Lesson13_Python_For_Data_Science_Sorting.ipynb

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    "colab": {
      "name": "Lesson13-Python For Data Science-Sorting.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/paiml/python_for_datascience/blob/master/Lesson13_Python_For_Data_Science_Sorting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "spdivf2TMnGC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Lesson 13 Sorting\n"
      ]
    },
    {
      "metadata": {
        "id": "c_Id55m6Jsbu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Pragmatic AI Labs\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "e5p96AqpSDZa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![alt text](https://paiml.com/images/logo_with_slogan_white_background.png)\n",
        "\n",
        "This notebook was produced by [Pragmatic AI Labs](https://paiml.com/).  You can continue learning about these topics by:\n",
        "\n",
        "*   Buying a copy of [Pragmatic AI: An Introduction to Cloud-Based Machine Learning](http://www.informit.com/store/pragmatic-ai-an-introduction-to-cloud-based-machine-9780134863917)\n",
        "*   Reading an online copy of [Pragmatic AI:Pragmatic AI: An Introduction to Cloud-Based Machine Learning](https://www.safaribooksonline.com/library/view/pragmatic-ai-an/9780134863924/)\n",
        "*  Watching video [Essential Machine Learning and AI with Python and Jupyter Notebook-Video-SafariOnline](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118) on Safari Books Online.\n",
        "* Watching video [AWS Certified Machine Learning-Speciality](https://learning.oreilly.com/videos/aws-certified-machine/9780135556597)\n",
        "* Purchasing video [Essential Machine Learning and AI with Python and Jupyter Notebook- Purchase Video](http://www.informit.com/store/essential-machine-learning-and-ai-with-python-and-jupyter-9780135261095)\n",
        "*   Viewing more content at [noahgift.com](https://noahgift.com/)\n"
      ]
    },
    {
      "metadata": {
        "id": "pBTeTbnRKG_k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qqAIvhP5N4iu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 13.1 Sort in python"
      ]
    },
    {
      "metadata": {
        "id": "quAhiuBw-sq9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Understanding Sorting\n",
        "\n",
        "Python has powerful built-in sorting\n"
      ]
    },
    {
      "metadata": {
        "id": "wwHjj7Br-sUk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### World Food Facts DataSet "
      ]
    },
    {
      "metadata": {
        "id": "yY9gKLFD-03J",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Original Data Source:  https://www.kaggle.com/openfoodfacts/world-food-facts\n",
        "* Modified Source:  https://www.kaggle.com/lwodarzek/nutrition-table-clustering/output"
      ]
    },
    {
      "metadata": {
        "id": "Y8a6auMZTzoa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Ingest"
      ]
    },
    {
      "metadata": {
        "id": "co7HfeLmvvZV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "77Ih-vxcv-BR",
        "colab_type": "code",
        "outputId": "2032ecd4-f6ce-47ce-ac72-8658573261da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\n",
        "    \"https://raw.githubusercontent.com/noahgift/food/master/data/features.en.openfoodfacts.org.products.csv\")\n",
        "df.drop([\"Unnamed: 0\", \"exceeded\", \"g_sum\", \"energy_100g\"], axis=1, inplace=True) #drop two rows we don't need\n",
        "df = df.drop(df.index[[1,11877]]) #drop outlier\n",
        "df.rename(index=str, columns={\"reconstructed_energy\": \"energy_100g\"}, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "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>fat_100g</th>\n",
              "      <th>carbohydrates_100g</th>\n",
              "      <th>sugars_100g</th>\n",
              "      <th>proteins_100g</th>\n",
              "      <th>salt_100g</th>\n",
              "      <th>energy_100g</th>\n",
              "      <th>product</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.57</td>\n",
              "      <td>64.29</td>\n",
              "      <td>14.29</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2267.85</td>\n",
              "      <td>Banana Chips Sweetened (Whole)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>57.14</td>\n",
              "      <td>17.86</td>\n",
              "      <td>3.57</td>\n",
              "      <td>17.86</td>\n",
              "      <td>1.22428</td>\n",
              "      <td>2835.70</td>\n",
              "      <td>Organic Salted Nut Mix</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18.75</td>\n",
              "      <td>57.81</td>\n",
              "      <td>15.62</td>\n",
              "      <td>14.06</td>\n",
              "      <td>0.13970</td>\n",
              "      <td>1953.04</td>\n",
              "      <td>Organic Muesli</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.67</td>\n",
              "      <td>36.67</td>\n",
              "      <td>3.33</td>\n",
              "      <td>16.67</td>\n",
              "      <td>1.60782</td>\n",
              "      <td>2336.91</td>\n",
              "      <td>Zen Party Mix</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>18.18</td>\n",
              "      <td>60.00</td>\n",
              "      <td>21.82</td>\n",
              "      <td>14.55</td>\n",
              "      <td>0.02286</td>\n",
              "      <td>1976.37</td>\n",
              "      <td>Cinnamon Nut Granola</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fat_100g  carbohydrates_100g  sugars_100g  proteins_100g  salt_100g  \\\n",
              "0     28.57               64.29        14.29           3.57    0.00000   \n",
              "2     57.14               17.86         3.57          17.86    1.22428   \n",
              "3     18.75               57.81        15.62          14.06    0.13970   \n",
              "4     36.67               36.67         3.33          16.67    1.60782   \n",
              "5     18.18               60.00        21.82          14.55    0.02286   \n",
              "\n",
              "   energy_100g                         product  \n",
              "0      2267.85  Banana Chips Sweetened (Whole)  \n",
              "2      2835.70          Organic Salted Nut Mix  \n",
              "3      1953.04                  Organic Muesli  \n",
              "4      2336.91                   Zen Party Mix  \n",
              "5      1976.37            Cinnamon Nut Granola  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "GC88vFkPQwLO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Using built-in sorting\n",
        "\n",
        "Convert Pandas DataFrame Columns into a list"
      ]
    },
    {
      "metadata": {
        "id": "-7lMsgk3Qz0m",
        "colab_type": "code",
        "outputId": "f2ed2a81-305e-4e3f-de68-83aa98b2d3cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "food_facts = list(df.columns.values)\n",
        "food_facts"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['fat_100g',\n",
              " 'carbohydrates_100g',\n",
              " 'sugars_100g',\n",
              " 'proteins_100g',\n",
              " 'salt_100g',\n",
              " 'energy_100g',\n",
              " 'product']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "4z2e2QHV4EvD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Alphabetical Sort"
      ]
    },
    {
      "metadata": {
        "id": "4_PU9Buv3wiT",
        "colab_type": "code",
        "outputId": "c3f08d56-ffe6-4687-9602-ed9c2385c2c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "sorted(food_facts)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['carbohydrates_100g',\n",
              " 'energy_100g',\n",
              " 'fat_100g',\n",
              " 'product',\n",
              " 'proteins_100g',\n",
              " 'salt_100g',\n",
              " 'sugars_100g']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "7PISBoZr4xgp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Reverse Alphabetical Sort"
      ]
    },
    {
      "metadata": {
        "id": "kn27GHO640j7",
        "colab_type": "code",
        "outputId": "67d7d8f3-e731-47d5-ffe7-cead4d6914bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "sorted(food_facts, reverse=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sugars_100g',\n",
              " 'salt_100g',\n",
              " 'proteins_100g',\n",
              " 'product',\n",
              " 'fat_100g',\n",
              " 'energy_100g',\n",
              " 'carbohydrates_100g']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "OqDh2Tdz5xoX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Using built in list sort"
      ]
    },
    {
      "metadata": {
        "id": "mL_jSyb16ANi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Only works on a list"
      ]
    },
    {
      "metadata": {
        "id": "IYVo0SlR6FFF",
        "colab_type": "code",
        "outputId": "d8ded999-a582-45ce-96f7-7547f218fc8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "food_facts = list(df.columns.values)\n",
        "print(f\"Before sort: {food_facts}\")\n",
        "food_facts.sort()\n",
        "print(f\"After sort: {food_facts}\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before sort: ['fat_100g', 'carbohydrates_100g', 'sugars_100g', 'proteins_100g', 'salt_100g', 'energy_100g', 'product']\n",
            "After sort: ['carbohydrates_100g', 'energy_100g', 'fat_100g', 'product', 'proteins_100g', 'salt_100g', 'sugars_100g']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9re2e5r8OghX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LRZ_0TGH6oB4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Timing built-in sort function vs list sort method"
      ]
    },
    {
      "metadata": {
        "id": "ytsl-5Mj68XH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "list method"
      ]
    },
    {
      "metadata": {
        "id": "V2nnwt9s7DWL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "food_facts = list(df.columns.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vcQ36Evq6z5h",
        "colab_type": "code",
        "outputId": "a1113bd3-7899-4365-c1e5-a26a956ef9c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "%%timeit -n 3 -r 3\n",
        "food_facts.sort()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3 loops, best of 3: 489 ns per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QhPpYNaM7JJb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "built in function"
      ]
    },
    {
      "metadata": {
        "id": "ue1XDqAc7LDJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "food_facts = list(df.columns.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZaIestd37Mrt",
        "colab_type": "code",
        "outputId": "b1aea201-5e5e-4673-d55b-ab46eaa40de9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "%%timeit -n 3 -r 3\n",
        "sorted(food_facts)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3 loops, best of 3: 656 ns per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RSggCymO8nny",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Sorting Dictionary"
      ]
    },
    {
      "metadata": {
        "id": "2Q3ukiyB9jLG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "sorting a dictionary"
      ]
    },
    {
      "metadata": {
        "id": "GSBRp6_q9Omq",
        "colab_type": "code",
        "outputId": "49280032-3ecf-4f3e-f8e2-10536d8c6613",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "food_facts_row = df.head(1).to_dict()\n",
        "food_facts_row"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'carbohydrates_100g': {'0': 64.29},\n",
              " 'energy_100g': {'0': 2267.85},\n",
              " 'fat_100g': {'0': 28.57},\n",
              " 'product': {'0': 'Banana Chips Sweetened (Whole)'},\n",
              " 'proteins_100g': {'0': 3.57},\n",
              " 'salt_100g': {'0': 0.0},\n",
              " 'sugars_100g': {'0': 14.29}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "id": "SatiHTXJ_Teq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "reverse sort dictionary"
      ]
    },
    {
      "metadata": {
        "id": "qhUojdlf9oSI",
        "colab_type": "code",
        "outputId": "620c2a75-4c71-4e5d-f138-a88c99ee81eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "sorted(food_facts_row, reverse=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sugars_100g',\n",
              " 'salt_100g',\n",
              " 'proteins_100g',\n",
              " 'product',\n",
              " 'fat_100g',\n",
              " 'energy_100g',\n",
              " 'carbohydrates_100g']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "id": "4-TPMSwWADEf",
        "colab_type": "code",
        "outputId": "28141a9a-8be0-40be-c4e2-a98ad0dc10fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "df[\"product\"].head().values"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Banana Chips Sweetened (Whole)', 'Organic Salted Nut Mix',\n",
              "       'Organic Muesli', 'Zen Party Mix', 'Cinnamon Nut Granola'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "Em50pLNG_cYo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Sorting A Generator Pipeline"
      ]
    },
    {
      "metadata": {
        "id": "uYo2NlNh_sHR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def dataframe_rows(df=df, column=\"product\", chunks=10):\n",
        "  \n",
        "    count_row = df.shape[0]\n",
        "    rows = list(df[column].values)\n",
        "    for i in range(0, count_row, chunks):\n",
        "      yield rows[i:i + chunks]\n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6Po9GpYABmxD",
        "colab_type": "code",
        "outputId": "39aad0d3-917b-4ff4-dba5-1fe7d46b6c37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "rows = dataframe_rows()\n",
        "next(rows)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Banana Chips Sweetened (Whole)',\n",
              " 'Organic Salted Nut Mix',\n",
              " 'Organic Muesli',\n",
              " 'Zen Party Mix',\n",
              " 'Cinnamon Nut Granola',\n",
              " 'Organic Hazelnuts',\n",
              " 'Organic Oat Groats',\n",
              " 'Energy Power Mix',\n",
              " 'Antioxidant Mix - Berries & Chocolate',\n",
              " 'Organic Quinoa Coconut Granola With Mango']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "metadata": {
        "id": "1Ka1vYOzHgSn",
        "colab_type": "code",
        "outputId": "8f710a40-094f-43e9-e29e-2baab3f6d3f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "next(rows)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Fire Roasted Hatch Green Chile Almonds',\n",
              " 'Peanut Butter Power Chews',\n",
              " 'Organic Unswt Berry Coconut Granola',\n",
              " 'Roasted Salted Black Pepper Cashews',\n",
              " 'Thai Curry Roasted Cashews',\n",
              " 'Wasabi Tamari Almonds',\n",
              " 'Organic Red Quinoa',\n",
              " 'Dark Chocolate Coconut Chews',\n",
              " 'Organic Unsweetened Granola, Cinnamon Almond',\n",
              " 'Organic Blueberry Almond Granola']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "metadata": {
        "id": "YbEsKMqYHpuR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sorted_row = (sorted(row) for row in rows )\n",
        "print(next(sorted_row))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "1VErz5Z7g0v7"
      },
      "cell_type": "markdown",
      "source": [
        "## 13.2 Create custom sorting functions"
      ]
    },
    {
      "metadata": {
        "id": "Sa-OqhQgIwl4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Building a Shuffle Function"
      ]
    },
    {
      "metadata": {
        "id": "UstkrDyVI1gM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "food_items = ['Chocolate Nut Crunch', 'Cranberries', 'Curry Lentil Soup Mix', \n",
        "                'Milk Chocolate Peanut Butter Malt Balls', 'Organic Harvest Pilaf', \n",
        "                'Organic Tamari Pumpkin Seed', 'Split Pea Soup Mix', \n",
        "                'Swiss-Style Muesli', \"Whole Wheat 'N Honey Fig Bars\", \n",
        "                'Yogurt Pretzels']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3NCAIctKI4ZT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from random import sample\n",
        "\n",
        "def shuffle_list(items):\n",
        "  \"\"\"Randomly Shuffles List\"\"\"\n",
        "  \n",
        "  shuffled = sample(items, len(items))\n",
        "  return shuffled\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0NjLGWrSKB9y",
        "colab_type": "code",
        "outputId": "2738b1a7-04da-4169-c7f7-0e9ff6bbbf0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "shuffled_food_items = shuffle_list(food_items)\n",
        "shuffled_food_items"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Milk Chocolate Peanut Butter Malt Balls',\n",
              " 'Organic Harvest Pilaf',\n",
              " 'Curry Lentil Soup Mix',\n",
              " 'Yogurt Pretzels',\n",
              " 'Organic Tamari Pumpkin Seed',\n",
              " 'Chocolate Nut Crunch',\n",
              " \"Whole Wheat 'N Honey Fig Bars\",\n",
              " 'Split Pea Soup Mix',\n",
              " 'Cranberries',\n",
              " 'Swiss-Style Muesli']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "metadata": {
        "id": "i_Skh8l8S6re",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Custom Sort Functions"
      ]
    },
    {
      "metadata": {
        "id": "OrWriGn-S_S6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Highly Customized Sort"
      ]
    },
    {
      "metadata": {
        "id": "IRoNvB9RS_fM",
        "colab_type": "code",
        "outputId": "46c9839a-78db-4ee2-ff64-7a9a19303bae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "def best_snack(item):\n",
        "  if item == \"Chocolate Nut Crunch\":\n",
        "    return 1\n",
        "  return len(item) \n",
        "\n",
        "sorted(shuffled_food_items, key=best_snack)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Chocolate Nut Crunch',\n",
              " 'Cranberries',\n",
              " 'Yogurt Pretzels',\n",
              " 'Split Pea Soup Mix',\n",
              " 'Swiss-Style Muesli',\n",
              " 'Organic Harvest Pilaf',\n",
              " 'Curry Lentil Soup Mix',\n",
              " 'Organic Tamari Pumpkin Seed',\n",
              " \"Whole Wheat 'N Honey Fig Bars\",\n",
              " 'Milk Chocolate Peanut Butter Malt Balls']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "id": "31myQds7Ns_6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Sorting Objects"
      ]
    },
    {
      "metadata": {
        "id": "_h35dY358Iln",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Food:\n",
        "  def __init__(self, product, protein):\n",
        "    self.product = product\n",
        "    self.protein = protein\n",
        "  def __repr__(self):\n",
        "    return f\"Food: {self.product}, Protein: {self.protein}\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KQ4vP3E-9Cfi",
        "colab_type": "code",
        "outputId": "c6c9321a-4bd9-4e96-d05c-f9faf50799b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "cell_type": "code",
      "source": [
        "pairs = df[[\"product\", \"proteins_100g\"]].head().values.tolist()\n",
        "pairs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['Banana Chips Sweetened (Whole)', 3.57],\n",
              " ['Organic Salted Nut Mix', 17.86],\n",
              " ['Organic Muesli', 14.06],\n",
              " ['Zen Party Mix', 16.67],\n",
              " ['Cinnamon Nut Granola', 14.55]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "id": "CGIVqRgaPbEE",
        "colab_type": "code",
        "outputId": "d87f50f1-e268-45f5-9956-ef52f98cf214",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "cell_type": "code",
      "source": [
        "pairs = df[[\"product\", \"proteins_100g\"]].head().values.tolist()\n",
        "foods = [Food(item[0], item[1]) for item in pairs]\n",
        "foods"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Food: Banana Chips Sweetened (Whole), Protein: 3.57,\n",
              " Food: Organic Salted Nut Mix, Protein: 17.86,\n",
              " Food: Organic Muesli, Protein: 14.06,\n",
              " Food: Zen Party Mix, Protein: 16.67,\n",
              " Food: Cinnamon Nut Granola, Protein: 14.55]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "metadata": {
        "id": "mQ-Pb-AHUZQz",
        "colab_type": "code",
        "outputId": "965add5f-4bc5-4c1f-8bce-3342151336af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "cell_type": "code",
      "source": [
        "sorted(foods, key=lambda food: food.protein)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Food: Banana Chips Sweetened (Whole), Protein: 3.57,\n",
              " Food: Organic Muesli, Protein: 14.06,\n",
              " Food: Cinnamon Nut Granola, Protein: 14.55,\n",
              " Food: Zen Party Mix, Protein: 16.67,\n",
              " Food: Organic Salted Nut Mix, Protein: 17.86]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "metadata": {
        "id": "lCljI0HYRM-k",
        "colab_type": "code",
        "outputId": "07430fac-1d6b-41a9-e2b1-4c1990e81f11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "foods[0].__dict__\n",
        "type(foods[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "__main__.Food"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "esYhvVKHg-TM"
      },
      "cell_type": "markdown",
      "source": [
        "## 13.3 Sort in pandas"
      ]
    },
    {
      "metadata": {
        "id": "iQawn9MQJIph",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Sort by One Column:  Protein"
      ]
    },
    {
      "metadata": {
        "id": "wvmwiE7N5b2J",
        "colab_type": "code",
        "outputId": "3735e5c7-cd8b-41cf-f14a-5f0d00001cdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "df.sort_values(by=[\"carbohydrates_100g\"], ascending=False).head(5)"
      ],
      "execution_count": 0,
      "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>fat_100g</th>\n",
              "      <th>carbohydrates_100g</th>\n",
              "      <th>sugars_100g</th>\n",
              "      <th>proteins_100g</th>\n",
              "      <th>salt_100g</th>\n",
              "      <th>energy_100g</th>\n",
              "      <th>product</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42012</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>85.71</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>Spongebob Squarepants Valentine Candy Card Kit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31827</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>80.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>Marvel Avengers Assemble, Classroom Candy Mail...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31661</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>White Crystal Sugar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31665</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.254</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>Dried Habanero Chiles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42366</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>88.89</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>Iced Tea Mix, Lemon</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fat_100g  carbohydrates_100g  sugars_100g  proteins_100g  salt_100g  \\\n",
              "42012       0.0               100.0        85.71            0.0      0.000   \n",
              "31827       0.0               100.0        80.00            0.0      0.000   \n",
              "31661       0.0               100.0       100.00            0.0      0.000   \n",
              "31665       0.0               100.0         0.00            0.0      0.254   \n",
              "42366       0.0               100.0        88.89            0.0      0.000   \n",
              "\n",
              "       energy_100g                                            product  \n",
              "42012       1700.0     Spongebob Squarepants Valentine Candy Card Kit  \n",
              "31827       1700.0  Marvel Avengers Assemble, Classroom Candy Mail...  \n",
              "31661       1700.0                                White Crystal Sugar  \n",
              "31665       1700.0                              Dried Habanero Chiles  \n",
              "42366       1700.0                                Iced Tea Mix, Lemon  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "metadata": {
        "id": "bq_K-nnPWQcA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Sort by Two Columns:  Sugar, Salt"
      ]
    },
    {
      "metadata": {
        "id": "iI2ogrIvKYIm",
        "colab_type": "code",
        "outputId": "c789bc74-8b9e-4f5c-c9b3-98690ab6deec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "cell_type": "code",
      "source": [
        "df.sort_values(by=[\"fat_100g\", \"salt_100g\"], ascending=[False, False]).head(10)"
      ],
      "execution_count": 0,
      "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>fat_100g</th>\n",
              "      <th>carbohydrates_100g</th>\n",
              "      <th>sugars_100g</th>\n",
              "      <th>proteins_100g</th>\n",
              "      <th>salt_100g</th>\n",
              "      <th>energy_100g</th>\n",
              "      <th>product</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8390</th>\n",
              "      <td>100.0</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.524</td>\n",
              "      <td>4240.00</td>\n",
              "      <td>Horseradish Sauce</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44709</th>\n",
              "      <td>100.0</td>\n",
              "      <td>17.86</td>\n",
              "      <td>3.57</td>\n",
              "      <td>10.71</td>\n",
              "      <td>0.381</td>\n",
              "      <td>4385.69</td>\n",
              "      <td>Roasted Pecans</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3900.00</td>\n",
              "      <td>Ventura, Soybean - Peanut Frying Oil Blend</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5122</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3900.00</td>\n",
              "      <td>Corn Oil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5123</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3900.00</td>\n",
              "      <td>Canola Oil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5124</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3900.00</td>\n",
              "      <td>Vegetable Oil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5125</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3900.00</td>\n",
              "      <td>Vegetable Shortening</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5671</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3900.00</td>\n",
              "      <td>Organic Coconut Oil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5797</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3900.00</td>\n",
              "      <td>Premium Sesame Oil (100% Pure)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5798</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3900.00</td>\n",
              "      <td>Sesame Oil</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fat_100g  carbohydrates_100g  sugars_100g  proteins_100g  salt_100g  \\\n",
              "8390      100.0               20.00         0.00           0.00      1.524   \n",
              "44709     100.0               17.86         3.57          10.71      0.381   \n",
              "295       100.0                0.00         0.00           0.00      0.000   \n",
              "5122      100.0                0.00         0.00           0.00      0.000   \n",
              "5123      100.0                0.00         0.00           0.00      0.000   \n",
              "5124      100.0                0.00         0.00           0.00      0.000   \n",
              "5125      100.0                0.00         0.00           0.00      0.000   \n",
              "5671      100.0                0.00         0.00           0.00      0.000   \n",
              "5797      100.0                0.00         0.00           0.00      0.000   \n",
              "5798      100.0                0.00         0.00           0.00      0.000   \n",
              "\n",
              "       energy_100g                                     product  \n",
              "8390       4240.00                           Horseradish Sauce  \n",
              "44709      4385.69                              Roasted Pecans  \n",
              "295        3900.00  Ventura, Soybean - Peanut Frying Oil Blend  \n",
              "5122       3900.00                                    Corn Oil  \n",
              "5123       3900.00                                  Canola Oil  \n",
              "5124       3900.00                               Vegetable Oil  \n",
              "5125       3900.00                        Vegetable Shortening  \n",
              "5671       3900.00                         Organic Coconut Oil  \n",
              "5797       3900.00              Premium Sesame Oil (100% Pure)  \n",
              "5798       3900.00                                  Sesame Oil  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "metadata": {
        "id": "sxVe1CtO_FKr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Groupby"
      ]
    },
    {
      "metadata": {
        "id": "Ljcivk3_AW1v",
        "colab_type": "code",
        "outputId": "fd234a0f-0a04-4e04-ff40-7d84ce861626",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "cell_type": "code",
      "source": [
        "def high_protein(row):\n",
        "  \"\"\"Creates a high or low protein category\"\"\"\n",
        "  \n",
        "  if row > 80:\n",
        "    return \"high_protein\"\n",
        "  return \"low_protein\"\n",
        "\n",
        "df[\"high_protein\"] = df[\"proteins_100g\"].apply(high_protein)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "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>fat_100g</th>\n",
              "      <th>carbohydrates_100g</th>\n",
              "      <th>sugars_100g</th>\n",
              "      <th>proteins_100g</th>\n",
              "      <th>salt_100g</th>\n",
              "      <th>energy_100g</th>\n",
              "      <th>product</th>\n",
              "      <th>high_protein</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.57</td>\n",
              "      <td>64.29</td>\n",
              "      <td>14.29</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2267.85</td>\n",
              "      <td>Banana Chips Sweetened (Whole)</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>57.14</td>\n",
              "      <td>17.86</td>\n",
              "      <td>3.57</td>\n",
              "      <td>17.86</td>\n",
              "      <td>1.22428</td>\n",
              "      <td>2835.70</td>\n",
              "      <td>Organic Salted Nut Mix</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18.75</td>\n",
              "      <td>57.81</td>\n",
              "      <td>15.62</td>\n",
              "      <td>14.06</td>\n",
              "      <td>0.13970</td>\n",
              "      <td>1953.04</td>\n",
              "      <td>Organic Muesli</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.67</td>\n",
              "      <td>36.67</td>\n",
              "      <td>3.33</td>\n",
              "      <td>16.67</td>\n",
              "      <td>1.60782</td>\n",
              "      <td>2336.91</td>\n",
              "      <td>Zen Party Mix</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>18.18</td>\n",
              "      <td>60.00</td>\n",
              "      <td>21.82</td>\n",
              "      <td>14.55</td>\n",
              "      <td>0.02286</td>\n",
              "      <td>1976.37</td>\n",
              "      <td>Cinnamon Nut Granola</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fat_100g  carbohydrates_100g  sugars_100g  proteins_100g  salt_100g  \\\n",
              "0     28.57               64.29        14.29           3.57    0.00000   \n",
              "2     57.14               17.86         3.57          17.86    1.22428   \n",
              "3     18.75               57.81        15.62          14.06    0.13970   \n",
              "4     36.67               36.67         3.33          16.67    1.60782   \n",
              "5     18.18               60.00        21.82          14.55    0.02286   \n",
              "\n",
              "   energy_100g                         product high_protein  \n",
              "0      2267.85  Banana Chips Sweetened (Whole)  low_protein  \n",
              "2      2835.70          Organic Salted Nut Mix  low_protein  \n",
              "3      1953.04                  Organic Muesli  low_protein  \n",
              "4      2336.91                   Zen Party Mix  low_protein  \n",
              "5      1976.37            Cinnamon Nut Granola  low_protein  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "metadata": {
        "id": "bNwREeat_HLX",
        "colab_type": "code",
        "outputId": "dcf29a31-f78e-4945-c984-10b3bf877711",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "df.groupby(\"high_protein\").median()"
      ],
      "execution_count": 0,
      "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>fat_100g</th>\n",
              "      <th>carbohydrates_100g</th>\n",
              "      <th>sugars_100g</th>\n",
              "      <th>proteins_100g</th>\n",
              "      <th>salt_100g</th>\n",
              "      <th>energy_100g</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high_protein</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>high_protein</th>\n",
              "      <td>1.665</td>\n",
              "      <td>3.335</td>\n",
              "      <td>1.665</td>\n",
              "      <td>93.18</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>1700.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low_protein</th>\n",
              "      <td>3.170</td>\n",
              "      <td>22.390</td>\n",
              "      <td>5.880</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.6350</td>\n",
              "      <td>1121.54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              fat_100g  carbohydrates_100g  sugars_100g  proteins_100g  \\\n",
              "high_protein                                                             \n",
              "high_protein     1.665               3.335        1.665          93.18   \n",
              "low_protein      3.170              22.390        5.880           4.00   \n",
              "\n",
              "              salt_100g  energy_100g  \n",
              "high_protein                          \n",
              "high_protein     0.5207      1700.00  \n",
              "low_protein      0.6350      1121.54  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "metadata": {
        "id": "dpEhkJnxTtUb",
        "colab_type": "code",
        "outputId": "e96bb739-6fc3-45b7-db55-d09cef972c96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "cell_type": "code",
      "source": [
        "df.groupby(\"high_protein\").describe()"
      ],
      "execution_count": 0,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">carbohydrates_100g</th>\n",
              "      <th colspan=\"2\" halign=\"left\">energy_100g</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"2\" halign=\"left\">salt_100g</th>\n",
              "      <th colspan=\"8\" halign=\"left\">sugars_100g</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>...</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high_protein</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>high_protein</th>\n",
              "      <td>4.0</td>\n",
              "      <td>7.350000</td>\n",
              "      <td>10.724610</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.335</td>\n",
              "      <td>10.685</td>\n",
              "      <td>22.73</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1795.09500</td>\n",
              "      <td>...</td>\n",
              "      <td>4.203065</td>\n",
              "      <td>14.77772</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.242500</td>\n",
              "      <td>6.458671</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.665</td>\n",
              "      <td>5.9075</td>\n",
              "      <td>13.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low_protein</th>\n",
              "      <td>45022.0</td>\n",
              "      <td>34.056436</td>\n",
              "      <td>29.557504</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.44</td>\n",
              "      <td>22.390</td>\n",
              "      <td>61.540</td>\n",
              "      <td>100.00</td>\n",
              "      <td>45022.0</td>\n",
              "      <td>1111.22544</td>\n",
              "      <td>...</td>\n",
              "      <td>1.440180</td>\n",
              "      <td>2032.00000</td>\n",
              "      <td>45022.0</td>\n",
              "      <td>16.006122</td>\n",
              "      <td>21.496335</td>\n",
              "      <td>-1.2</td>\n",
              "      <td>1.57</td>\n",
              "      <td>5.880</td>\n",
              "      <td>23.0800</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 48 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             carbohydrates_100g                                           \\\n",
              "                          count       mean        std  min   25%     50%   \n",
              "high_protein                                                               \n",
              "high_protein                4.0   7.350000  10.724610  0.0  0.00   3.335   \n",
              "low_protein             45022.0  34.056436  29.557504  0.0  7.44  22.390   \n",
              "\n",
              "                             energy_100g               ...   salt_100g  \\\n",
              "                 75%     max       count        mean   ...         75%   \n",
              "high_protein                                           ...               \n",
              "high_protein  10.685   22.73         4.0  1795.09500   ...    4.203065   \n",
              "low_protein   61.540  100.00     45022.0  1111.22544   ...    1.440180   \n",
              "\n",
              "                         sugars_100g                                          \\\n",
              "                     max       count       mean        std  min   25%    50%   \n",
              "high_protein                                                                   \n",
              "high_protein    14.77772         4.0   4.242500   6.458671  0.0  0.00  1.665   \n",
              "low_protein   2032.00000     45022.0  16.006122  21.496335 -1.2  1.57  5.880   \n",
              "\n",
              "                               \n",
              "                  75%     max  \n",
              "high_protein                   \n",
              "high_protein   5.9075   13.64  \n",
              "low_protein   23.0800  100.00  \n",
              "\n",
              "[2 rows x 48 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    }
  ]
}