McDonaldsNutritionAnalysis.ipynb

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  "metadata": {
    "colab": {
      "name": "Challenge-1_Kelompok-1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nimMdf-MknK1"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyTU7Q2ykntv",
        "outputId": "de0f1f44-bb4e-42ea-a570-9056e4701b15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.8)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.8.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.0.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (0.0.1)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2020.6.20)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        "id": "eMN7XDIOkphW",
        "outputId": "3c0943d8-2c40-40b0-e4dd-d4cfcd0b1a4d",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9c5e50cc-dcbe-49c8-85e9-c90ce17e2fff\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9c5e50cc-dcbe-49c8-85e9-c90ce17e2fff\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"alifia28\",\"key\":\"91567b3c962f40f66a9c3c6dc890d06b\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cN7Ayo1kvp2"
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aT2BlruAk5Kz",
        "outputId": "f8ed6775-7997-4e49-ed79-24838685669f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "!kaggle datasets download -d mcdonalds/nutrition-facts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading nutrition-facts.zip to /content\n",
            "\r  0% 0.00/7.37k [00:00<?, ?B/s]\n",
            "\r100% 7.37k/7.37k [00:00<00:00, 6.97MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjvXkwTtk9Jv",
        "outputId": "eb6e971d-f689-4dcf-a819-d9cc0c515963",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json  nutrition-facts.zip  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NfRE4AKk_e_"
      },
      "source": [
        "df = pd.read_csv('nutrition-facts.zip')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xu_bCRnDlHVT",
        "outputId": "9ef39c2f-d1af-48fa-d433-fa6a1250ef31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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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>Category</th>\n",
              "      <th>Item</th>\n",
              "      <th>Serving Size</th>\n",
              "      <th>Calories</th>\n",
              "      <th>Calories from Fat</th>\n",
              "      <th>Total Fat</th>\n",
              "      <th>Total Fat (% Daily Value)</th>\n",
              "      <th>Saturated Fat</th>\n",
              "      <th>Saturated Fat (% Daily Value)</th>\n",
              "      <th>Trans Fat</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Cholesterol (% Daily Value)</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Egg McMuffin</td>\n",
              "      <td>4.8 oz (136 g)</td>\n",
              "      <td>300</td>\n",
              "      <td>120</td>\n",
              "      <td>13.0</td>\n",
              "      <td>20</td>\n",
              "      <td>5.0</td>\n",
              "      <td>25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>260</td>\n",
              "      <td>87</td>\n",
              "      <td>750</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Egg White Delight</td>\n",
              "      <td>4.8 oz (135 g)</td>\n",
              "      <td>250</td>\n",
              "      <td>70</td>\n",
              "      <td>8.0</td>\n",
              "      <td>12</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25</td>\n",
              "      <td>8</td>\n",
              "      <td>770</td>\n",
              "      <td>32</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Sausage McMuffin</td>\n",
              "      <td>3.9 oz (111 g)</td>\n",
              "      <td>370</td>\n",
              "      <td>200</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35</td>\n",
              "      <td>8.0</td>\n",
              "      <td>42</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45</td>\n",
              "      <td>15</td>\n",
              "      <td>780</td>\n",
              "      <td>33</td>\n",
              "      <td>29</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Sausage McMuffin with Egg</td>\n",
              "      <td>5.7 oz (161 g)</td>\n",
              "      <td>450</td>\n",
              "      <td>250</td>\n",
              "      <td>28.0</td>\n",
              "      <td>43</td>\n",
              "      <td>10.0</td>\n",
              "      <td>52</td>\n",
              "      <td>0.0</td>\n",
              "      <td>285</td>\n",
              "      <td>95</td>\n",
              "      <td>860</td>\n",
              "      <td>36</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Sausage McMuffin with Egg Whites</td>\n",
              "      <td>5.7 oz (161 g)</td>\n",
              "      <td>400</td>\n",
              "      <td>210</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35</td>\n",
              "      <td>8.0</td>\n",
              "      <td>42</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50</td>\n",
              "      <td>16</td>\n",
              "      <td>880</td>\n",
              "      <td>37</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Category  ... Iron (% Daily Value)\n",
              "0  Breakfast  ...                   15\n",
              "1  Breakfast  ...                    8\n",
              "2  Breakfast  ...                   10\n",
              "3  Breakfast  ...                   15\n",
              "4  Breakfast  ...                   10\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vh1qfJamogut",
        "outputId": "e5da7506-22e7-45c7-f2f9-5cf114ba35fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Item</th>\n",
              "      <th>Serving Size</th>\n",
              "      <th>Calories</th>\n",
              "      <th>Calories from Fat</th>\n",
              "      <th>Total Fat</th>\n",
              "      <th>Total Fat (% Daily Value)</th>\n",
              "      <th>Saturated Fat</th>\n",
              "      <th>Saturated Fat (% Daily Value)</th>\n",
              "      <th>Trans Fat</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Cholesterol (% Daily Value)</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>Smoothies &amp; Shakes</td>\n",
              "      <td>McFlurry with Oreo Cookies (Small)</td>\n",
              "      <td>10.1 oz (285 g)</td>\n",
              "      <td>510</td>\n",
              "      <td>150</td>\n",
              "      <td>17.0</td>\n",
              "      <td>26</td>\n",
              "      <td>9.0</td>\n",
              "      <td>44</td>\n",
              "      <td>0.5</td>\n",
              "      <td>45</td>\n",
              "      <td>14</td>\n",
              "      <td>280</td>\n",
              "      <td>12</td>\n",
              "      <td>80</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>64</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>Smoothies &amp; Shakes</td>\n",
              "      <td>McFlurry with Oreo Cookies (Medium)</td>\n",
              "      <td>13.4 oz (381 g)</td>\n",
              "      <td>690</td>\n",
              "      <td>200</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35</td>\n",
              "      <td>12.0</td>\n",
              "      <td>58</td>\n",
              "      <td>1.0</td>\n",
              "      <td>55</td>\n",
              "      <td>19</td>\n",
              "      <td>380</td>\n",
              "      <td>16</td>\n",
              "      <td>106</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>85</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257</th>\n",
              "      <td>Smoothies &amp; Shakes</td>\n",
              "      <td>McFlurry with Oreo Cookies (Snack)</td>\n",
              "      <td>6.7 oz (190 g)</td>\n",
              "      <td>340</td>\n",
              "      <td>100</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17</td>\n",
              "      <td>6.0</td>\n",
              "      <td>29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30</td>\n",
              "      <td>9</td>\n",
              "      <td>190</td>\n",
              "      <td>8</td>\n",
              "      <td>53</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>Smoothies &amp; Shakes</td>\n",
              "      <td>McFlurry with Reese's Peanut Butter Cups (Medium)</td>\n",
              "      <td>14.2 oz (403 g)</td>\n",
              "      <td>810</td>\n",
              "      <td>290</td>\n",
              "      <td>32.0</td>\n",
              "      <td>50</td>\n",
              "      <td>15.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60</td>\n",
              "      <td>20</td>\n",
              "      <td>400</td>\n",
              "      <td>17</td>\n",
              "      <td>114</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>103</td>\n",
              "      <td>21</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>Smoothies &amp; Shakes</td>\n",
              "      <td>McFlurry with Reese's Peanut Butter Cups (Snack)</td>\n",
              "      <td>7.1 oz (202 g)</td>\n",
              "      <td>410</td>\n",
              "      <td>150</td>\n",
              "      <td>16.0</td>\n",
              "      <td>25</td>\n",
              "      <td>8.0</td>\n",
              "      <td>38</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>200</td>\n",
              "      <td>8</td>\n",
              "      <td>57</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>51</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Category  ... Iron (% Daily Value)\n",
              "255  Smoothies & Shakes  ...                    8\n",
              "256  Smoothies & Shakes  ...                   10\n",
              "257  Smoothies & Shakes  ...                    6\n",
              "258  Smoothies & Shakes  ...                    6\n",
              "259  Smoothies & Shakes  ...                    4\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIMGUSt8ola0",
        "outputId": "70c7459a-64eb-441f-af92-b8ea7e3a21a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(260, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTZMMQDor5R4",
        "outputId": "ac5aaf18-898f-42cc-aa03-a2df4202d06c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 260 entries, 0 to 259\n",
            "Data columns (total 24 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   Category                       260 non-null    object \n",
            " 1   Item                           260 non-null    object \n",
            " 2   Serving Size                   260 non-null    object \n",
            " 3   Calories                       260 non-null    int64  \n",
            " 4   Calories from Fat              260 non-null    int64  \n",
            " 5   Total Fat                      260 non-null    float64\n",
            " 6   Total Fat (% Daily Value)      260 non-null    int64  \n",
            " 7   Saturated Fat                  260 non-null    float64\n",
            " 8   Saturated Fat (% Daily Value)  260 non-null    int64  \n",
            " 9   Trans Fat                      260 non-null    float64\n",
            " 10  Cholesterol                    260 non-null    int64  \n",
            " 11  Cholesterol (% Daily Value)    260 non-null    int64  \n",
            " 12  Sodium                         260 non-null    int64  \n",
            " 13  Sodium (% Daily Value)         260 non-null    int64  \n",
            " 14  Carbohydrates                  260 non-null    int64  \n",
            " 15  Carbohydrates (% Daily Value)  260 non-null    int64  \n",
            " 16  Dietary Fiber                  260 non-null    int64  \n",
            " 17  Dietary Fiber (% Daily Value)  260 non-null    int64  \n",
            " 18  Sugars                         260 non-null    int64  \n",
            " 19  Protein                        260 non-null    int64  \n",
            " 20  Vitamin A (% Daily Value)      260 non-null    int64  \n",
            " 21  Vitamin C (% Daily Value)      260 non-null    int64  \n",
            " 22  Calcium (% Daily Value)        260 non-null    int64  \n",
            " 23  Iron (% Daily Value)           260 non-null    int64  \n",
            "dtypes: float64(3), int64(18), object(3)\n",
            "memory usage: 48.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywxrHCyql-vL"
      },
      "source": [
        "**a) How many calories does the average McDonald's value meal contain?**\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg9nH18ur0Xb",
        "outputId": "e80b21e5-d9b4-4488-cf19-dad24d2ef3c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>Calories</th>\n",
              "      <th>Calories from Fat</th>\n",
              "      <th>Total Fat</th>\n",
              "      <th>Total Fat (% Daily Value)</th>\n",
              "      <th>Saturated Fat</th>\n",
              "      <th>Saturated Fat (% Daily Value)</th>\n",
              "      <th>Trans Fat</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Cholesterol (% Daily Value)</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>260.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>368.269231</td>\n",
              "      <td>127.096154</td>\n",
              "      <td>14.165385</td>\n",
              "      <td>21.815385</td>\n",
              "      <td>6.007692</td>\n",
              "      <td>29.965385</td>\n",
              "      <td>0.203846</td>\n",
              "      <td>54.942308</td>\n",
              "      <td>18.392308</td>\n",
              "      <td>495.750000</td>\n",
              "      <td>20.676923</td>\n",
              "      <td>47.346154</td>\n",
              "      <td>15.780769</td>\n",
              "      <td>1.630769</td>\n",
              "      <td>6.530769</td>\n",
              "      <td>29.423077</td>\n",
              "      <td>13.338462</td>\n",
              "      <td>13.426923</td>\n",
              "      <td>8.534615</td>\n",
              "      <td>20.973077</td>\n",
              "      <td>7.734615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>240.269886</td>\n",
              "      <td>127.875914</td>\n",
              "      <td>14.205998</td>\n",
              "      <td>21.885199</td>\n",
              "      <td>5.321873</td>\n",
              "      <td>26.639209</td>\n",
              "      <td>0.429133</td>\n",
              "      <td>87.269257</td>\n",
              "      <td>29.091653</td>\n",
              "      <td>577.026323</td>\n",
              "      <td>24.034954</td>\n",
              "      <td>28.252232</td>\n",
              "      <td>9.419544</td>\n",
              "      <td>1.567717</td>\n",
              "      <td>6.307057</td>\n",
              "      <td>28.679797</td>\n",
              "      <td>11.426146</td>\n",
              "      <td>24.366381</td>\n",
              "      <td>26.345542</td>\n",
              "      <td>17.019953</td>\n",
              "      <td>8.723263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\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>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>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>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>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>210.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>2.375000</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>107.500000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.750000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>340.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>500.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>22.250000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>21.250000</td>\n",
              "      <td>865.000000</td>\n",
              "      <td>36.250000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1880.000000</td>\n",
              "      <td>1060.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>182.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>575.000000</td>\n",
              "      <td>192.000000</td>\n",
              "      <td>3600.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Calories  ...  Iron (% Daily Value)\n",
              "count   260.000000  ...            260.000000\n",
              "mean    368.269231  ...              7.734615\n",
              "std     240.269886  ...              8.723263\n",
              "min       0.000000  ...              0.000000\n",
              "25%     210.000000  ...              0.000000\n",
              "50%     340.000000  ...              4.000000\n",
              "75%     500.000000  ...             15.000000\n",
              "max    1880.000000  ...             40.000000\n",
              "\n",
              "[8 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNvUSb9QpzL4",
        "outputId": "63b9f195-34a1-4b07-fa0e-a5a83b2db275",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "df.Category.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Breakfast', 'Beef & Pork', 'Chicken & Fish', 'Salads',\n",
              "       'Snacks & Sides', 'Desserts', 'Beverages', 'Coffee & Tea',\n",
              "       'Smoothies & Shakes'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zBiin_slIbt",
        "outputId": "eb70ba03-b3e7-4385-ea3e-e2696757984b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['Calories'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "368.2692307692308"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8m703EevBJr",
        "outputId": "3990d6c7-c139-4d46-ed85-6ce3ded1c937",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df.groupby(['Category'])['Calories'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category\n",
              "Beef & Pork           494.000000\n",
              "Beverages             113.703704\n",
              "Breakfast             526.666667\n",
              "Chicken & Fish        552.962963\n",
              "Coffee & Tea          283.894737\n",
              "Desserts              222.142857\n",
              "Salads                270.000000\n",
              "Smoothies & Shakes    531.428571\n",
              "Snacks & Sides        245.769231\n",
              "Name: Calories, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Lr9IdiPye1i"
      },
      "source": [
        "mcd_meal = df.set_index('Category')\n",
        "mcd_meal = mcd_meal.drop(['Beverages', 'Coffee & Tea', 'Smoothies & Shakes'],axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcYtPHSC61BZ"
      },
      "source": [
        "mcd_meal.reset_index(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ycjpLp90Be_",
        "outputId": "c8265490-b860-4614-8cc8-e1cf47fa6f8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "mcd_meal_avgcal = mcd_meal.groupby(['Category'])['Calories'].mean()\n",
        "mcd_meal_avgcal"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category\n",
              "Beef & Pork       494.000000\n",
              "Breakfast         526.666667\n",
              "Chicken & Fish    552.962963\n",
              "Desserts          222.142857\n",
              "Salads            270.000000\n",
              "Snacks & Sides    245.769231\n",
              "Name: Calories, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOalpbgi8EZ3",
        "outputId": "ccacb523-9b80-49d5-8601-732d42763cc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mcd_meal['Calories'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "462.09090909090907"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKt-oGaTtmnh",
        "outputId": "ac92d4de-7964-4d91-b156-2386193064e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        }
      },
      "source": [
        "a = mcd_meal_avgcal.plot.bar(stacked=False, figsize= (10,8), color=['red','orange'])\n",
        "a.set_title(\"Average Calories on McDonald's Meal\", size=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, \"Average Calories on McDonald's Meal\")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQ_X_qOGmUbw"
      },
      "source": [
        "**b) How much do beverages, like soda or coffee, contribute to the overall caloric intake?**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5w7ZtxXQB9GP",
        "outputId": "8a7dc03b-9db1-4dce-c394-0299e5a7fd1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "source": [
        "df['% Overall Daily Calories Intake'] = df['Calories']/2000*100\n",
        "minuman = df.loc[df['Category'].isin(['Beverages','Coffee & Tea','Smoothies & Shakes'])]\n",
        "minuman.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Category</th>\n",
              "      <th>Item</th>\n",
              "      <th>Serving Size</th>\n",
              "      <th>Calories</th>\n",
              "      <th>Calories from Fat</th>\n",
              "      <th>Total Fat</th>\n",
              "      <th>Total Fat (% Daily Value)</th>\n",
              "      <th>Saturated Fat</th>\n",
              "      <th>Saturated Fat (% Daily Value)</th>\n",
              "      <th>Trans Fat</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Cholesterol (% Daily Value)</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "      <th>% Overall Daily Calories Intake</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Beverages</td>\n",
              "      <td>Coca-Cola Classic (Small)</td>\n",
              "      <td>16 fl oz cup</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Beverages</td>\n",
              "      <td>Coca-Cola Classic (Medium)</td>\n",
              "      <td>21 fl oz cup</td>\n",
              "      <td>200</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Beverages</td>\n",
              "      <td>Coca-Cola Classic (Large)</td>\n",
              "      <td>30 fl oz cup</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Beverages</td>\n",
              "      <td>Coca-Cola Classic (Child)</td>\n",
              "      <td>12 fl oz cup</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Beverages</td>\n",
              "      <td>Diet Coke (Small)</td>\n",
              "      <td>16 fl oz cup</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Category  ... % Overall Daily Calories Intake\n",
              "110  Beverages  ...                             7.0\n",
              "111  Beverages  ...                            10.0\n",
              "112  Beverages  ...                            14.0\n",
              "113  Beverages  ...                             5.0\n",
              "114  Beverages  ...                             0.0\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1YIrON2DliA",
        "outputId": "f94d2b00-71b8-44dc-eca9-b33a10171796",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "cols = list(minuman.columns.values)\n",
        "print(cols)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Category', 'Item', 'Serving Size', 'Calories', 'Calories from Fat', 'Total Fat', 'Total Fat (% Daily Value)', 'Saturated Fat', 'Saturated Fat (% Daily Value)', 'Trans Fat', 'Cholesterol', 'Cholesterol (% Daily Value)', 'Sodium', 'Sodium (% Daily Value)', 'Carbohydrates', 'Carbohydrates (% Daily Value)', 'Dietary Fiber', 'Dietary Fiber (% Daily Value)', 'Sugars', 'Protein', 'Vitamin A (% Daily Value)', 'Vitamin C (% Daily Value)', 'Calcium (% Daily Value)', 'Iron (% Daily Value)', '% Overall Daily Calories Intake']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kh7QWGz-NbtE"
      },
      "source": [
        "minuman = minuman[['Category', 'Item', 'Calories', '% Overall Daily Calories Intake', 'Serving Size', 'Calories from Fat', 'Total Fat', 'Total Fat (% Daily Value)', 'Saturated Fat', 'Saturated Fat (% Daily Value)', 'Trans Fat', 'Cholesterol', 'Cholesterol (% Daily Value)', 'Sodium', 'Sodium (% Daily Value)', 'Carbohydrates', 'Carbohydrates (% Daily Value)', 'Dietary Fiber', 'Dietary Fiber (% Daily Value)', 'Sugars', 'Protein', 'Vitamin A (% Daily Value)', 'Vitamin C (% Daily Value)', 'Calcium (% Daily Value)', 'Iron (% Daily Value)']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akULtcvsNe9K",
        "outputId": "a945e5b1-837d-4660-9c0f-35eed7d23584",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "beve = minuman.groupby('Category')['Calories','% Overall Daily Calories Intake'].mean()\n",
        "bev = pd.DataFrame(beve)\n",
        "bev"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Calories</th>\n",
              "      <th>% Overall Daily Calories Intake</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Beverages</th>\n",
              "      <td>113.703704</td>\n",
              "      <td>5.685185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coffee &amp; Tea</th>\n",
              "      <td>283.894737</td>\n",
              "      <td>14.194737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smoothies &amp; Shakes</th>\n",
              "      <td>531.428571</td>\n",
              "      <td>26.571429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Calories  % Overall Daily Calories Intake\n",
              "Category                                                       \n",
              "Beverages           113.703704                         5.685185\n",
              "Coffee & Tea        283.894737                        14.194737\n",
              "Smoothies & Shakes  531.428571                        26.571429"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTZRPGg4NiMy",
        "outputId": "8bbfafdb-8e3c-4213-bced-e44410ca755f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.boxplot(x=\"Category\", \n",
        "            y=\"% Overall Daily Calories Intake\", \n",
        "            data=minuman)\n",
        "plt.ylabel(\"% of Daily Intake (2000 cal)\", size=14)\n",
        "plt.xlabel(\"Category\", size=14)\n",
        "plt.title(\"Contribution of Type of Beverages to % Calories Daily Intake\", size=18)\n",
        "plt.savefig(\"Graph B.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YLyL+RGnMfMgQt5XXiogndKgVeypwc9vtggWUp51+n8vftcLVwJMjYqX2WOpV4Wb0dr2sRLlF0bKA0lD2L5n5qA50B8vMWyPiFGCPetJ4A+UkMbjrkAWUJ9nOrLU1y2sL4F+Bj2fmR9sH1G5L2i2q/5/cYT6dykb1XdYT8yn1r5Xs/IySbL5jpOmH0L6fDE7mlms/qd/L1yhtDRfU4lksW/vYer0RQyckMPpj8w6U9m1vysxvDYrr4C6mv6b+73TceArlhDyW/aO1Pm4dVGM8pMz8A6UWkYjYiJLEHExpm7TconRivS2l9rpVozfW9TecYyhJzn6UY+PGlAdhJspw59a9gV9n5l7thbUPu6F8l3Iu+zalxv/lbQn5wrq8Vbr9nkerH9uIfYNyRfD+iOjYbigitoyIt9e3p1OeZnlXRDy2bZzHUrLqu+s4y6N1QO6m/US31qQ8mdPu7bW8vYPH1s44uK3We+n8vd9N93H2cp11ciGlUfYbI6LVR1ir37UPUHaSE8dxeY9Sb0ceQ7mabZ3EvznMJB9ufxMRe1BO4O3f0bGU76LjAay2UxrJTylJyuBE4s21fDyutB8lIlrtBNtvIX67/v9UREzvME2nzzOP0l7q9cB/AKd3aHN3LKVvuI41Yl2uJ3jkanuZK+uIeDqD2grVJHo+pU3PJm3jrkx5enKwrr/L2rZksFbHw93sg0PtqydR9oUP1Dhby1ufcrV/LeXEPxoHU2oNP9tWdj3w9Lb3/9JWPpzRHpuH+r52oov2TbW922+Bl9fvuDV9UGqEYGz7xw8oT9V+rEMbRSLicfUieajvfDGlFnVM54co/YidQNn+2vt2G9P6G06Wvsx+SqkBfielBvp7Y5nnKA13bn2IR3/m1SnnviFl5nHAaygJ7an1IoTMvI1ywfTv0eGXJKJ41K9PjEbf1Yhl5r0RsSvl6vKnEXEaJSm4jXJiehHlZHpoHf/2iPgg5cmM8+KR3xLbl3J/+S2ZeQfLp9Xr9CER8V3KkyZ/ysyx3G76M/DRemC5gNKQ/U2U2rCBtvF+STnofbw20LyGcuvnuZQnZjrF+uKI+BAl6cm6YT5Kj9dZp+U9FBHvpBw0z4+IIylV7K+un+dTbVfrvXQUJfF7DfCrYZZ5K2Wn3YDyFOWmlGT5JsrTPgBk5g8j4lvAOyPimZROGW+l1DhsTVmXI7V9O5SSwHylzuMiSoPs/Sjf/6Gj/pSP9syIeH19vSqlhmF/SnX/gW2f5/yIOIjyGS+OiBMoJ+f1KdvpLpQG9u1+Rtk3D6FcTHSq+j+M8qDAZyNie0rj3TspV+E7UParF3XxOa6g3Br9YES0npTcjNJv2aU1xnbvpxw7fhsRX6UkJK9q+wz/uCof5Xd5WkTcDvyGUpu0Fo88vdVKZofTcV/NzCsj4rOUPrJ+HRHHU9pk7U9p//K6zOy6Z/h6G+3tlG4O2jv6/A6lZvhY4HzKRcdZmbl4uPmN9thMefrzRkpHpbMpicu/UWo8LuWRBHA476Z0m/GbiPhKnd+udTnfy8zBNfpdy8zFEfE2SoJ5RUR8m5Lszqyx7U6piVwEHFgToJOpT9BSHnx5CqPbR/eMiFbP+utQ+sV7BSUJe09mntA27nisv+EcSdkfdgXmtT+4NQGGO7f+EHhL3f5/SWmn9yYeaWs2pLofL6Uk2b+IiJ3r53obZX3+um73F1HW+SaU9qbH0nZsH7VuH69c0f4oV9nvrSvv75STxk2Ug8DePLpjtz0oV0/31L/f0qH/KYbu3HQ7yoF030HlH6RUfy+l7ZF3RuhiodNyeKTDu1aHrvfUz/ZtOjxSTjnJ/JxytXJ73bg2HGLem1Laz9xZ48ouPnNP1tkw3+kLKQfuOyk73kUM6tC1m3U7wjLObv/sHYafUee99zDTL+KRDl3vpCSNJwJzhphmb8pJufW5FlG6OHh1lzHPpDw52+ovajElSV530Hiz27fBLubb+n7a/x6i3KL9MfCsIaZ7GaUh8t8oNQbXURqjP6qTyzr+4XXedwAzhhhnJUrj8PPbtrcFlFsKO3W7TVGeHDyBUhNxL+U20R5DbTOUdjS/r99Lq0PX59RxP7g83yWltvL0Or8HKLfzTqHLTnwZZl9tm/9Fdfl31mVt2828B63vS4DDhhj+prr+76jb9vqjmHfXx2bK7eSf1/Huouxf29K5q4VHldXyf6XU3rS2xysYpkPXYeJepvuKtvJtKBeJN/NIR59nUfqRW61tuzy+bg9LaiznUWqyh+2ipj22tr/76/J+Q6m17Ng321jXH8N3ExF1G8jl2L6G6r7i7A7jbsfozq2PodTgXkvZBxZQLhZ2GDwfhu7Q9WV12t9T+wijPBD0Wcrdplan0pdSjgmbj+bzD/5r9WsjqQu1TdPWlL7DHtVAM8qPg8/OzNkTHJomSES8knLV/ZocotZYmgoi4jJKQvuUpmNZkfVjGzGpJ2pjz5dQOnwc01Mymvxq24/VBpWtTGmr9iCldkGakmozgc0pTTY0Bn3XRkwab1H6TXoq5dbYA5QuF9T/VgWurW1QrqS0yXk15XbPIdm5rzipr9UE7J8pDzzcgonYmJmISSN7G6VrhaspDZ4XNRuOJshSSrul3SgPHAQlIXtHZg7+dQtpqvgI5cGvy4F9cmIb6fcl24hJkiQ1xDZikiRJDVkhb02uu+66OXv27KbDkCRJGtEFF1xwa2Z27Ph1hUzEZs+ezfz5w/5urSRJ0qQQEUP+uLu3JiVJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhqyUtMBSJLUjwYGBli4cGHTYSxj8eLFAMyaNavhSJY1Z84c5s6d23QYjTARkyRpiliyZEnTIWgQEzFJknpgMtbwtGIaGBhoOBK12EZMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpIROeiEXE9Ii4KCJOru+fFBHnRcTCiDg+IlaZ6JgkSZKa0ESN2LuBK9reHwJ8MTPnAH8H9msgJkmSpAk3oYlYRMwCXgZ8o74PYHvgh3WUecDuExmTJElSUya6RuxLwAeBh+v7dYDbM/PB+n4xsOEExyRJktSICUvEImJX4ObMvGA5p98/IuZHxPxbbrllnKOTJEmaeBNZI7YN8IqIWAQcR7kleRiwVkSsVMeZBfy108SZeWRmbpWZW82cOXMi4pUkSeqpCUvEMvOAzJyVmbOBvYAzM/N1wFnAnnW0fYATJyomSZKkJk2GfsQ+BPy/iFhIaTP2zYbjkSRJmhArjTzK+MvMs4Gz6+urgWc3EYckSVKTJkONmCRJ0pRkIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCQJuPXWW3nXu97Fbbfd1nQokqYQEzFJAubNm8cf//hH5s2b13QokqYQEzFJU96tt97KqaeeSmZy6qmnWismacKYiEma8ubNm0dmAvDwww9bKyZpwpiISZryTj/9dJYuXQrA0qVLOe200xqOSNJUYSImacrbcccdWXnllQFYeeWV2WmnnRqOSNJUYSImacrbZ599iAgApk2bxj777NNwRJKmChMxSVPeuuuuy84770xEsPPOO7POOus0HZKkKWKlpgOQpMlgn332YdGiRdaGSZpQJmKSRKkVO/zww5sOQ9IU461JSZKkhpiISZIkNcRETJIkqSEmYpIkSQ0xEZMkSWqIiZgkSVJDTMQkSZIaYiImSZLUEBMxSZKkhpiISZIkNcRETJIkqSEmYpIkSQ0xEZMkSWqIiZgkSVJDTMQkSZIaYiImSZLUEBMxSZKkhpiISZIkNcRETJIkqSEmYpIkSQ0xEZMkSWqIiZgkSVJDVmo6AEmSxmJgYICFCxc2HcYKYcGCBQDMnTu34Ugmvzlz5kzIejIRkySt0BYuXMhFl10EazUdyQrg4fLvor9e1Gwck93tE7eorhKxiFgN2BqYDcwAbgEuzMw/9y40SZK6tBY8vN3DTUehPjHt7IlruTVsIhYR2wDvBl4OrAzcASwB1gZWjYirgSOBr2fmXT2OVZIkqa8MmfJFxEnAD4BrgZ2Ax2bmOpk5KzMfA2wKHAzsAFwVETtORMCSJEn9YrgasZ8De2bmA50GZubVwNXAvIh4GrBBD+KTJEnqW0PWiGXmV4dKwjqMe1lmnj5+YUnSxLrqqqvYeeedffpO0oSyHzFJAg4++GDuuecePv7xjzcdiqQpZMhbkxFxF5DdzCQz1xy3iCRpgl111VUsWrQIgEWLFrFw4ULmzJnTbFCSpoTh2oi9c8KikKQGHXzwwcu8//jHP86xxx7bUDSSppIhE7HMnDeRgUhSU1q1YUO9l6ResY2YpClv9uzZw76XpF7pKhGLiFUi4mMRcVVE3BcRD7X/9TpISeqlAw88cJn3H/nIRxqKRNJU022N2CeAfYDPU36p6gPAV4DbgLf3JjRJmhibbbbZP2rBZs+ebUN9SROm20TsVcBbM/MI4CHgxMycC3wUsEd9SSu8Aw88kNVXX93aMEkTqqsf/QbWAy6vr+/mkd+4/zlwyHgHJUkTbbPNNuPUU09tOgxJU0y3NWJ/4ZGfMFoIvKS+3pryI+CSJEkapW4TsZ9Qftwb4DDgYxFxDXAM8I0exCVJktT3uro1mZkHtL3+YURcB2wDXJWZJ/cqOEmSpH7WbRuxZWTmecB54xyLJEnSlNJtP2KfjIi3dih/a0R8YvzDkiRJ6n/dthHbG7ioQ/kFwBvGLxxJkqSpo9tE7PHALR3Kb6N0bSFJkqRRGk33Fdt2KH8BsHj8wpEkSZo6um2sfwTwxYhYBTizlu0AfBo7dJUkSVou3XZf8fmIWBcYAFapxQ8Ah2Xmob0KTpIkqZ913X1FZh4QEQcDm9eiKzLz7t6EJamfDQwMsHDhwqbDWMbixaWVxaxZsxqOZFlz5sxh7ty5TYchqUdG1Y9YZt4DnN+jWCSpMUuW+GttkibecnXoKkljMRlreFoxDQwMNByJpKmk26cmJUmSNM5MxCRJkhpiIiZJktSQEduIRUQALwaeBzyhFt8InAuckZnZu/AkSZL617A1YhGxIXAh8HPgP4DN6t9/AL8A5tdxJEmSNEoj1Yh9FbgdeGJmLvNTRhExCzgW+Aqwe2/CkyRJ6l8jJWI7AM8fnIQBZObiiHgf8JueRCZJktTnRmqsvwRYe5jha9dxRhQRq0XEHyLikoi4LCI+VsufFBHnRcTCiDi+/p6lJElS3xspETsOODYi9oqIdVqFEbFOROwFfAv4XpfLuh/YPjP/Ffg34KUR8VzKj4Z/MTPnAH8H9hvth5AkSVoRjXRr8n11nHnAShHxUC2fDjwIfBN4fzcLqk9Xtn6bcuX6l8D2wGtr+TzgIOBr3YUvSZK04ho2EcvMB4C3RcSHgK2A9eqgG4ELMvPO0SwsIqYDFwBzKI38/wzcnpkP1lEWAz6FKUmSpoSufmuyJlxnjnVhmfkQ8G8RsRbwE+Ap3U4bEfsD+wNsvPHGYw1FkiSpcd106DoLeBuP7tD1t8DXM/O60S40M2+PiLOArYG1ImKlWis2C/jrENMcCRwJsNVWW9mJrCRJWuGN1KHr84ErKB24XkZpmP+9+npP4LKI2KabBUXEzFoTRkTMAHas8z6rzgtgH+DE0X8MSZKkFd7KywwAACAASURBVM9INWJfAr6VmXM7DYyIw+o4z+piWesD82o7sWnADzLz5Ii4HDguIg4GLqI8ACBJktT3RkrEnga8bpjhX6O22xpJZv4ReEaH8quBZ3czD0mSpH4yUj9iNwDD3Xrcpo4jSZKkURqpRuxzwNcj4tnA6cBNtXw9ShuvfYH39Cw6SZKkPjZSP2JfjYjbgPdSeryfXgc9ROkP7A2Z+YPehihJktSfRuy+IjOPB46PiJWBdWvxrZm5tKeRSZIk9bmuOnStHq5/tP2XJEnSchqpsT4RsUdEnAvcC1xf/+6NiHMjYvdeByhJktSvRurQ9S3A8cDllG4stqt/r6N06npcRLy5tyFKkiT1p5FuTX4AeHtmfqPDsB9GxB+AA4Cjxj0ySZKkPjfSrckNgd8MM/wcYIPxC0eSJGnqGCkRu4zyg99DeUsdR5IkSaM00q3J9wE/i4idgdN4dIeuGwK79C48SZKk/jVSh66/ioinU2rFngs8oQ66Efgp8PXMXNTTCCVJkvpUNx26LgI+1PtQJEmSppYR+xGTJElSb3TToeteEXFCRBwZEVsOGrZuRFzdu/AkSZL610gdur4R+DblJ402An4bEfu3jTIdeGLvwpMkSepfI7URew/wrsz8OkBE7Ap8NyJWzczDex6dJElSHxspEZsD/Lz1JjNPjohdgFMiYhpwXC+DkyRJ6mcjJWJ3AOsDi1oFmXluRLwMOAV71ZckSVpuIyVifwB2Bn7XXpiZ59TblKf0KjBJkrqxePFiuAOmnW1HABont8PiXDwhixppq/0isKTTgMz8NbArcOx4ByVJkjQVjNizPvCrYYafDZw9viFJktS9WbNmcUvcwsPbPdx0KOoT086exqwNZ03MsoYaEBGPHc2MRju+JEnSVDfcrckFEXFgRAyZEkbEtIjYOSJOB94x/uFJkiT1r+FuTW4LfBK4OiIuBeYD1wP3Af8EbE75IfAlwKeAo3obqiRJUn8ZMhHLzAXAqyJiI+BVlMTs2cAM4FbgIuBI4JTM9Ma8JEnSKI3UfQWZeR3w+fonSZKkcWKnK5IkSQ0xEZMkSWqIiZgkSVJDTMQkSZIaYiImSZLUkK4TsYhYLyLeHxFfi4h1a9k2EfGk3oUnSZLUv7pKxCJiS+BK4HXAfsCaddCOlE5fJUmSNErd1oh9DjgsM58B3N9W/gtgm3GPSpIkaQroNhHbEpjXofwGYL3xC0eSJGnq6DYRW0L5fcnBngLcPH7hSJIkTR3dJmInAh+NiFXr+4yI2cAhwI96EJckSVLf6zYRez+wNnAL8BjgHGAhcDtwYG9CkyRJ6m8j/ug3QGbeCTw/IrYHnklJ4C7MzF9GxMq9DFCSJKlfdZWIRcQnMvN/MvNM4My28lWAHwKv6FF8kiRJfavbW5P7RcTc9oJaE/ZjYONxj0qSJGkK6KpGDNgZOCsibsvM79aasJ8As4DtexadJElSH+u2jdglEbEbcHJE3Ae8EdgQ2D4zb+tlgJIkSf2q69+azMzfAK8Fvo9JmCRJ0pgNWSMWEScNMehW4B7gmIgAIDNtrC9JkjRKw92aHKq26xe9CESSJGmqGTIRy8w3TmQgkiRJU03XbcQkSZI0vrrtvoKIeCPwGkq/Yau0D8vMTcY5LkmSpL7XVY1YRHwA+DxwATAb+CnwJ8rvTx7dq+AkSZL6Wbe3Jt8M7J+ZBwBLgS/XJyU/DzyxV8FJkiT1s24TsVnAH+rrJcCa9fX3gVeOd1CSJElTQbeJ2I3AuvX1tcDW9fUcIMc7KEmSpKmg20TsTKDVaes3gS9ExFnA8ZQf/pYkSdIodfvU5P7UpC0zvx4Rfwe2AX4EHNGj2CRJkvpat4nYLOC61pvMPB44PspvHG0E/KUHsUmSJPW1bm9NXgPM7FC+dh0mSZKkUeo2EQs6N8pfA7hv/MKRJEmaOoa9NRkRA/VlAp+OiHvbBk8Hng1c3KPYJEmS+tpIbcT+pf4P4KnAA23DHgAuBD7Xg7gkSZL63rCJWGa+CCAivgW8OzPvnJCoJEmSpoCunprMzDf2OhBJkqSppqtELCJWA94N7AA8nkGN/DNzi/EPTZIkqb9124/YV4E9gBOA3+LPGkmSJI1Zt4nY7sB/ZOYvexmMJEnSVNJtP2L30tazviRJksau20TsUOD/1Z80kiRJ0jjo9tbkjsC2wEsj4nJgafvAzHzFeAcmSZLU77pNxG4FftLLQCRJkqYa+xGTJElqSLdtxCRJkjTORvrR75O6mYltxCRJkkZvpFuTt01IFJIkSVPQSD/6bdswSZKkHrGNmCRJUkMmLBGLiI0i4qyIuDwiLouId9fytSPi9IhYUP//00TFJEmS1KSJrBF7EHhfZm4OPBd4R0RsDnwYOCMzNwXOqO8lSZL63oQlYpl5Q2ZeWF/fBVwBbAjsBsyro82j/MC4JElS3+sqEYuIbnvg70pEzAaeAZwHrJeZN9RBNwLrjeeyJEmSJqtua8RuiIjPRcRTx7rAiFgD+BHwnsy8s31YZiaQQ0y3f0TMj4j5t9xyy1jDkCRJaly3idh/Ac8D/hQRv4uI/WpCNSoRsTIlCftuZv64Ft8UEevX4esDN3eaNjOPzMytMnOrmTNnjnbRkiRJk05XiVhmHpWZzwOeDpwDHEypJTs6IrbpZh4REcA3gSsy8wttg04C9qmv9wFO7DZ4SZKkFdmoGutn5hWZ+QFgFqWW7LXAryPi/yLirREx3Py2AfYGto+Ii+vfLsBngB0jYgHw4vpekiSp742qEX5ErAL8O/AmYHtK7dg3gQ2A/wG2A/bqNG1mngPEELPeYTRxSJIk9YOuErGIeCYl+XoNsBQ4FnhnZl7VNs7JwPxeBClJktSPuq0ROx84DdgfODEzH+wwziLguHGKS5Ikqe91m4htkpnXDjdCZt4D+CPhkiRJXer2qclhkzBJkiSN3pA1YhFxF0N0rjpYZq45bhFJkiRNEcPdmnznhEUhSZI0BQ2ZiGXmvKGGSZIkaezG9ce8JU0uAwMDLFy4sOkwVggLFiwAYO7cuQ1HMvnNmTPH9SSNk+HaiN1JeVry1pHai9lGTJqcFi5cyFV/upCN13io6VAmvVWWlmeX7lt0fsORTG5/uXt60yFIfWW4GrF3AXfV17YXk1ZQG6/xEAdudXfTYahPHDx/jaZDkPpKV23EbC8mSZI0/kb1o9+SJEkaP10lYhGxSkR8LCKuioj7IuKh9r9eBylJktSPuq0R+wSwD/B54GHgA8BXgNuAt/cmNEmSpP7WbSL2KuCtmXkE8BDlh7/nAh8FduxVcJIkSf2s20RsPeDy+vpuYK36+ufATuMdlCRJ0lTQbSL2F2CD+noh8JL6emtgyXgHJUmSNBV0m4j9BNihvj4M+FhEXAMcA3yjB3FJkiT1va5+4igzD2h7/cOIWAw8D7gqM0/uVXCSJEn9bLl+azIzfw/8fpxjkSRJmlK6SsQi4oXAK4FNKL85eTVwQmae08PYJEmS+tqIiVhEDFB+a/LvwFVAANsA74yIwzPzPb0NUZKkEdwO0872x2JG1PrZWX8ydHi3AxtOzKKGTcQiYlfgbcBbgG9m5sO1fBrwZuDwiDg9M3/W80glSepgzpw5TYewwliwYAEAm264acORTHIbTtx2NVKN2JuAgcw8qr2wJmRHRMRTgP0AEzFJUiPmzp3bdAgrjNa6GhgYaDgStYxUj7sV8MNhhv+gjiNJkqRRGikRmwlcN8zw6+o4kiRJGqWRErFVgQeGGb4UWGX8wpEkSZo6uum+4q0RcfcQwx47nsFIkiRNJSMlYn8B3tjFOJIkSRqlYROxzJw9QXFIkiRNOfZ+J0mS1BATMUmSpIaYiEmSJDXEREySJKkhQyZiEfGRiHhMfb1xRMTEhSVJktT/hqsR+wiP/D77NdiDviRJ0rgarvuKvwJ7RsTPgABmRcRqnUbMTPsSkyRJGqXhErFPAl8GDgcSOL/DOFGHTR//0CRJkvrbkIlYZh4ZET8AZgMXAi8FbpuguCRJkvreSD3r3w5cHBFvBH6VmfdPTFiSJEn9r5sf/SYz5wFExPbA5pTbkZdn5lk9jE2SJKmvdZWIRcSGwE+ALYHra/EGETEf2CMzrx9yYkmSJHXUbYeuA8BDwJzM3CgzNwI2rWUDvQpOkiSpn3VVIwbsCGyXmde0CjLz6oiYC5zRk8gkSZL63Gh+4ii7LJMkSVIXuq0ROwM4PCJek5nXQfnZI+BLWCM2rgYGBli4cGHTYSxj8eLFAMyaNavhSJY1Z84c5s6d23QYkiQtt25rxOYCqwNXR8S1EXEt8Oda5pmwzy1ZsoQlS5Y0HYYkSX2n2+4rrouIZwIvBp5Si6/IzF/2LLIpajLW8LRiGhjwuQxJksZTt7cmycwETq9/kiRJGqOuEzFJK57Fixdzz13TOXj+Gk2Hoj5x7V3TWb22G5U0dqN5alKSJEnjyBoxqY/NmjWL+x68gQO3urvpUNQnDp6/BqtNsieopRWZNWKSJEkNGXWNWEQ8DdgOmA6ck5kXjndQkiRJU8GoasQi4i3AWcALge2BsyPig70ITJIkqd8NWyMWETMz85a2ornAFpl5Yx2+LfAj4NDehShJktSfRqoR+0NE7Nv2/l4e6dAVYHPgzvEOSpIkaSoYqY3Y84EvR8TewJspNWInRMTKddoHgb17G6IkSVJ/GjYRy8y/AntExCspPeofBWwG/DOlNu3KzLyv51FKkiT1oa4a62fmj4BnALOBc4HVMvMSkzBJkqTlN2L3FRGxC/BU4JLMfGtEPB84OiLOAP47M+/pdZCSJEn9aNgasYj4PPAt4FnAERHxP5l5DrAlcAdwUU3UJEmSNEoj3ZrcF9glM/eiJGN7A2TmA5n5UWB34ICeRihJktSnRkrE7gGeVF9vBCzTJiwzL8/MbXsRmCRJUr8bKRE7ADg2Iq4HfgX8T+9DkiRJmhpG6r7iuxHxc2ATYEFm3j4xYUmSJPW/EZ+azMzbgNsmIBZJkqQpZVQ/+i1JkqTxYyImSZLUEBMxSZKkhpiISZIkNcRETJIkqSEmYpIkSQ0xEZMkSWqIiZgkSVJDRuzQVZIkjd7AwAALFy5sOoxlLFiwAIC5c+c2HMmy5syZM+limigTViMWEUdHxM0R8ae2srUj4vSIWFD//9NExSNJ0lQzY8YMZsyY0XQYajORNWLHAF8Gjm0r+zBwRmZ+JiI+XN9/aAJjkiSpJ6ZqDY9GZ8JqxDLz18DfBhXvBsyrr+cBu09UPJIkSU1rurH+epl5Q319I7Bek8FIkiRNpKYTsX/IzARyqOERsX9EzI+I+bfccssERiZJktQbTSdiN0XE+gD1/81DjZiZR2bmVpm51cyZMycsQEmSpF5pOhE7Cdinvt4HOLHBWCRJkibUhD01GRHfB7YD1o2IxcBHgc8AP4iI/YBrgVdNVDyTsX+XyWqy9jszGU3lvnAkSaM3YYlYZr5miEE7TFQM7RYuXMhFl17Ow49Zu4nFr1DigdJ074I/39hwJJPbtHsHPxQsSdLwpnTP+g8/Zm3u23zXpsNQn1jt8pObDkGStIJpuo2YJEnSlGUiJkmS1JApfWtSmgr+cvd0Dp6/RtNhTHo33VuuS9d7zMMNRzK5/eXu6WzWdBBSHzERk/rYnDlzmg5hhfFAfTp4tdmbNhzJ5LYZblfSeDIRk/qYXWl0r7WuBgYGGo5E0lRiGzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhpiIiZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikiRJDTERkyRJaoiJmCRJUkNMxCRJkhqyUtMBNGXx4sVMu/cOVrv85KZDUZ+Ydu9tLF78YNNhSJJWINaISZIkNWTK1ojNmjWLm+5fifs237XpUNQnVrv8ZGbNekLTYUiSViDWiEmSJDXEREySJKkhJmKSJEkNMRGTJElqiImYJElSQ0zEJEmSGmIiJkmS1BATMUmSpIaYiEmSJDXEREySJKkhJmKSJEkNMRGTJElqiImYJElSQ0zEJEmSGmIiJkmS1BATMUmSpIaYiEmSJDXEREySJKkhJmKSJEkNMRGTJElqiImYJElSQ0zEJEmSGmIiJkmS1BATMUmSpIaYiEmSJDXEREySJKkhJmKSJEkNMRGTJElqiImYJElSQ0zEJEmSGmIiJkmS1BATMUmSpIaYiEmSJDXEREySJKkhJmKSJEkNMRGTJElqyKRIxCLipRFxZUQsjIgPNx2PJEnSRGg8EYuI6cBXgJ2BzYHXRMTmzUYlSZLUe40nYsCzgYWZeXVmPgAcB+zWcEySJEk9t1LTAQAbAte1vV8MPGciFjzt3r+x2uUnT8Siuhb33Uk8vLTpMFYIOW1lcrU1mw7jH6bd+zfgCU2HsUIYGBhg4cKFTYexjAULFgAwd+7chiNZ1pw5cyZdTJLGz2RIxLoSEfsD+wNsvPHGY57fnDlzxjyPXli8+EGWLFnSdBgrhBkzZjBr1mRKfJ4wabcrjWzGjBlNhyBpCorMbDaAiK2BgzLzJfX9AQCZ+emhptlqq61y/vz5ExShJEnS8ouICzJzq07DJkMbsfOBTSPiSRGxCrAXcFLDMUmSJPVc47cmM/PBiHgn8AtgOnB0Zl7WcFiSJEk913giBpCZpwCnNB2HJEnSRJoMtyYlSZKmJBMxSZKkhpiISZIkNcRETJIkqSEmYpIkSQ0xEZMkSWqIiZgkSVJDTMQkSZIaYiImSZLUEBMxSZKkhpiISZIkNcRETJIkqSEmYpIkSQ0xEZMkSWqIiZgkSVJDIjObjmHUIuIW4Nqm45hi1gVubToIqcfczjUVuJ1PvCdm5sxOA1bIREwTLyLmZ+ZWTcch9ZLbuaYCt/PJxVuTkiRJDTERkyRJaoiJmLp1ZNMBSBPA7VxTgdv5JGIbMUmSpIZYIyZJktQQE7EVXEQ8FBEXR8QlEXFhRDyv6ZiksYiIJ0TEcRHx54i4ICJOiYjNRpjmsxFxWf0/MyLOi4iLImLbcYjnzRFxZZ3/24cY57/rfnhx2z55cUTMHevyNXnU7/myiPhj/X6f08NlzY6I17a93zcivjzEuKdExFrjsMwRt/U63pMj4uy6Dq6IiCNHinGYeS2KiHXHGvuKbKWmA9CYLcnMfwOIiJcAnwZe2IsFRURQbmc/3Iv5S3Ub+wkwLzP3qmX/CqwHXDXMpPsDa2fmQxGxF3BpZv7nOMSzEvBJYA5wF7Bxp/Ey85N1PCLi7tY+qf4REVsDuwLPzMz7a/KwSg8XORt4LfC9kUbMzF3GurBut/VqAPhiZp5Yp/2XsS5/KrNGrL+sCfy99SYiPhAR59ert4/Vss9ExDvaxjkoIt4/zPiz6xXSscCfgI0i4msRMb9eNX2sbV67RMT/1VqMgYg4uZavHhFHR8Qfai3FbrX8abXs4rrMTSdgHWlyexGwNDO/3irIzEsy8zdRfDYi/hQRl0bEqwEi4iRgDeCCiPgQcCiwW92uZkTEThHxu1pjfEJErFGn2zIiflW3119ExPpDxLQSsE4WXXckHRHTa7ytfeottXyNiDijxnNpa3/QpLc+cGtm3g+Qmbdm5vXwj1qdT9dtbn5EPLNuU3+OiLfWcYbafjuWA58Btq3zfG8t2yAifh4RCyLi0FZg7bVKEfH6tuPqEXU7nB4Rx7QtozW/wbrd1tcHFrfeZOalbcOGirHjeaNt+IyIODVKrdzUOmdkpn8r8B/wEHAx8H/AHcCWtXwnypMxQUm4TwZeADwD+FXb9JcDGw0z/mzgYeC5bdOsXf9PB84GtgBWA64DnlSHfR84ub7+FPD6+notSs3G6sDhwOtq+SrAjKbXp3/N/gFzKVfanYa9Eji9bnfrAX8B1q/D7m4bb1/gy/X1usCvgdXr+w8BHwFWBn4LzKzlrwaO7rDM1YCzgEta230Xn+Hu+n9/4MD6elVgPvAkysluzbb4FlIfnPJv8v5Rkv2L6/Hrq8AL24YtAt5WX38R+CPwWGAmcFMt77j9DlO+XesYWqffF7gaeFzdLq8FNmpb/rrAU4H/BVau5V8F3gBsCZzeNq+1Ony+rrd14I2U882pwHtb8xshxkedN9pinw38EnhDLZtS5wxvTa742m9Nbg0cGxFPpyRWOwEX1fHWADbNzG9GxOMjYgPKQeLvmXldRLy70/iUg8K1mfn7tmW+KiL2p5xQ1gc2pyRvV2fmNXWc71NORNT5vqJV80bZQTcGfgf8d0TMAn6cmQvGaZ2oPz0f+H5mPgTcFBG/Ap4FnDTMNM+lbJ/nRgSUg/fvgCcDTwdOr+XTgRs6TP9p4Fv19UkRsRPwMuA5mfn+DuO32wnYIiL2rO8fR9mnFgOfiogXUC5yNqScgG8cYX5qUGbeHRFbAttSam6Pj4gPZ+YxdZTWdngpsEZm3gXcFRH3R2m/NdT2O1T5nR3COCMz7wCIiMuBJ1IugFt2oCRd59ftegZwMyU52yQiDgd+BpzWYd5db+uZ+a2I+AXwUmA34C1RmhAMF2On88Yf6zQnAodm5nfr+yl1zjAR6yOZ+btaPT2TUrP16cw8osOoJwB7Ak8Ajq9lHcePiNnAPW3vnwS8H3hWZv49Io6h7CTDCeCVmXnloPIrIuI8ys5+SkS8JTPPHPGDqp9dRtk2x0tQagJes0xhadNyWWZuPcL0LwEOy8xFEfF4yr5zD/DZLpf9rsz8xaBl70vZR7fMzKURsYiR9yFNAjVZOhs4OyIuBfYBjqmD76//H2573Xo/Xufa9vk+1GG+QWlfecDgCWui9BLgrcCrgDcNGmVU23qW27JHA0dHxJ8oFzYdY+zivHEu8NKI+F6W6q4pdc6wjVgfiYinUK7sbwN+AbyprT3MhnXngpJ87UU54Z1Qy4Ybv92alJ3zjohYD9i5ll9JueKaXd+/um2aXwDvinqJFhHPqP83odSiDVCuiLZY/k+vPnEmsGq9cgYgIraI8vTjb4BX1/YuMym3zv8wwvx+D2wTEXPqvFaP8gTmlcDMWotMRKwcEU/rMP1FlFs7AF+g3G56GnBBF5/lF8DbImLluozNImJ1Ss3YzTUJexGlxkCTXJQnBdvbJP0b5dZbt4bafocqv4uyvY3GGcCerWN3RKwdEU+sF+jTMvNHwIHAMztM2/W2HhEvbduunwCsA/x1mLiGOm+0fITSvvkr9f2UOmdYI7bimxERF9fXAexTr9pOi4inAr+r2/LdwOspJ4DLIuKxwF8z8waAzBxq/IfaF5aZl0TERZQ2addRrmTIzCVRHnf+eUTcA5zfNtkngC8Bf4yIacA1lKePXgXsHRFLKbdlPjWeK0YrnszMiNgD+FKUhvf3UdqQvAc4B9ia0oYlgQ9m5rC38zLzlloD9f2IWLUWH5iZV9VbhgMR8TjKsfBLlBq5du8BjoiIy4AllCc6N6W0A3r3CB/nG5S2LxfWE8otwO7Ad4H/rTUq8yn7kia/NYDD623GBylt+/YffpJl/IQO229EDFV+G/BQRFxCqXX7e+fZPiIzL4+IAynH/2nAUuAdlG33W7UM4FE1ZoxuW98JOCwi7qvvP1BjHiqujueNQd5NqV07FPgoU+icYc/6GjcRsUZtRxGUK5sFmfnFpuOSJGmy8takxtOba+3cZZTbL53ap0mSpMoaMUmSpIZYIyZJktQQEzFJkqSGmIhJkiQ1xERMkiSpISZikialiFgvIg6L8sPJ90fEX6P8KPAuXU6/b0Tc3es4JWks7NBV0qRTf6HhXErv4gdQOrucRvktva9TfnduhRIRq2TmA03HIWlysUZM0mT01fp/q8z8QWZemZlXZOaXqT9rEhH/LyL+GBH31Nqyb9Rez4mI7Sg/YLx6RGT9O6gOWyUiDomIxRFxb0ScHxEvaV94RLwsIq6MiPsi4tcRsVedx+y2cf49Ii6ttXXXRcR/t36SpQ5fFBEHRcTREf+/vfsLsaqK4jj+/Rn9F4nsQU0qmyhFQ6hpCJQGhECIIqYMtF4UK9IBs4YIoSihf2IvZRBMf4wYzB4sqB5CsIdCESRpKoWMGMV6kUKwvwquHtYeOp6Z7iReuHduvw8cmHv2Pn8fhsXae5+l48CQpF2SttSuNa3cR1+T36GZTQIOxMysrUi6HFgKvBYRY4YWI+J4+fM0WZZlPrAC6AFeLW27S9vvwMyybS5tbwO95ZgFwDtkyaGF5fpXATuAT4CFwCvApto93kzWad0B3Ag8SWbu+mu3+xhZ1qUb2AAMAisq5ZYAlpMlxT6a6N2YWefxB13NrK1I6gH2An0R8cFZHLeULAR8cUScLjUmt0TE1EqfLuAQcE1EHKns/xD4KSLWSHoBuDsi5lXaNwDPAXMiYkTSEDAzIpZU+jwDrI6I2eX3CPB1RNxZ6XMhWRy5PyLeK/v2Ap9HxMB/fVYz6xzOiJlZuxm/cnC9k7RE0s4yxHiCzE5dAMxocNhN5fwHJP06ugF3AF2lz1zOLFoPGRhWzWNs4eIvgCslTavs21ftEBF/Ae8Cq8ozzCczeW82uGcz62CerG9m7eYQEGSwM25GTNLV5NDhIPA08DMZZG0jg7F/M6Wc+xbgVK3tj3O6639Uhxl+G6f9DWC4DIGuAvZExMEmXdvMJhlnxMysrUTEL8CnQL+kqfX2MiG/mwy41kfEnoj4DphV63oSOK+2bz+ZEZsREd/Xth9Ln9E5XVU9td8HgUW1fYuBoxFxYoLn+5bMsD0IPAC81ai/mXU2B2Jm1o7WkgHTPknLJN0gaa6kR4BhMms2BXhU0hxJy8nJ+VUjwEWSbpd0haRLSsA2BGyVdK+kayV1SxqorFp8HeiSkRfnWwAAAShJREFUtLlctw94uLSNZrteBnrLqsjrJd0PPE5tUn8Dg8ATwKXA9rN7NWbWSRyImVnbiYgfyKHGncBLZPC1C7gLeCgihoF15KrEA8BqYKB2jt1kULUNOEYGPgAryZWTm8js18fAbcDhctxh4J5yra+A9cCz5dg/S58vgWWl3zfAi2U749MUDWwnM3bvT5RBM7PO5lWTZmYTkLQO2AhcFk34pylpFnAE6I2I+qR/M/sf8WR9M7MaSWvJlZPHgFuBp4Ct5xqESTofmA48D+x3EGZmDsTMzMa6jvwA63TgKDnEubEJ510EfEbOcbuvCeczs0nOQ5NmZmZmLeLJ+mZmZmYt4kDMzMzMrEUciJmZmZm1iAMxMzMzsxZxIGZmZmbWIg7EzMzMzFrkb3eDSJjWcog6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTDsemqwmeqf"
      },
      "source": [
        "**c) Does ordered grilled chicken instead of crispy increase a sandwich's nutritional value?**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-GYzIYomfFV"
      },
      "source": [
        "df_chicken = df[df['Item'].str.contains('Chicken', regex=False)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Si6Ha42kkg91",
        "outputId": "6f9098e5-2865-488f-e1f8-b7bb66752cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        }
      },
      "source": [
        "df_chicken.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Category</th>\n",
              "      <th>Item</th>\n",
              "      <th>Serving Size</th>\n",
              "      <th>Calories</th>\n",
              "      <th>Calories from Fat</th>\n",
              "      <th>Total Fat</th>\n",
              "      <th>Total Fat (% Daily Value)</th>\n",
              "      <th>Saturated Fat</th>\n",
              "      <th>Saturated Fat (% Daily Value)</th>\n",
              "      <th>Trans Fat</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Cholesterol (% Daily Value)</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "      <th>% Overall Daily Calories Intake</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Southern Style Chicken Biscuit (Regular Biscuit)</td>\n",
              "      <td>5 oz (143 g)</td>\n",
              "      <td>410</td>\n",
              "      <td>180</td>\n",
              "      <td>20.0</td>\n",
              "      <td>31</td>\n",
              "      <td>8.0</td>\n",
              "      <td>41</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>1180</td>\n",
              "      <td>49</td>\n",
              "      <td>41</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>20.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Southern Style Chicken Biscuit (Large Biscuit)</td>\n",
              "      <td>5.5 oz (157 g)</td>\n",
              "      <td>470</td>\n",
              "      <td>220</td>\n",
              "      <td>24.0</td>\n",
              "      <td>37</td>\n",
              "      <td>9.0</td>\n",
              "      <td>45</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>1290</td>\n",
              "      <td>54</td>\n",
              "      <td>46</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>23.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Chicken &amp; Fish</td>\n",
              "      <td>Premium Crispy Chicken Classic Sandwich</td>\n",
              "      <td>7.5 oz (213 g)</td>\n",
              "      <td>510</td>\n",
              "      <td>200</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33</td>\n",
              "      <td>3.5</td>\n",
              "      <td>18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45</td>\n",
              "      <td>16</td>\n",
              "      <td>990</td>\n",
              "      <td>41</td>\n",
              "      <td>55</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>10</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Chicken &amp; Fish</td>\n",
              "      <td>Premium Grilled Chicken Classic Sandwich</td>\n",
              "      <td>7 oz (200 g)</td>\n",
              "      <td>350</td>\n",
              "      <td>80</td>\n",
              "      <td>9.0</td>\n",
              "      <td>13</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>65</td>\n",
              "      <td>22</td>\n",
              "      <td>820</td>\n",
              "      <td>34</td>\n",
              "      <td>42</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>17.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Chicken &amp; Fish</td>\n",
              "      <td>Premium Crispy Chicken Club Sandwich</td>\n",
              "      <td>8.8 oz (249 g)</td>\n",
              "      <td>670</td>\n",
              "      <td>300</td>\n",
              "      <td>33.0</td>\n",
              "      <td>51</td>\n",
              "      <td>9.0</td>\n",
              "      <td>44</td>\n",
              "      <td>0.0</td>\n",
              "      <td>85</td>\n",
              "      <td>29</td>\n",
              "      <td>1410</td>\n",
              "      <td>59</td>\n",
              "      <td>58</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>36</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>30</td>\n",
              "      <td>20</td>\n",
              "      <td>33.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Category  ... % Overall Daily Calories Intake\n",
              "16       Breakfast  ...                            20.5\n",
              "17       Breakfast  ...                            23.5\n",
              "57  Chicken & Fish  ...                            25.5\n",
              "58  Chicken & Fish  ...                            17.5\n",
              "59  Chicken & Fish  ...                            33.5\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DorpThcqlR6I"
      },
      "source": [
        "df_chicken_nut = df_chicken.drop(['Category', 'Serving Size', 'Calories', 'Calories from Fat',\n",
        "       'Total Fat', 'Total Fat (% Daily Value)', 'Saturated Fat',\n",
        "       'Saturated Fat (% Daily Value)', 'Trans Fat', 'Cholesterol',\n",
        "       'Cholesterol (% Daily Value)','% Overall Daily Calories Intake'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9zmIs2jlWsl",
        "outputId": "5e706bfe-c0ac-4a51-fe82-0dfbbe21ec6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_chicken_nut.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5g0OJ1OlbKW"
      },
      "source": [
        "df_grilledchicken_nut = df_chicken_nut[df_chicken_nut['Item'].str.contains('Grilled', regex=False)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrNq1u8Rlg9M",
        "outputId": "752f792d-3aa1-4b1c-cacc-56617648b3ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_grilledchicken_nut.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNvE6P82mAlW"
      },
      "source": [
        "df_crispychicken_nut = df_chicken_nut[df_chicken_nut['Item'].str.contains('Crispy', regex=False)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIy92cDrmDS1",
        "outputId": "b951b208-fd50-484f-f9f2-0fdce52b2354",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_crispychicken_nut.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPEryotqmS5o",
        "outputId": "c36a3667-1880-4874-b476-69f34418fb29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "df_grilledchicken_nut.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Item</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Premium Grilled Chicken Classic Sandwich</td>\n",
              "      <td>820</td>\n",
              "      <td>34</td>\n",
              "      <td>42</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Premium Grilled Chicken Club Sandwich</td>\n",
              "      <td>1250</td>\n",
              "      <td>52</td>\n",
              "      <td>44</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>30</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Premium Grilled Chicken Ranch BLT Sandwich</td>\n",
              "      <td>1230</td>\n",
              "      <td>51</td>\n",
              "      <td>43</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Bacon Clubhouse Grilled Chicken Sandwich</td>\n",
              "      <td>1560</td>\n",
              "      <td>65</td>\n",
              "      <td>51</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Premium McWrap Chicken &amp; Bacon (Grilled Chicken)</td>\n",
              "      <td>1370</td>\n",
              "      <td>57</td>\n",
              "      <td>42</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>36</td>\n",
              "      <td>60</td>\n",
              "      <td>25</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Item  ...  Iron (% Daily Value)\n",
              "58          Premium Grilled Chicken Classic Sandwich  ...                    20\n",
              "60             Premium Grilled Chicken Club Sandwich  ...                    20\n",
              "62        Premium Grilled Chicken Ranch BLT Sandwich  ...                    20\n",
              "64          Bacon Clubhouse Grilled Chicken Sandwich  ...                    15\n",
              "71  Premium McWrap Chicken & Bacon (Grilled Chicken)  ...                    20\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9e5G_cR9mTG6",
        "outputId": "c18acfbf-59a4-407d-f95e-d4e27aa51ec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "df_crispychicken_nut.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Item</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Premium Crispy Chicken Classic Sandwich</td>\n",
              "      <td>990</td>\n",
              "      <td>41</td>\n",
              "      <td>55</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>10</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Premium Crispy Chicken Club Sandwich</td>\n",
              "      <td>1410</td>\n",
              "      <td>59</td>\n",
              "      <td>58</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>36</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>30</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Premium Crispy Chicken Ranch BLT Sandwich</td>\n",
              "      <td>1400</td>\n",
              "      <td>58</td>\n",
              "      <td>57</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>32</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Bacon Clubhouse Crispy Chicken Sandwich</td>\n",
              "      <td>1720</td>\n",
              "      <td>72</td>\n",
              "      <td>65</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>36</td>\n",
              "      <td>8</td>\n",
              "      <td>25</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Southern Style Crispy Chicken Sandwich</td>\n",
              "      <td>910</td>\n",
              "      <td>38</td>\n",
              "      <td>43</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Item  ...  Iron (% Daily Value)\n",
              "57    Premium Crispy Chicken Classic Sandwich  ...                    20\n",
              "59       Premium Crispy Chicken Club Sandwich  ...                    20\n",
              "61  Premium Crispy Chicken Ranch BLT Sandwich  ...                    20\n",
              "63    Bacon Clubhouse Crispy Chicken Sandwich  ...                    15\n",
              "65     Southern Style Crispy Chicken Sandwich  ...                    15\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nB55oTFDmn04"
      },
      "source": [
        "grilledchicken = df_grilledchicken_nut.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihyQyJnH06Sl",
        "outputId": "42a9861d-48fd-455f-f409-7cfdbf409dff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "grilledchicken.round()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sodium                           1019.0\n",
              "Sodium (% Daily Value)             42.0\n",
              "Carbohydrates                      37.0\n",
              "Carbohydrates (% Daily Value)      12.0\n",
              "Dietary Fiber                       3.0\n",
              "Dietary Fiber (% Daily Value)      13.0\n",
              "Sugars                              8.0\n",
              "Protein                            29.0\n",
              "Vitamin A (% Daily Value)          43.0\n",
              "Vitamin C (% Daily Value)          16.0\n",
              "Calcium (% Daily Value)            16.0\n",
              "Iron (% Daily Value)               16.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gvxwFokmrL-"
      },
      "source": [
        "crispychicken = df_crispychicken_nut.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_Asg75k0_Ow",
        "outputId": "d3fc35b0-7bab-4d8e-c6f7-94e483d0f8bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "crispychicken.round()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sodium                           1149.0\n",
              "Sodium (% Daily Value)             48.0\n",
              "Carbohydrates                      49.0\n",
              "Carbohydrates (% Daily Value)      16.0\n",
              "Dietary Fiber                       3.0\n",
              "Dietary Fiber (% Daily Value)      12.0\n",
              "Sugars                              9.0\n",
              "Protein                            25.0\n",
              "Vitamin A (% Daily Value)          39.0\n",
              "Vitamin C (% Daily Value)          14.0\n",
              "Calcium (% Daily Value)            17.0\n",
              "Iron (% Daily Value)               16.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hInFtpwImwIm"
      },
      "source": [
        "df_allchick = pd.DataFrame({'Crispy Chicken': crispychicken.round(),\n",
        "                   'Grilled Chicken': grilledchicken.round()})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWTHT9FanDlF",
        "outputId": "8f378d22-be24-4384-9413-9402c6a7851b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "df_allchick"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Crispy Chicken</th>\n",
              "      <th>Grilled Chicken</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sodium</th>\n",
              "      <td>1149.0</td>\n",
              "      <td>1019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <td>48.0</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carbohydrates</th>\n",
              "      <td>49.0</td>\n",
              "      <td>37.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <td>16.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <td>12.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sugars</th>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Protein</th>\n",
              "      <td>25.0</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <td>39.0</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <td>14.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <td>17.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Crispy Chicken  Grilled Chicken\n",
              "Sodium                                 1149.0           1019.0\n",
              "Sodium (% Daily Value)                   48.0             42.0\n",
              "Carbohydrates                            49.0             37.0\n",
              "Carbohydrates (% Daily Value)            16.0             12.0\n",
              "Dietary Fiber                             3.0              3.0\n",
              "Dietary Fiber (% Daily Value)            12.0             13.0\n",
              "Sugars                                    9.0              8.0\n",
              "Protein                                  25.0             29.0\n",
              "Vitamin A (% Daily Value)                39.0             43.0\n",
              "Vitamin C (% Daily Value)                14.0             16.0\n",
              "Calcium (% Daily Value)                  17.0             16.0\n",
              "Iron (% Daily Value)                     16.0             16.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5ddZ2zPnP1M",
        "outputId": "4186f6c9-1f7b-436b-fa09-92afa4372ea2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        }
      },
      "source": [
        "ax = df_allchick.plot.barh(stacked=False, figsize= (20,10), color=['orange', 'red'])\n",
        "ax.set_title(\"Nutrition Comparison (Crispy vs Grilled)\", size=20)\n",
        "\n",
        "for p in ax.patches:\n",
        "    left, bottom, width, height = p.get_bbox().bounds\n",
        "    ax.annotate(str(width), xy=(left+width/2, bottom+height/2), \n",
        "                horizontalalignment='left', verticalalignment='center')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwwyUeVIna5e"
      },
      "source": [
        "**d) What about ordering egg whites instead of whole eggs?**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rniQhiJqnbHA"
      },
      "source": [
        "df_wholeegg = df[df['Item'].str.contains('Egg', regex=False)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gW0KriJFRxNh",
        "outputId": "2cfaaa2a-fec9-49c2-b9b0-d02962680b2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "df_wholeegg.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Category', 'Item', 'Serving Size', 'Calories', 'Calories from Fat',\n",
              "       'Total Fat', 'Total Fat (% Daily Value)', 'Saturated Fat',\n",
              "       'Saturated Fat (% Daily Value)', 'Trans Fat', 'Cholesterol',\n",
              "       'Cholesterol (% Daily Value)', 'Sodium', 'Sodium (% Daily Value)',\n",
              "       'Carbohydrates', 'Carbohydrates (% Daily Value)', 'Dietary Fiber',\n",
              "       'Dietary Fiber (% Daily Value)', 'Sugars', 'Protein',\n",
              "       'Vitamin A (% Daily Value)', 'Vitamin C (% Daily Value)',\n",
              "       'Calcium (% Daily Value)', 'Iron (% Daily Value)',\n",
              "       '% Overall Daily Calories Intake'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_MIM-XASXD4"
      },
      "source": [
        "df_wholeegg_nut = df_wholeegg.drop(['Category', 'Serving Size', 'Calories', 'Calories from Fat',\n",
        "       'Total Fat', 'Total Fat (% Daily Value)', 'Saturated Fat',\n",
        "       'Saturated Fat (% Daily Value)', 'Trans Fat', 'Cholesterol',\n",
        "       'Cholesterol (% Daily Value)','% Overall Daily Calories Intake'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOqFfK6DStP7",
        "outputId": "9065ddba-c4f1-484f-d812-145e5b4ce6c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_wholeegg_nut.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ks5OFGwIUDhZ"
      },
      "source": [
        "df_eggwhites_nut = df_wholeegg_nut[df_wholeegg_nut['Item'].str.contains('Whites', regex=False)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTocXeQdUglE",
        "outputId": "8bec8ac6-f1ff-4640-e6aa-192f90ddb6d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_eggwhites_nut.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afm1cLXaVG7F"
      },
      "source": [
        "df_egg_nut = df_wholeegg_nut[~df_wholeegg_nut['Item'].str.contains('Whites',regex=False)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmUTzj4gV7Au",
        "outputId": "4df0fcda-9574-4910-de35-f2be5e0b522f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_egg_nut.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0V7_u28_UE1M",
        "outputId": "6d4e8fe3-aaf4-449a-c6a8-28fc2666691e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        }
      },
      "source": [
        "df_egg_nut"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Egg McMuffin</td>\n",
              "      <td>750</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
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              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Egg White Delight</td>\n",
              "      <td>770</td>\n",
              "      <td>32</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sausage McMuffin with Egg</td>\n",
              "      <td>860</td>\n",
              "      <td>36</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Steak &amp; Egg McMuffin</td>\n",
              "      <td>960</td>\n",
              "      <td>40</td>\n",
              "      <td>31</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>26</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>30</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bacon, Egg &amp; Cheese Biscuit (Regular Biscuit)</td>\n",
              "      <td>1300</td>\n",
              "      <td>54</td>\n",
              "      <td>38</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Bacon, Egg &amp; Cheese Biscuit (Large Biscuit)</td>\n",
              "      <td>1410</td>\n",
              "      <td>59</td>\n",
              "      <td>43</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Sausage Biscuit with Egg (Regular Biscuit)</td>\n",
              "      <td>1170</td>\n",
              "      <td>49</td>\n",
              "      <td>36</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Sausage Biscuit with Egg (Large Biscuit)</td>\n",
              "      <td>1280</td>\n",
              "      <td>53</td>\n",
              "      <td>42</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Steak &amp; Egg Biscuit (Regular Biscuit)</td>\n",
              "      <td>1470</td>\n",
              "      <td>61</td>\n",
              "      <td>38</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
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              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bacon, Egg &amp; Cheese McGriddles</td>\n",
              "      <td>1250</td>\n",
              "      <td>52</td>\n",
              "      <td>48</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
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              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Sausage, Egg &amp; Cheese McGriddles</td>\n",
              "      <td>1320</td>\n",
              "      <td>55</td>\n",
              "      <td>48</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Bacon, Egg &amp; Cheese Bagel</td>\n",
              "      <td>1480</td>\n",
              "      <td>62</td>\n",
              "      <td>57</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>30</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Steak, Egg &amp; Cheese Bagel</td>\n",
              "      <td>1510</td>\n",
              "      <td>63</td>\n",
              "      <td>56</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>33</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             Item  ...  Iron (% Daily Value)\n",
              "0                                    Egg McMuffin  ...                    15\n",
              "1                               Egg White Delight  ...                     8\n",
              "3                       Sausage McMuffin with Egg  ...                    15\n",
              "5                            Steak & Egg McMuffin  ...                    20\n",
              "6   Bacon, Egg & Cheese Biscuit (Regular Biscuit)  ...                    15\n",
              "7     Bacon, Egg & Cheese Biscuit (Large Biscuit)  ...                    20\n",
              "12     Sausage Biscuit with Egg (Regular Biscuit)  ...                    20\n",
              "13       Sausage Biscuit with Egg (Large Biscuit)  ...                    20\n",
              "18          Steak & Egg Biscuit (Regular Biscuit)  ...                    25\n",
              "19                 Bacon, Egg & Cheese McGriddles  ...                    15\n",
              "22               Sausage, Egg & Cheese McGriddles  ...                    15\n",
              "24                      Bacon, Egg & Cheese Bagel  ...                    20\n",
              "26                      Steak, Egg & Cheese Bagel  ...                    25\n",
              "\n",
              "[13 rows x 13 columns]"
            ]
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      "cell_type": "code",
      "metadata": {
        "id": "xkdOIvdCWs_W",
        "outputId": "d8bd1816-3c82-4271-a81b-276c99146334",
        "colab": {
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      "source": [
        "df_eggwhites_nut"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Item</th>\n",
              "      <th>Sodium</th>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <th>Sugars</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sausage McMuffin with Egg Whites</td>\n",
              "      <td>880</td>\n",
              "      <td>37</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
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              "      <td>2</td>\n",
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              "      <td>25</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Bacon, Egg &amp; Cheese Biscuit with Egg Whites (R...</td>\n",
              "      <td>1300</td>\n",
              "      <td>54</td>\n",
              "      <td>36</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bacon, Egg &amp; Cheese Biscuit with Egg Whites (L...</td>\n",
              "      <td>1420</td>\n",
              "      <td>59</td>\n",
              "      <td>42</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Sausage Biscuit with Egg Whites (Regular Biscuit)</td>\n",
              "      <td>1180</td>\n",
              "      <td>49</td>\n",
              "      <td>34</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Sausage Biscuit with Egg Whites (Large Biscuit)</td>\n",
              "      <td>1290</td>\n",
              "      <td>54</td>\n",
              "      <td>40</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Bacon, Egg &amp; Cheese McGriddles with Egg Whites</td>\n",
              "      <td>1250</td>\n",
              "      <td>52</td>\n",
              "      <td>47</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Sausage, Egg &amp; Cheese McGriddles with Egg Whites</td>\n",
              "      <td>1320</td>\n",
              "      <td>55</td>\n",
              "      <td>46</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>21</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Bacon, Egg &amp; Cheese Bagel with Egg Whites</td>\n",
              "      <td>1480</td>\n",
              "      <td>62</td>\n",
              "      <td>55</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Big Breakfast with Egg Whites (Regular Biscuit)</td>\n",
              "      <td>1590</td>\n",
              "      <td>66</td>\n",
              "      <td>50</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Big Breakfast with Egg Whites (Large Biscuit)</td>\n",
              "      <td>1700</td>\n",
              "      <td>71</td>\n",
              "      <td>55</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Big Breakfast with Hotcakes and Egg Whites (Re...</td>\n",
              "      <td>2170</td>\n",
              "      <td>91</td>\n",
              "      <td>110</td>\n",
              "      <td>37</td>\n",
              "      <td>6</td>\n",
              "      <td>23</td>\n",
              "      <td>17</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Big Breakfast with Hotcakes and Egg Whites (La...</td>\n",
              "      <td>2290</td>\n",
              "      <td>95</td>\n",
              "      <td>115</td>\n",
              "      <td>38</td>\n",
              "      <td>7</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 Item  ...  Iron (% Daily Value)\n",
              "4                    Sausage McMuffin with Egg Whites  ...                    10\n",
              "8   Bacon, Egg & Cheese Biscuit with Egg Whites (R...  ...                    10\n",
              "9   Bacon, Egg & Cheese Biscuit with Egg Whites (L...  ...                    15\n",
              "14  Sausage Biscuit with Egg Whites (Regular Biscuit)  ...                    15\n",
              "15    Sausage Biscuit with Egg Whites (Large Biscuit)  ...                    15\n",
              "20     Bacon, Egg & Cheese McGriddles with Egg Whites  ...                    10\n",
              "23   Sausage, Egg & Cheese McGriddles with Egg Whites  ...                    10\n",
              "25          Bacon, Egg & Cheese Bagel with Egg Whites  ...                    15\n",
              "29    Big Breakfast with Egg Whites (Regular Biscuit)  ...                    15\n",
              "30      Big Breakfast with Egg Whites (Large Biscuit)  ...                    15\n",
              "33  Big Breakfast with Hotcakes and Egg Whites (Re...  ...                    30\n",
              "34  Big Breakfast with Hotcakes and Egg Whites (La...  ...                    30\n",
              "\n",
              "[12 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-qnk6NAXTLM"
      },
      "source": [
        "wholeeggs = df_egg_nut.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWay519V1mf0",
        "outputId": "6e77389b-9c20-4de0-e615-c756cc83dae0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "wholeeggs.round()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sodium                           1195.0\n",
              "Sodium (% Daily Value)             50.0\n",
              "Carbohydrates                      41.0\n",
              "Carbohydrates (% Daily Value)      14.0\n",
              "Dietary Fiber                       3.0\n",
              "Dietary Fiber (% Daily Value)      12.0\n",
              "Sugars                              5.0\n",
              "Protein                            22.0\n",
              "Vitamin A (% Daily Value)          12.0\n",
              "Vitamin C (% Daily Value)           4.0\n",
              "Calcium (% Daily Value)            21.0\n",
              "Iron (% Daily Value)               18.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ko0GVJ3gYA-"
      },
      "source": [
        "whiteeggs = df_eggwhites_nut.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZSP1MaH1pTC",
        "outputId": "b1c25f17-53aa-45b4-c033-5910e98c9165",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "whiteeggs.round()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sodium                           1489.0\n",
              "Sodium (% Daily Value)             62.0\n",
              "Carbohydrates                      55.0\n",
              "Carbohydrates (% Daily Value)      18.0\n",
              "Dietary Fiber                       3.0\n",
              "Dietary Fiber (% Daily Value)      14.0\n",
              "Sugars                              8.0\n",
              "Protein                            24.0\n",
              "Vitamin A (% Daily Value)           3.0\n",
              "Vitamin C (% Daily Value)           4.0\n",
              "Calcium (% Daily Value)            16.0\n",
              "Iron (% Daily Value)               16.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vluLoA5g6P-"
      },
      "source": [
        "df_all = pd.DataFrame({'Whole Eggs': wholeeggs.round(),\n",
        "                   'White Eggs': whiteeggs.round()})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIbmQddChujp",
        "outputId": "5a54941e-faab-4626-9247-a509632ad364",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "df_all"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Whole Eggs</th>\n",
              "      <th>White Eggs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sodium</th>\n",
              "      <td>1195.0</td>\n",
              "      <td>1489.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sodium (% Daily Value)</th>\n",
              "      <td>50.0</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carbohydrates</th>\n",
              "      <td>41.0</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carbohydrates (% Daily Value)</th>\n",
              "      <td>14.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dietary Fiber</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dietary Fiber (% Daily Value)</th>\n",
              "      <td>12.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sugars</th>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Protein</th>\n",
              "      <td>22.0</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vitamin A (% Daily Value)</th>\n",
              "      <td>12.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vitamin C (% Daily Value)</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calcium (% Daily Value)</th>\n",
              "      <td>21.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iron (% Daily Value)</th>\n",
              "      <td>18.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Whole Eggs  White Eggs\n",
              "Sodium                             1195.0      1489.0\n",
              "Sodium (% Daily Value)               50.0        62.0\n",
              "Carbohydrates                        41.0        55.0\n",
              "Carbohydrates (% Daily Value)        14.0        18.0\n",
              "Dietary Fiber                         3.0         3.0\n",
              "Dietary Fiber (% Daily Value)        12.0        14.0\n",
              "Sugars                                5.0         8.0\n",
              "Protein                              22.0        24.0\n",
              "Vitamin A (% Daily Value)            12.0         3.0\n",
              "Vitamin C (% Daily Value)             4.0         4.0\n",
              "Calcium (% Daily Value)              21.0        16.0\n",
              "Iron (% Daily Value)                 18.0        16.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yw1vPqbUqMu3",
        "outputId": "67f3ef99-8398-4cb8-e3ae-6523bf1f1111",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        }
      },
      "source": [
        "ax = df_all.plot.barh(stacked=False, figsize= (20,10), color=['orange', 'red'])\n",
        "ax.set_title(\"Nutrition Comparison (Whole Eggs vs White Eggs)\", size=20)\n",
        "\n",
        "for p in ax.patches:\n",
        "    left, bottom, width, height = p.get_bbox().bounds\n",
        "    ax.annotate(str(width), xy=(left+width/2, bottom+height/2), \n",
        "                horizontalalignment='left', verticalalignment='center')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odnP8upqnkWf"
      },
      "source": [
        "**e) What is the least number of items could you order from the menu to meet one day's nutritional requirements?**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pH79cOknkkt",
        "outputId": "3c7b426e-dd4e-4eec-a595-a90fe48e0297",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        }
      },
      "source": [
        "persen_fat = df.pivot_table('Total Fat (% Daily Value)','Category').plot(kind='bar', stacked='True', color='y')\n",
        "proteins = df.pivot_table('Protein','Category').plot(kind='bar', stacked='True', color='r')\n",
        "\n",
        "# Dari barplot di bawah, dapat diketahui bahwa makanan tinggi lemak dan tinggi protein\n",
        "# dapat diperoleh dari item yang ada pada kategori Chicken & Fish atau Beef & Pork"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hA544ObC933M",
        "outputId": "aae66061-b9a5-4d43-8339-73732df7b5f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        }
      },
      "source": [
        "persen_karbo = df.pivot_table('Carbohydrates (% Daily Value)','Category').plot(kind='bar', stacked='True', color='g')\n",
        "sugar = df.pivot_table('Sugars','Category').plot(kind='bar', stacked='True', color='c')\n",
        "\n",
        "# Dari dua barplot di bawah diketahui bahwa Smoothies & Shakes memiliki kandungan Karbohidrat yang tinggi,\n",
        "# tetapi tidak disarankan untuk dikonsumsi karena kandungan gulanya juga tinggi. Oleh karena itu,\n",
        "# apabila menginginkan item dengan karbo tinggi tetapi gula rendah, dapat mencoba menu dari kategori\n",
        "# Breakfast atau Chicken & Fish"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-nOuSK696-4"
      },
      "source": [
        "MenuItems = df.Item.tolist()\n",
        "Calories = df.set_index('Item')['Calories'].to_dict()\n",
        "TotalFat = df.set_index('Item')['Total Fat'].to_dict()\n",
        "SaturatedFat = df.set_index('Item')['Saturated Fat'].to_dict()\n",
        "Carbohydrates = df.set_index('Item')['Carbohydrates'].to_dict()\n",
        "Sugars = df.set_index('Item')['Sugars'].to_dict()\n",
        "Protein = df.set_index('Item')['Protein'].to_dict()\n",
        "Sodium = df.set_index('Item')['Sodium'].to_dict()\n",
        "\n",
        "# Energy: 2,000kcal\n",
        "# Total fat: less than 70g\n",
        "# Saturates: less than 20g\n",
        "# Carbohydrate: at least 260g\n",
        "# Total sugars: 90g\n",
        "# Protein: 50g\n",
        "# Salt: less than 6g"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWup-FHI99gQ",
        "outputId": "84a7e39f-6f07-42af-e41d-92fd597d2508",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "from tabulate import tabulate\n",
        "from plotly.offline import init_notebook_mode, iplot\n",
        "init_notebook_mode(connected=True)\n",
        "import plotly.graph_objs as go\n",
        "import os\n",
        "import plotly.figure_factory as ff"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8egpPCN-DeN",
        "outputId": "7131d57f-8dd8-4e70-9c3c-b08c77fc7a41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "pip install pulp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pulp\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c3/22/5743d7b5d69f84fb63a0b4925862522dbf80e82defcd0c447afb694f3fd0/PuLP-2.3-py3-none-any.whl (40.6MB)\n",
            "\u001b[K     |████████████████████████████████| 40.6MB 92kB/s \n",
            "\u001b[?25hCollecting amply>=0.1.2\n",
            "  Downloading https://files.pythonhosted.org/packages/f3/c5/dfa09dd2595a2ab2ab4e6fa7bebef9565812722e1980d04b0edce5032066/amply-0.1.4-py3-none-any.whl\n",
            "Requirement already satisfied: docutils>=0.3 in /usr/local/lib/python3.6/dist-packages (from amply>=0.1.2->pulp) (0.16)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.6/dist-packages (from amply>=0.1.2->pulp) (2.4.7)\n",
            "Installing collected packages: amply, pulp\n",
            "Successfully installed amply-0.1.4 pulp-2.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgCSicDU-LWd"
      },
      "source": [
        "from pulp import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIDMW-bk-Nk8",
        "outputId": "7de1747c-7015-4d77-c38d-c1f5883f1690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "prob = LpProblem(\"McOptimization Problem\", LpMinimize)\n",
        "MenuItems_vars = LpVariable.dicts(\"MenuItems\",MenuItems,lowBound=0, upBound=10,cat='Integer')\n",
        "prob += lpSum([Calories[i]*MenuItems_vars[i] for i in MenuItems]), \"Calories\"\n",
        "prob += lpSum([TotalFat[i]*MenuItems_vars[i] for i in MenuItems]) <= 70, \"TotalFat\"\n",
        "prob += lpSum([SaturatedFat[i]*MenuItems_vars[i] for i in MenuItems]) <= 20, \"Saturated Fat\"\n",
        "prob += lpSum([Carbohydrates[i]*MenuItems_vars[i] for i in MenuItems]) >= 260, \"Carbohydrates_lower\"\n",
        "# prob += lpSum([Carbohydrates[i]*MenuItems_vars[i] for i in MenuItems]) <= 360, \"Carbohydrates_upper\"\n",
        "prob += lpSum([Sugars[i]*MenuItems_vars[i] for i in MenuItems]) >= 80, \"Sugars_lower\"\n",
        "prob += lpSum([Sugars[i]*MenuItems_vars[i] for i in MenuItems]) <= 100, \"Sugars_upper\"\n",
        "\n",
        "prob += lpSum([Protein[i]*MenuItems_vars[i] for i in MenuItems]) >= 45, \"Protein_lower\"\n",
        "prob += lpSum([Protein[i]*MenuItems_vars[i] for i in MenuItems]) <= 55, \"Protein_upper\"\n",
        "\n",
        "prob += lpSum([Sodium[i]*MenuItems_vars[i] for i in MenuItems]) <= 6000, \"Sodium\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pulp/pulp.py:1195: UserWarning:\n",
            "\n",
            "Spaces are not permitted in the name. Converted to '_'\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CG3NEMfN-Pz_"
      },
      "source": [
        "prob.writeLP(\"McOptimization.lp\")\n",
        "prob.solve()\n",
        "data_matrix = []\n",
        "data_matrix.append(['Item', 'Amount','Calories','Total Fat','Carbohydrates','Protein','Sodium'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uD4lZJRB-SNG"
      },
      "source": [
        "for v in prob.variables():\n",
        "    if v.varValue > 0:\n",
        "        Item = df.loc[df['Item'] == v.name.replace('MenuItems_','').replace('_',' ')]\n",
        "        Item_Calories = Item['Calories'].values*v.varValue\n",
        "        Item_TotalFat = Item['Total Fat'].values*v.varValue\n",
        "        Item_Carbohydrates = Item['Carbohydrates'].values*v.varValue\n",
        "        Item_Protein = Item['Protein'].values*v.varValue\n",
        "        Item_Sodium = Item['Sodium'].values*v.varValue\n",
        "\n",
        "\n",
        "        data_matrix.append([v.name.replace('MenuItems_','').replace('_',' '),v.varValue,Item_Calories[0], \\\n",
        "                           Item_TotalFat[0],Item_Carbohydrates[0],Item_Protein[0],Item_Sodium[0]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smhGnuHW-T2R",
        "outputId": "659bbae0-cc96-4633-dee2-9fd97a723a8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sums = 0\n",
        "results = {}\n",
        "print(\"Total Calories = \", value(prob.objective))\n",
        "for constraint in prob.constraints:\n",
        "    s = 0\n",
        "    for var, coefficient in prob.constraints[constraint].items():\n",
        "        sums += var.varValue * coefficient\n",
        "    results[prob.constraints[constraint].name.replace('_lower','').replace('_upper','')] = s  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Calories =  1370.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqq056Qr-V9_",
        "outputId": "c530858e-49e2-4163-dbc2-c1297fb7e69a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "data_matrix\n",
        "coba = pd.DataFrame(data_matrix)\n",
        "tabel = coba.drop_duplicates()\n",
        "hasil = tabel.rename(columns={0:'Item',1:'Amount',2:'Calories',3:'Total Fat',4:'Carbohydrates',5:'Protein',6:'Sodium'})\n",
        "data_item = hasil.drop(index=0)\n",
        "data_item"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Item</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Calories</th>\n",
              "      <th>Total Fat</th>\n",
              "      <th>Carbohydrates</th>\n",
              "      <th>Protein</th>\n",
              "      <th>Sodium</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apple Slices</td>\n",
              "      <td>2</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Diet Dr Pepper (Large)</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fruit &amp; Maple Oatmeal without Brown Sugar</td>\n",
              "      <td>5</td>\n",
              "      <td>1300</td>\n",
              "      <td>20</td>\n",
              "      <td>245</td>\n",
              "      <td>25</td>\n",
              "      <td>575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Side Salad</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Item Amount  ... Protein Sodium\n",
              "1                               Apple Slices      2  ...       0      0\n",
              "2                     Diet Dr Pepper (Large)      7  ...      28    980\n",
              "3  Fruit & Maple Oatmeal without Brown Sugar      5  ...      25    575\n",
              "4                                 Side Salad      2  ...       2     20\n",
              "\n",
              "[4 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3n4NQr9-XfL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}