurger with Cheese          1\n",
       "1/2 lb.* Cheesy Potato Burrito           1\n",
       "1/2 lb.* Combo Burrito                   1\n",
       "...                                    ...\n",
       "WHOPPER w/o Cheese                       1\n",
       "Wild Naked Chicken Chalupa               1\n",
       "XXL Grilled Stuft Burrito - Beef         1\n",
       "XXL Grilled Stuft Burrito - Chicken      1\n",
       "XXL Grilled Stuft Burrito - Steak        1\n",
       "\n",
       "[505 rows x 1 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab( fastfood['item'], columns='count')  ## what we found?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salad</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Other</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  count\n",
       "salad       \n",
       "Other    515"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab( fastfood['salad'], columns='count')  ## what we found?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating subsets by containing words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>restaurant</th>\n",
       "      <th>item</th>\n",
       "      <th>calories</th>\n",
       "      <th>cal_fat</th>\n",
       "      <th>total_fat</th>\n",
       "      <th>sat_fat</th>\n",
       "      <th>trans_fat</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>sodium</th>\n",
       "      <th>total_carb</th>\n",
       "      <th>fiber</th>\n",
       "      <th>sugar</th>\n",
       "      <th>protein</th>\n",
       "      <th>vit_a</th>\n",
       "      <th>vit_c</th>\n",
       "      <th>calcium</th>\n",
       "      <th>salad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Single Bacon Smokehouse Burger</td>\n",
       "      <td>840</td>\n",
       "      <td>410</td>\n",
       "      <td>45</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>130</td>\n",
       "      <td>1580</td>\n",
       "      <td>62</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Double Bacon Smokehouse Burger</td>\n",
       "      <td>1130</td>\n",
       "      <td>600</td>\n",
       "      <td>67</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>220</td>\n",
       "      <td>1920</td>\n",
       "      <td>63</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Cheeseburger</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>40</td>\n",
       "      <td>680</td>\n",
       "      <td>33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Double Cheeseburger</td>\n",
       "      <td>430</td>\n",
       "      <td>190</td>\n",
       "      <td>21</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85</td>\n",
       "      <td>1040</td>\n",
       "      <td>35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Garlic White Cheddar Burger</td>\n",
       "      <td>620</td>\n",
       "      <td>300</td>\n",
       "      <td>34</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>95</td>\n",
       "      <td>790</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   restaurant                            item  calories  cal_fat  total_fat  \\\n",
       "1   Mcdonalds  Single Bacon Smokehouse Burger       840      410         45   \n",
       "2   Mcdonalds  Double Bacon Smokehouse Burger      1130      600         67   \n",
       "6   Mcdonalds                    Cheeseburger       300      100         12   \n",
       "8   Mcdonalds             Double Cheeseburger       430      190         21   \n",
       "11  Mcdonalds     Garlic White Cheddar Burger       620      300         34   \n",
       "\n",
       "    sat_fat  trans_fat  cholesterol  sodium  total_carb  fiber  sugar  \\\n",
       "1      17.0        1.5          130    1580          62    2.0     18   \n",
       "2      27.0        3.0          220    1920          63    3.0     18   \n",
       "6       5.0        0.5           40     680          33    2.0      7   \n",
       "8      11.0        1.0           85    1040          35    2.0      7   \n",
       "11     13.0        1.5           95     790          48    3.0     11   \n",
       "\n",
       "    protein  vit_a  vit_c  calcium  salad  \n",
       "1      46.0    6.0   20.0     20.0  Other  \n",
       "2      70.0   10.0   20.0     50.0  Other  \n",
       "6      15.0   10.0    2.0     10.0  Other  \n",
       "8      25.0   20.0    4.0     15.0  Other  \n",
       "11     32.0   10.0   10.0     35.0  Other  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_burger = fastfood[ fastfood['item'].str.contains('Burger|burger')]\n",
    "fastfood_burger.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant</th>\n",
       "      <th>item</th>\n",
       "      <th>calories</th>\n",
       "      <th>cal_fat</th>\n",
       "      <th>total_fat</th>\n",
       "      <th>sat_fat</th>\n",
       "      <th>trans_fat</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>sodium</th>\n",
       "      <th>total_carb</th>\n",
       "      <th>fiber</th>\n",
       "      <th>sugar</th>\n",
       "      <th>protein</th>\n",
       "      <th>vit_a</th>\n",
       "      <th>vit_c</th>\n",
       "      <th>calcium</th>\n",
       "      <th>salad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>Taco Bell</td>\n",
       "      <td>1/2 lb.* Cheesy Potato Burrito</td>\n",
       "      <td>540</td>\n",
       "      <td>230</td>\n",
       "      <td>26</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>1360</td>\n",
       "      <td>59</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>401</td>\n",
       "      <td>Taco Bell</td>\n",
       "      <td>1/2 lb.* Combo Burrito</td>\n",
       "      <td>460</td>\n",
       "      <td>170</td>\n",
       "      <td>18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>1320</td>\n",
       "      <td>53</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>402</td>\n",
       "      <td>Taco Bell</td>\n",
       "      <td>7-Layer Burrito</td>\n",
       "      <td>510</td>\n",
       "      <td>170</td>\n",
       "      <td>19</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1090</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>403</td>\n",
       "      <td>Taco Bell</td>\n",
       "      <td>Bean Burrito</td>\n",
       "      <td>370</td>\n",
       "      <td>100</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>960</td>\n",
       "      <td>56</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>404</td>\n",
       "      <td>Taco Bell</td>\n",
       "      <td>Beefy 5-Layer Burrito</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>22</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "      <td>1270</td>\n",
       "      <td>68</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    restaurant                            item  calories  cal_fat  total_fat  \\\n",
       "400  Taco Bell  1/2 lb.* Cheesy Potato Burrito       540      230         26   \n",
       "401  Taco Bell          1/2 lb.* Combo Burrito       460      170         18   \n",
       "402  Taco Bell                 7-Layer Burrito       510      170         19   \n",
       "403  Taco Bell                    Bean Burrito       370      100         11   \n",
       "404  Taco Bell           Beefy 5-Layer Burrito       550      200         22   \n",
       "\n",
       "     sat_fat  trans_fat  cholesterol  sodium  total_carb  fiber  sugar  \\\n",
       "400      7.0        1.0           45    1360          59    7.0      4   \n",
       "401      7.0        1.0           45    1320          53    9.0      3   \n",
       "402      7.0        0.0           20    1090          68   11.0      4   \n",
       "403      4.0        0.0            5     960          56    9.0      3   \n",
       "404      8.0        0.0           35    1270          68    8.0      5   \n",
       "\n",
       "     protein  vit_a  vit_c  calcium  salad  \n",
       "400     19.0    NaN    NaN      NaN  Other  \n",
       "401     21.0    NaN    NaN      NaN  Other  \n",
       "402     16.0    NaN    NaN      NaN  Other  \n",
       "403     13.0    NaN    NaN      NaN  Other  \n",
       "404     19.0    NaN    NaN      NaN  Other  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_burrito = fastfood[ fastfood['item'].str.contains('Burrito')]\n",
    "fastfood_burrito.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Artisan Grilled Chicken Sandwich</td>\n",
       "      <td>380</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95</td>\n",
       "      <td>1110</td>\n",
       "      <td>44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Grilled Bacon Smokehouse Chicken Sandwich</td>\n",
       "      <td>750</td>\n",
       "      <td>280</td>\n",
       "      <td>31</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>155</td>\n",
       "      <td>1940</td>\n",
       "      <td>62</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Crispy Bacon Smokehouse Chicken Sandwich</td>\n",
       "      <td>920</td>\n",
       "      <td>410</td>\n",
       "      <td>45</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>120</td>\n",
       "      <td>1980</td>\n",
       "      <td>81</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Classic Chicken Sandwich</td>\n",
       "      <td>510</td>\n",
       "      <td>210</td>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>1040</td>\n",
       "      <td>49</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Grilled Garlic White Cheddar Chicken Sandwich</td>\n",
       "      <td>530</td>\n",
       "      <td>180</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125</td>\n",
       "      <td>1150</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>42.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   restaurant                                           item  calories  \\\n",
       "0   Mcdonalds               Artisan Grilled Chicken Sandwich       380   \n",
       "3   Mcdonalds      Grilled Bacon Smokehouse Chicken Sandwich       750   \n",
       "4   Mcdonalds       Crispy Bacon Smokehouse Chicken Sandwich       920   \n",
       "7   Mcdonalds                       Classic Chicken Sandwich       510   \n",
       "12  Mcdonalds  Grilled Garlic White Cheddar Chicken Sandwich       530   \n",
       "\n",
       "    cal_fat  total_fat  sat_fat  trans_fat  cholesterol  sodium  total_carb  \\\n",
       "0        60          7      2.0        0.0           95    1110          44   \n",
       "3       280         31     10.0        0.5          155    1940          62   \n",
       "4       410         45     12.0        0.5          120    1980          81   \n",
       "7       210         24      4.0        0.0           65    1040          49   \n",
       "12      180         20      7.0        0.0          125    1150          48   \n",
       "\n",
       "    fiber  sugar  protein  vit_a  vit_c  calcium  salad  \n",
       "0     3.0     11     37.0    4.0   20.0     20.0  Other  \n",
       "3     2.0     18     55.0    6.0   25.0     20.0  Other  \n",
       "4     4.0     18     46.0    6.0   20.0     20.0  Other  \n",
       "7     3.0      6     25.0    0.0    4.0      2.0  Other  \n",
       "12    3.0     11     42.0   10.0   20.0     35.0  Other  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_sandwich = fastfood[ fastfood['item'].str.contains('Sandwich')]\n",
    "fastfood_sandwich.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>restaurant</th>\n",
       "      <th>item</th>\n",
       "      <th>calories</th>\n",
       "      <th>cal_fat</th>\n",
       "      <th>total_fat</th>\n",
       "      <th>sat_fat</th>\n",
       "      <th>trans_fat</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>sodium</th>\n",
       "      <th>total_carb</th>\n",
       "      <th>fiber</th>\n",
       "      <th>sugar</th>\n",
       "      <th>protein</th>\n",
       "      <th>vit_a</th>\n",
       "      <th>vit_c</th>\n",
       "      <th>calcium</th>\n",
       "      <th>salad</th>\n",
       "      <th>burger</th>\n",
       "      <th>my_groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Artisan Grilled Chicken Sandwich</td>\n",
       "      <td>380</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95</td>\n",
       "      <td>1110</td>\n",
       "      <td>44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Single Bacon Smokehouse Burger</td>\n",
       "      <td>840</td>\n",
       "      <td>410</td>\n",
       "      <td>45</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>130</td>\n",
       "      <td>1580</td>\n",
       "      <td>62</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Double Bacon Smokehouse Burger</td>\n",
       "      <td>1130</td>\n",
       "      <td>600</td>\n",
       "      <td>67</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>220</td>\n",
       "      <td>1920</td>\n",
       "      <td>63</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Grilled Bacon Smokehouse Chicken Sandwich</td>\n",
       "      <td>750</td>\n",
       "      <td>280</td>\n",
       "      <td>31</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>155</td>\n",
       "      <td>1940</td>\n",
       "      <td>62</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>Crispy Bacon Smokehouse Chicken Sandwich</td>\n",
       "      <td>920</td>\n",
       "      <td>410</td>\n",
       "      <td>45</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>120</td>\n",
       "      <td>1980</td>\n",
       "      <td>81</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  restaurant                                       item  calories  cal_fat  \\\n",
       "0  Mcdonalds           Artisan Grilled Chicken Sandwich       380       60   \n",
       "1  Mcdonalds             Single Bacon Smokehouse Burger       840      410   \n",
       "2  Mcdonalds             Double Bacon Smokehouse Burger      1130      600   \n",
       "3  Mcdonalds  Grilled Bacon Smokehouse Chicken Sandwich       750      280   \n",
       "4  Mcdonalds   Crispy Bacon Smokehouse Chicken Sandwich       920      410   \n",
       "\n",
       "   total_fat  sat_fat  trans_fat  cholesterol  sodium  total_carb  fiber  \\\n",
       "0          7      2.0        0.0           95    1110          44    3.0   \n",
       "1         45     17.0        1.5          130    1580          62    2.0   \n",
       "2         67     27.0        3.0          220    1920          63    3.0   \n",
       "3         31     10.0        0.5          155    1940          62    2.0   \n",
       "4         45     12.0        0.5          120    1980          81    4.0   \n",
       "\n",
       "   sugar  protein  vit_a  vit_c  calcium  salad  burger  my_groups  \n",
       "0     11     37.0    4.0   20.0     20.0  Other       0          2  \n",
       "1     18     46.0    6.0   20.0     20.0  Other       1          1  \n",
       "2     18     70.0   10.0   20.0     50.0  Other       1          1  \n",
       "3     18     55.0    6.0   25.0     20.0  Other       0          2  \n",
       "4     18     46.0    6.0   20.0     20.0  Other       0          2  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood['burger'] =  fastfood['item'].apply(lambda x: 1 if 'urger' in x else 0)\n",
    "\n",
    "fastfood['my_groups'] =  fastfood['item'].apply(lambda x: 1 if 'urger' in x else 2 if 'Sandwich' in x else 0)\n",
    "fastfood.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>cal_fat</th>\n",
       "      <th>total_fat</th>\n",
       "      <th>sat_fat</th>\n",
       "      <th>trans_fat</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>sodium</th>\n",
       "      <th>total_carb</th>\n",
       "      <th>fiber</th>\n",
       "      <th>sugar</th>\n",
       "      <th>protein</th>\n",
       "      <th>vit_a</th>\n",
       "      <th>vit_c</th>\n",
       "      <th>calcium</th>\n",
       "      <th>my_groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>calories</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.901661</td>\n",
       "      <td>0.900494</td>\n",
       "      <td>0.739664</td>\n",
       "      <td>0.530354</td>\n",
       "      <td>0.762460</td>\n",
       "      <td>0.817855</td>\n",
       "      <td>0.712701</td>\n",
       "      <td>0.287031</td>\n",
       "      <td>0.437711</td>\n",
       "      <td>0.831957</td>\n",
       "      <td>-0.153963</td>\n",
       "      <td>0.007387</td>\n",
       "      <td>0.351207</td>\n",
       "      <td>0.167346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cal_fat</td>\n",
       "      <td>0.901661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995311</td>\n",
       "      <td>0.852150</td>\n",
       "      <td>0.648422</td>\n",
       "      <td>0.803997</td>\n",
       "      <td>0.670458</td>\n",
       "      <td>0.419373</td>\n",
       "      <td>0.032643</td>\n",
       "      <td>0.255485</td>\n",
       "      <td>0.720379</td>\n",
       "      <td>-0.121748</td>\n",
       "      <td>-0.115456</td>\n",
       "      <td>0.166801</td>\n",
       "      <td>0.141292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_fat</td>\n",
       "      <td>0.900494</td>\n",
       "      <td>0.995311</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846716</td>\n",
       "      <td>0.648821</td>\n",
       "      <td>0.801352</td>\n",
       "      <td>0.669182</td>\n",
       "      <td>0.422543</td>\n",
       "      <td>0.036386</td>\n",
       "      <td>0.259370</td>\n",
       "      <td>0.719518</td>\n",
       "      <td>-0.122280</td>\n",
       "      <td>-0.112868</td>\n",
       "      <td>0.168817</td>\n",
       "      <td>0.150016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sat_fat</td>\n",
       "      <td>0.739664</td>\n",
       "      <td>0.852150</td>\n",
       "      <td>0.846716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.812126</td>\n",
       "      <td>0.764030</td>\n",
       "      <td>0.487456</td>\n",
       "      <td>0.276534</td>\n",
       "      <td>-0.041220</td>\n",
       "      <td>0.234218</td>\n",
       "      <td>0.603645</td>\n",
       "      <td>-0.054292</td>\n",
       "      <td>-0.088834</td>\n",
       "      <td>0.304948</td>\n",
       "      <td>0.096770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trans_fat</td>\n",
       "      <td>0.530354</td>\n",
       "      <td>0.648422</td>\n",
       "      <td>0.648821</td>\n",
       "      <td>0.812126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.680858</td>\n",
       "      <td>0.261466</td>\n",
       "      <td>0.100284</td>\n",
       "      <td>-0.121890</td>\n",
       "      <td>0.112651</td>\n",
       "      <td>0.478960</td>\n",
       "      <td>-0.086352</td>\n",
       "      <td>-0.141754</td>\n",
       "      <td>0.114094</td>\n",
       "      <td>0.070785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cholesterol</td>\n",
       "      <td>0.762460</td>\n",
       "      <td>0.803997</td>\n",
       "      <td>0.801352</td>\n",
       "      <td>0.764030</td>\n",
       "      <td>0.680858</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.596164</td>\n",
       "      <td>0.238728</td>\n",
       "      <td>-0.061503</td>\n",
       "      <td>0.298259</td>\n",
       "      <td>0.880960</td>\n",
       "      <td>-0.057142</td>\n",
       "      <td>-0.015610</td>\n",
       "      <td>0.162438</td>\n",
       "      <td>0.132387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sodium</td>\n",
       "      <td>0.817855</td>\n",
       "      <td>0.670458</td>\n",
       "      <td>0.669182</td>\n",
       "      <td>0.487456</td>\n",
       "      <td>0.261466</td>\n",
       "      <td>0.596164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.671976</td>\n",
       "      <td>0.301023</td>\n",
       "      <td>0.422993</td>\n",
       "      <td>0.766942</td>\n",
       "      <td>-0.134031</td>\n",
       "      <td>0.062994</td>\n",
       "      <td>0.284882</td>\n",
       "      <td>0.110674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_carb</td>\n",
       "      <td>0.712701</td>\n",
       "      <td>0.419373</td>\n",
       "      <td>0.422543</td>\n",
       "      <td>0.276534</td>\n",
       "      <td>0.100284</td>\n",
       "      <td>0.238728</td>\n",
       "      <td>0.671976</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.648839</td>\n",
       "      <td>0.547026</td>\n",
       "      <td>0.474396</td>\n",
       "      <td>-0.187004</td>\n",
       "      <td>0.128177</td>\n",
       "      <td>0.514463</td>\n",
       "      <td>0.128297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fiber</td>\n",
       "      <td>0.287031</td>\n",
       "      <td>0.032643</td>\n",
       "      <td>0.036386</td>\n",
       "      <td>-0.041220</td>\n",
       "      <td>-0.121890</td>\n",
       "      <td>-0.061503</td>\n",
       "      <td>0.301023</td>\n",
       "      <td>0.648839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.236475</td>\n",
       "      <td>0.148053</td>\n",
       "      <td>0.157631</td>\n",
       "      <td>0.491224</td>\n",
       "      <td>0.546734</td>\n",
       "      <td>-0.201021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sugar</td>\n",
       "      <td>0.437711</td>\n",
       "      <td>0.255485</td>\n",
       "      <td>0.259370</td>\n",
       "      <td>0.234218</td>\n",
       "      <td>0.112651</td>\n",
       "      <td>0.298259</td>\n",
       "      <td>0.422993</td>\n",
       "      <td>0.547026</td>\n",
       "      <td>0.236475</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.389959</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>0.229223</td>\n",
       "      <td>0.310559</td>\n",
       "      <td>0.202098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>protein</td>\n",
       "      <td>0.831957</td>\n",
       "      <td>0.720379</td>\n",
       "      <td>0.719518</td>\n",
       "      <td>0.603645</td>\n",
       "      <td>0.478960</td>\n",
       "      <td>0.880960</td>\n",
       "      <td>0.766942</td>\n",
       "      <td>0.474396</td>\n",
       "      <td>0.148053</td>\n",
       "      <td>0.389959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049423</td>\n",
       "      <td>0.100523</td>\n",
       "      <td>0.284497</td>\n",
       "      <td>0.120708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vit_a</td>\n",
       "      <td>-0.153963</td>\n",
       "      <td>-0.121748</td>\n",
       "      <td>-0.122280</td>\n",
       "      <td>-0.054292</td>\n",
       "      <td>-0.086352</td>\n",
       "      <td>-0.057142</td>\n",
       "      <td>-0.134031</td>\n",
       "      <td>-0.187004</td>\n",
       "      <td>0.157631</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>-0.049423</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.332259</td>\n",
       "      <td>0.019624</td>\n",
       "      <td>-0.142410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vit_c</td>\n",
       "      <td>0.007387</td>\n",
       "      <td>-0.115456</td>\n",
       "      <td>-0.112868</td>\n",
       "      <td>-0.088834</td>\n",
       "      <td>-0.141754</td>\n",
       "      <td>-0.015610</td>\n",
       "      <td>0.062994</td>\n",
       "      <td>0.128177</td>\n",
       "      <td>0.491224</td>\n",
       "      <td>0.229223</td>\n",
       "      <td>0.100523</td>\n",
       "      <td>0.332259</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.236129</td>\n",
       "      <td>-0.123360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>calcium</td>\n",
       "      <td>0.351207</td>\n",
       "      <td>0.166801</td>\n",
       "      <td>0.168817</td>\n",
       "      <td>0.304948</td>\n",
       "      <td>0.114094</td>\n",
       "      <td>0.162438</td>\n",
       "      <td>0.284882</td>\n",
       "      <td>0.514463</td>\n",
       "      <td>0.546734</td>\n",
       "      <td>0.310559</td>\n",
       "      <td>0.284497</td>\n",
       "      <td>0.019624</td>\n",
       "      <td>0.236129</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>my_groups</td>\n",
       "      <td>0.167346</td>\n",
       "      <td>0.141292</td>\n",
       "      <td>0.150016</td>\n",
       "      <td>0.096770</td>\n",
       "      <td>0.070785</td>\n",
       "      <td>0.132387</td>\n",
       "      <td>0.110674</td>\n",
       "      <td>0.128297</td>\n",
       "      <td>-0.201021</td>\n",
       "      <td>0.202098</td>\n",
       "      <td>0.120708</td>\n",
       "      <td>-0.142410</td>\n",
       "      <td>-0.123360</td>\n",
       "      <td>0.035346</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             calories   cal_fat  total_fat   sat_fat  trans_fat  cholesterol  \\\n",
       "calories     1.000000  0.901661   0.900494  0.739664   0.530354     0.762460   \n",
       "cal_fat      0.901661  1.000000   0.995311  0.852150   0.648422     0.803997   \n",
       "total_fat    0.900494  0.995311   1.000000  0.846716   0.648821     0.801352   \n",
       "sat_fat      0.739664  0.852150   0.846716  1.000000   0.812126     0.764030   \n",
       "trans_fat    0.530354  0.648422   0.648821  0.812126   1.000000     0.680858   \n",
       "cholesterol  0.762460  0.803997   0.801352  0.764030   0.680858     1.000000   \n",
       "sodium       0.817855  0.670458   0.669182  0.487456   0.261466     0.596164   \n",
       "total_carb   0.712701  0.419373   0.422543  0.276534   0.100284     0.238728   \n",
       "fiber        0.287031  0.032643   0.036386 -0.041220  -0.121890    -0.061503   \n",
       "sugar        0.437711  0.255485   0.259370  0.234218   0.112651     0.298259   \n",
       "protein      0.831957  0.720379   0.719518  0.603645   0.478960     0.880960   \n",
       "vit_a       -0.153963 -0.121748  -0.122280 -0.054292  -0.086352    -0.057142   \n",
       "vit_c        0.007387 -0.115456  -0.112868 -0.088834  -0.141754    -0.015610   \n",
       "calcium      0.351207  0.166801   0.168817  0.304948   0.114094     0.162438   \n",
       "my_groups    0.167346  0.141292   0.150016  0.096770   0.070785     0.132387   \n",
       "\n",
       "               sodium  total_carb     fiber     sugar   protein     vit_a  \\\n",
       "calories     0.817855    0.712701  0.287031  0.437711  0.831957 -0.153963   \n",
       "cal_fat      0.670458    0.419373  0.032643  0.255485  0.720379 -0.121748   \n",
       "total_fat    0.669182    0.422543  0.036386  0.259370  0.719518 -0.122280   \n",
       "sat_fat      0.487456    0.276534 -0.041220  0.234218  0.603645 -0.054292   \n",
       "trans_fat    0.261466    0.100284 -0.121890  0.112651  0.478960 -0.086352   \n",
       "cholesterol  0.596164    0.238728 -0.061503  0.298259  0.880960 -0.057142   \n",
       "sodium       1.000000    0.671976  0.301023  0.422993  0.766942 -0.134031   \n",
       "total_carb   0.671976    1.000000  0.648839  0.547026  0.474396 -0.187004   \n",
       "fiber        0.301023    0.648839  1.000000  0.236475  0.148053  0.157631   \n",
       "sugar        0.422993    0.547026  0.236475  1.000000  0.389959  0.001626   \n",
       "protein      0.766942    0.474396  0.148053  0.389959  1.000000 -0.049423   \n",
       "vit_a       -0.134031   -0.187004  0.157631  0.001626 -0.049423  1.000000   \n",
       "vit_c        0.062994    0.128177  0.491224  0.229223  0.100523  0.332259   \n",
       "calcium      0.284882    0.514463  0.546734  0.310559  0.284497  0.019624   \n",
       "my_groups    0.110674    0.128297 -0.201021  0.202098  0.120708 -0.142410   \n",
       "\n",
       "                vit_c   calcium  my_groups  \n",
       "calories     0.007387  0.351207   0.167346  \n",
       "cal_fat     -0.115456  0.166801   0.141292  \n",
       "total_fat   -0.112868  0.168817   0.150016  \n",
       "sat_fat     -0.088834  0.304948   0.096770  \n",
       "trans_fat   -0.141754  0.114094   0.070785  \n",
       "cholesterol -0.015610  0.162438   0.132387  \n",
       "sodium       0.062994  0.284882   0.110674  \n",
       "total_carb   0.128177  0.514463   0.128297  \n",
       "fiber        0.491224  0.546734  -0.201021  \n",
       "sugar        0.229223  0.310559   0.202098  \n",
       "protein      0.100523  0.284497   0.120708  \n",
       "vit_a        0.332259  0.019624  -0.142410  \n",
       "vit_c        1.000000  0.236129  -0.123360  \n",
       "calcium      0.236129  1.000000   0.035346  \n",
       "my_groups   -0.123360  0.035346   1.000000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d68fb00f88>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sb.heatmap( fastfood.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using pandas for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6a034c2c8>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fastfood.plot.scatter(x= 'calories', y= 'protein' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6a0391c88>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fastfood.boxplot(column=['calories'], by=['restaurant'], figsize=(8,5) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696c39f88>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fastfood['calories'].hist()        ## this is using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restaurant\n",
       "Arbys          532.727273\n",
       "Burger King    608.571429\n",
       "Chick Fil-A    384.444444\n",
       "Dairy Queen    520.238095\n",
       "Mcdonalds      640.350877\n",
       "Sonic          631.698113\n",
       "Subway         503.020833\n",
       "Taco Bell      443.652174\n",
       "Name: calories, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood.groupby(['restaurant'])['calories'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot with seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Calories in items by restaurant')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sb.boxplot(x=\"restaurant\", y=\"calories\", data=fastfood, palette=\"Set1\")\n",
    "plt.title('Calories in items by restaurant')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing item calories in McDonalds and Burger King products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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": [
    "## Two different ways to ask the same\n",
    "\n",
    "x1 = fastfood.calories[fastfood.restaurant=='Burger King']\n",
    "\n",
    "x2 = fastfood[fastfood.restaurant=='Mcdonalds']['calories']\n",
    "\n",
    "# Normalize\n",
    "kwargs = dict(alpha=0.5, bins=8, density=True, stacked=True)  # 8 bins for 8 groups of age, 0 -10 , 11 - 20, ...\n",
    "\n",
    "plt.hist(x1, **kwargs, color='g', label='Burger King');      # green\n",
    "plt.hist(x2, **kwargs, color='b', label='Mcdonalds');   # blue\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing calories by restaurants in burgers only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restaurant\n",
       "Burger King    403.333333\n",
       "Dairy Queen    606.666667\n",
       "Mcdonalds      615.000000\n",
       "Sonic          721.818182\n",
       "Name: calories, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_burger.groupby(['restaurant'])['calories'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Calories in Burgers by restaurant')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x=\"restaurant\", y=\"calories\", data=fastfood_burger, palette=\"Set1\")\n",
    "plt.title('Calories in Burgers by restaurant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cholesterol in Burgers by restaurant')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x=\"restaurant\", y=\"cholesterol\", data=fastfood_burger, palette=\"Set1\")\n",
    "plt.title('Cholesterol in Burgers by restaurant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Trans fat in Burgers by restaurant')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x=\"restaurant\", y=\"trans_fat\", data=fastfood_burger, palette=\"Set1\")\n",
    "plt.title('Trans fat in Burgers by restaurant')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sandwich comparison by restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Calories in sandwich by restaurant')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sb.boxplot(x=\"restaurant\", y=\"calories\", data=fastfood_sandwich, palette=\"Set1\")\n",
    "plt.title('Calories in sandwich by restaurant')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing item calories in McDonalds and Burger King products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d686230f08>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Using x1 for typing reduction only\n",
    "x1= fastfood_sandwich\n",
    "\n",
    "sb.boxplot(x=\"restaurant\", y=\"calories\", data=x1[(x1.restaurant=='Burger King') | (x1.restaurant=='Mcdonalds') ])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CONFIRMATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis based in observations from previous graphs\n",
    "\n",
    "\\begin{align}\n",
    "H_0: \\mu_1  = \\mu_2 \\\\\n",
    "H_1: \\mu_1 \\neq \\mu_2 \\\\\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calories in sandwiches: two independent samples comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-value:  0.18377643096743376\n",
      "pvalue:  0.8559992151726648\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "## x1 comes from previous lines: x1 is fastfood_sandwich\n",
    "\n",
    "df1= x1.calories[x1.restaurant=='Burger King']\n",
    "df2= x1.calories[x1.restaurant=='Mcdonalds']\n",
    "                 \n",
    "tprueba,pvalue =stats.ttest_ind(df1,df2, equal_var=False)\n",
    "print(\"t-value: \",tprueba)\n",
    "print(\"pvalue: \", pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we read this output?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### calories in burgers: two independent samples comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-value:  -2.4266853634975187\n",
      "pvalue:  0.03228851658436662\n"
     ]
    }
   ],
   "source": [
    "x1= fastfood_burger\n",
    "df1= x1.calories[x1.restaurant=='Burger King']\n",
    "df2= x1.calories[x1.restaurant=='Mcdonalds']\n",
    "                 \n",
    "tprueba,pvalue =stats.ttest_ind(df1,df2, equal_var=False)\n",
    "print(\"t-value: \",tprueba)\n",
    "print(\"pvalue: \", pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA\n",
    "#### calories in burgers by restaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restaurant</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Burger King</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dairy Queen</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mcdonalds</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sonic</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0        count\n",
       "restaurant        \n",
       "Burger King     12\n",
       "Dairy Queen     12\n",
       "Mcdonalds       10\n",
       "Sonic           22"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab( x1['restaurant'], columns='count')  ## items per restaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=4.7519053254378445, pvalue=0.005292342210519278)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import f_oneway\n",
    "\n",
    "BB = x1.calories[x1.restaurant==\"Burger King\"]    \n",
    "DQ = x1.calories[x1.restaurant==\"Dairy Queen\"]\n",
    "MC = x1.calories[x1.restaurant==\"Mcdonalds\"]\n",
    "SS = x1.calories[x1.restaurant==\"Sonic\"]\n",
    "\n",
    "f_oneway( BB, DQ, MC,SS)\n",
    "\n",
    "## assumptions not valid?  check  scipy.stats.kruskal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What else to do?\n",
    "- Check normality assumptions\n",
    "- Check homocedasticity\n",
    "- Independence\n",
    "- Multiple comparisons\n",
    "\n",
    "I created a video about testing LM assumptions:\n",
    "\n",
    "https://youtu.be/8M6f2OIXjDg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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