Lesson8_Python_For_Data_Science_Functions.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lesson8-Python For Data Science-Functions.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "chwCKrPTeu0V",
        "9MMO5aLZe8GC",
        "HO4Gzz0Oje08",
        "heQ5djh0fppR",
        "dL-yPSgtfwHc",
        "jZZPzN9ofzqE",
        "k67ErjU7qkVU",
        "evHbw7C8f2y0",
        "ZrldedS-sFUS",
        "loWnoGY8OPLA",
        "RHsiAy8N3-Cj",
        "AMPU657F4JWF",
        "3Srgz3C65Vdk",
        "9KQ2Cmcelcy-",
        "qO-uoYUClvqr"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "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/Lesson8_Python_For_Data_Science_Functions.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 8: Functions"
      ]
    },
    {
      "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": "chwCKrPTeu0V",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 8.1 Write and use functions"
      ]
    },
    {
      "metadata": {
        "id": "9MMO5aLZe8GC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Building blocks of distributed computing"
      ]
    },
    {
      "metadata": {
        "id": "xcU--BqPgQKN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def work(input):\n",
        "  \"\"\"Processes input and returns output\"\"\"\n",
        "  \n",
        "  output = input + 1\n",
        "  return output\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_yUpu-I9hhdf",
        "colab_type": "code",
        "outputId": "4b185ca1-8462-4092-cfb8-714006aafc5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "work(1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "Kocgo6ZijVpi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Key Components of Functions"
      ]
    },
    {
      "metadata": {
        "id": "HO4Gzz0Oje08",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Docstrings"
      ]
    },
    {
      "metadata": {
        "id": "InpDV6kejca8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def docstring():\n",
        "  \"\"\"Triple Quoted documentation!\"\"\"\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hM_gK54njn7r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "docstring?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BdZ6O3lkkUkp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Arguments:  Keyword and Positional"
      ]
    },
    {
      "metadata": {
        "id": "n0ha-pEKmE8Q",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* *Positional*:  Order based processing\n",
        "* *Keyword*:  Key/Value processing\n"
      ]
    },
    {
      "metadata": {
        "id": "RpK99_2BpmgX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Positional"
      ]
    },
    {
      "metadata": {
        "id": "YJvR7-o7kaP3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def positional(first,second,third):\n",
        "  \"\"\"Processes arguments to function in order\"\"\"\n",
        "  \n",
        "  print(f\"Processed first {first}\")\n",
        "  print(f\"Processed second {second}\")\n",
        "  print(f\"Processed third {third}\")\n",
        "  \n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9HJAF-v8lxAk",
        "colab_type": "code",
        "outputId": "6d56644d-31c7-4b80-d0a3-4c8ce4a33621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "positional(1, 2, 3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processed first 1\n",
            "Processed second 2\n",
            "Processed third 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XUghQwcSl69Q",
        "colab_type": "code",
        "outputId": "9b4d2886-af03-4944-e3a7-f899a0736445",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "positional(2, 3, 1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processed first 2\n",
            "Processed second 3\n",
            "Processed third 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5gxfaOIIppTy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Keyword"
      ]
    },
    {
      "metadata": {
        "id": "z8Ud2enHpQiW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def keyword(first=1, second=2, third=3):\n",
        "  \"\"\"Processed in any order\"\"\"\n",
        "  \n",
        "  print(f\"Processed first {first}\")\n",
        "  print(f\"Processed second {second}\")\n",
        "  print(f\"Processed third {third}\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Kf0afT68piRI",
        "colab_type": "code",
        "outputId": "a2d9c475-2b70-400d-807c-c6b827c66df8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "keyword(1,2,3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processed first 1\n",
            "Processed second 2\n",
            "Processed third 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BZEQ6NagqAMK",
        "colab_type": "code",
        "outputId": "cf83e515-0f3a-4a58-b780-9480c62f1edb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "keyword(second=2, third=3, first=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processed first 1\n",
            "Processed second 2\n",
            "Processed third 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Cw4QflPLqgOs",
        "colab_type": "code",
        "outputId": "6e1de89f-3aad-4919-e231-4ce0d5a28d1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "keyword(second=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processed first 1\n",
            "Processed second 2\n",
            "Processed third 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "t292FxCKri6_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Return"
      ]
    },
    {
      "metadata": {
        "id": "JZpCuThdsehI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Default is None"
      ]
    },
    {
      "metadata": {
        "id": "-lmlSwRHroMM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def bridge_to_nowhere():pass\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FeW19iLCsXNa",
        "colab_type": "code",
        "outputId": "189e59fb-d84a-40e2-e32a-d1c1a8993c45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "bridge_to_nowhere() == None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "6E-n3TktN9vP",
        "colab_type": "code",
        "outputId": "1cbe907e-e5d3-4c8d-f2e5-15de311c749b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "type(bridge_to_nowhere())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NoneType"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "kYqhX30VsxY6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Most useful functions return something"
      ]
    },
    {
      "metadata": {
        "id": "1mOKT9nOsg0y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def more_than_zero():\n",
        "  \n",
        "  return 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CwMszHHpsok6",
        "colab_type": "code",
        "outputId": "0dfc577d-68e6-4c2c-a98d-ad5e08602753",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "more_than_zero() == 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "-RsSF-E3s1g2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Functions can return functions"
      ]
    },
    {
      "metadata": {
        "id": "yG9l-n73s5VA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def inner_peace():\n",
        "  \"\"\"A deep function\"\"\"\n",
        "  \n",
        "  def peace():\n",
        "    return \"piece\"\n",
        "  \n",
        "  return peace"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RGbMCsu6ta57",
        "colab_type": "code",
        "outputId": "759e51d0-382f-47f7-9a80-5cfa52cb4b3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "inner = inner_peace()\n",
        "print(f\"Hey, I need that {inner()}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hey, I need that piece\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dkITkJLtOVg-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "inner2 = inner_peace()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Mx01RIPWObVb",
        "colab_type": "code",
        "outputId": "dc4947a7-b9ce-4e6a-9930-12337f37f54e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "type(inner2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "function"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "heQ5djh0fppR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 8.2 Write and use decorators"
      ]
    },
    {
      "metadata": {
        "id": "4rtpT5VKvS-E",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Using Decorators"
      ]
    },
    {
      "metadata": {
        "id": "Unf0PyHlzvl3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Very common to use for dispatching a function via:\n",
        "\n",
        "\n",
        "*   Command-line tools\n",
        "*   Web Routes\n",
        "*   Speeding up Python code\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "IhJ4ugKaznwL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Command-line Tools"
      ]
    },
    {
      "metadata": {
        "id": "8fH6Sw5gvZPU",
        "colab_type": "code",
        "outputId": "1ed57d27-ec3b-441f-d176-54e97353c3c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "%%python\n",
        "import click\n",
        "\n",
        "def less_than_zero():\n",
        "  \n",
        "  return {\"iron_man\": -1}\n",
        "\n",
        "@click.command()\n",
        "def run():\n",
        "  \n",
        "  rdj = less_than_zero()\n",
        "  click.echo(f\"Robert Downey Junior is versatile {rdj}\")\n",
        "  \n",
        "if __name__== \"__main__\":\n",
        "  run()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Robert Downey Junior is versatile {'iron_man': -1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YZBqLiy_1-9r",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Web App"
      ]
    },
    {
      "metadata": {
        "id": "cryR4v0lzZnn",
        "colab_type": "code",
        "outputId": "83214037-31be-4c94-c70b-4f8b1736f9ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "%%writefile run.py\n",
        "from flask import Flask\n",
        "app = Flask(__name__)\n",
        "\n",
        "def less_than_zero():\n",
        "  \n",
        "  return {\"iron_man\": -1}\n",
        "\n",
        "@app.route('/')\n",
        "def runit():\n",
        "  return less_than_zero()\n",
        "  \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting run.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_Qela4-QPUy6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "curl localhost:5000/ {'iron_man': -1}\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "e4j-RCLj7qVa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Using Numba"
      ]
    },
    {
      "metadata": {
        "id": "zyVxYYr8dPZ9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Using numba Just in Time Compiler (JIT) can dramatically speed up code"
      ]
    },
    {
      "metadata": {
        "id": "XpHg_ZXm_VFs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def crunchy_normal():\n",
        "  count = 0\n",
        "  num = 10000000\n",
        "  for i in range(num):\n",
        "    count += num  \n",
        "  return count"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KGdobU-Z_al4",
        "colab_type": "code",
        "outputId": "39da257e-6e9e-42b6-bd5c-2950d53d4308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "%%time\n",
        "crunchy_normal()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 906 ms, sys: 581 µs, total: 907 ms\n",
            "Wall time: 908 ms\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000000000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "Ml9C38hhzixJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from numba import jit\n",
        "\n",
        "@jit(nopython=True)\n",
        "def crunchy():\n",
        "  count = 0\n",
        "  num = 10000000\n",
        "  for i in range(num):\n",
        "    count += num  \n",
        "  return count"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Vi8MvQH-8b2F",
        "colab_type": "code",
        "outputId": "1bc1d290-934f-4bc5-9224-992323a35d55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "%%time\n",
        "crunchy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 113 ms, sys: 15.9 ms, total: 129 ms\n",
            "Wall time: 194 ms\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000000000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "cvnTUxZYvfKR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Writing Decorators"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "AxG-DkX2gOA_"
      },
      "cell_type": "markdown",
      "source": [
        "#### Instrumentation Decorator"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Lu_1OjESgOBA"
      },
      "cell_type": "markdown",
      "source": [
        "Using a decorator to time, debug or instrument code is very common"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "416ZT8wsgOBB",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from functools import wraps\n",
        "import time\n",
        "\n",
        "def instrument(f):\n",
        "    @wraps(f)\n",
        "    def wrap(*args, **kw):\n",
        "        ts = time.time()\n",
        "        result = f(*args, **kw)\n",
        "        te = time.time()\n",
        "        print(f\"function: {f.__name__}, args: [{args}, {kw}] took: {te-ts} sec\")\n",
        "        return result\n",
        "    return wrap"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "spX1jAORgOBD"
      },
      "cell_type": "markdown",
      "source": [
        "Using decorator to time execution of a function"
      ]
    },
    {
      "metadata": {
        "id": "nhV7RoBLxGdY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from time import sleep\n",
        "\n",
        "@instrument\n",
        "def simulated_work(count, task):\n",
        "  \"\"\"simulates work\"\"\"\n",
        "  \n",
        "  print(\"Starting work\")\n",
        "  sleep(count)\n",
        "  processed = f\"one {task} leap\"\n",
        "  return processed\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "83e6646e-6307-425e-83cb-cfe901895aed",
        "id": "nEeQPk_sgOBD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "simulated_work(3, task=\"small\")  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting work\n",
            "function: simulated_work, args: [(3,), {'task': 'small'}] took: 3.0027008056640625 sec\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'one small leap'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "LjmVGMm0fthG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 8.3 Compose closure functions"
      ]
    },
    {
      "metadata": {
        "id": "dL-yPSgtfwHc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Functions with state"
      ]
    },
    {
      "metadata": {
        "id": "yIKSkPO8vI9Y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def calorie_counter():\n",
        "    \"\"\"Counts calories\"\"\"\n",
        "    \n",
        "    protein = 0\n",
        "    fat = 0\n",
        "    carbohydrate = 0\n",
        "    total = 0\n",
        "    def calorie_counter_inner(food):\n",
        "        nonlocal protein\n",
        "        nonlocal fat\n",
        "        nonlocal carbohydrate\n",
        "        if food == \"protein\":\n",
        "          protein += 4\n",
        "        elif food == \"carbohydrate\":\n",
        "          carbohydrate += 4\n",
        "        elif food == \"fat\":\n",
        "          fat += 9\n",
        "        total = protein + carbohydrate + fat\n",
        "        print(f\"Consumed {total} calories of protein: {protein}, carbohydrate: {carbohydrate}, fat: {fat}\")\n",
        "    return calorie_counter_inner"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rhDStaUJxfG0",
        "colab_type": "code",
        "outputId": "bd6db3c5-7a7e-480b-906b-3c288923df63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "meal = calorie_counter()\n",
        "type(meal)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "function"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "A_qbn0kfxsuv",
        "colab_type": "code",
        "outputId": "6d3e1779-6f81-48b3-8128-61a9a4169ac3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "meal(\"carbohydrate\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Consumed 4 calories of protein: 0, carbohydrate: 4, fat: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_rswW-D-xtth",
        "colab_type": "code",
        "outputId": "a896f942-ca6d-4034-f7d4-acc3d0aadaa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "meal(\"fat\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Consumed 13 calories of protein: 0, carbohydrate: 4, fat: 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OVZZFhewx41w",
        "colab_type": "code",
        "outputId": "25eeca4a-f07b-49f5-ec49-f44932725ee5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "meal(\"protein\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Consumed 17 calories of protein: 4, carbohydrate: 4, fat: 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Xox6IlosfwnH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 8.4 Use lambda"
      ]
    },
    {
      "metadata": {
        "id": "jZZPzN9ofzqE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### YAGNI\n"
      ]
    },
    {
      "metadata": {
        "id": "ZQl421JrpKJK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Y**ou **A**in't **G**onna **N**eed** I**t\n"
      ]
    },
    {
      "metadata": {
        "id": "y0lfEYGTR7Eo",
        "colab_type": "code",
        "outputId": "604ec6e7-0613-4019-fe8c-01c3035970a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "import this"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Zen of Python, by Tim Peters\n",
            "\n",
            "Beautiful is better than ugly.\n",
            "Explicit is better than implicit.\n",
            "Simple is better than complex.\n",
            "Complex is better than complicated.\n",
            "Flat is better than nested.\n",
            "Sparse is better than dense.\n",
            "Readability counts.\n",
            "Special cases aren't special enough to break the rules.\n",
            "Although practicality beats purity.\n",
            "Errors should never pass silently.\n",
            "Unless explicitly silenced.\n",
            "In the face of ambiguity, refuse the temptation to guess.\n",
            "There should be one-- and preferably only one --obvious way to do it.\n",
            "Although that way may not be obvious at first unless you're Dutch.\n",
            "Now is better than never.\n",
            "Although never is often better than *right* now.\n",
            "If the implementation is hard to explain, it's a bad idea.\n",
            "If the implementation is easy to explain, it may be a good idea.\n",
            "Namespaces are one honking great idea -- let's do more of those!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "5885a57c-da47-42b4-e4a7-96d1923dfab0",
        "id": "dc1YjODApmo3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "func = lambda x: x**x\n",
        "func(4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "256"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "f4cc5b17-6bfd-4172-da43-f800bd265086",
        "id": "NcsUPsvYpmo6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "def expo(x):\n",
        "  return x**x\n",
        "\n",
        "expo(4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "256"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "k67ErjU7qkVU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Close Encounters with Lambdas"
      ]
    },
    {
      "metadata": {
        "id": "aShV74ZSrZsm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Used in series or DataFrame "
      ]
    },
    {
      "metadata": {
        "id": "7hzx6YXMqsAr",
        "colab_type": "code",
        "outputId": "d88ec4ed-7f82-477b-df3c-0e19a23dd762",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "series = pd.Series([1, 5, 10])\n",
        "series.apply(lambda x: x**x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              1\n",
              "1           3125\n",
              "2    10000000000\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "CYeSRDx2SSdE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def expo(x):\n",
        "  return x**x\n",
        "\n",
        "expo(4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GUlDqiwGSOtK",
        "colab_type": "code",
        "outputId": "0b0c533d-3c6c-4bf2-ecf6-67944a5832f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "series = pd.Series([1, 5, 10])\n",
        "series.apply(expo)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              1\n",
              "1           3125\n",
              "2    10000000000\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "metadata": {
        "id": "JiNag1pMfz2E",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 8.5 Advanced Use of Functions"
      ]
    },
    {
      "metadata": {
        "id": "evHbw7C8f2y0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Applying a Function to a Pandas DataFrame"
      ]
    },
    {
      "metadata": {
        "id": "RXxygaSEeFqn",
        "colab_type": "code",
        "outputId": "c563ff1b-1729-4e72-ed02-073eebcf784a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "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.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>reconstructed_energy</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>1</th>\n",
              "      <td>17.86</td>\n",
              "      <td>60.71</td>\n",
              "      <td>17.86</td>\n",
              "      <td>17.86</td>\n",
              "      <td>0.63500</td>\n",
              "      <td>2032.23</td>\n",
              "      <td>Peanuts</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",
              "  </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",
              "1     17.86               60.71        17.86          17.86    0.63500   \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",
              "\n",
              "   reconstructed_energy                         product  \n",
              "0               2267.85  Banana Chips Sweetened (Whole)  \n",
              "1               2032.23                         Peanuts  \n",
              "2               2835.70          Organic Salted Nut Mix  \n",
              "3               1953.04                  Organic Muesli  \n",
              "4               2336.91                   Zen Party Mix  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "HYvmsSyBeU-6",
        "colab_type": "code",
        "colab": {}
      },
      "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\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H9Qhe8YtfM-t",
        "colab_type": "code",
        "outputId": "492f4580-5046-451c-c6c7-027754801915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "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>reconstructed_energy</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>1</th>\n",
              "      <td>17.86</td>\n",
              "      <td>60.71</td>\n",
              "      <td>17.86</td>\n",
              "      <td>17.86</td>\n",
              "      <td>0.63500</td>\n",
              "      <td>2032.23</td>\n",
              "      <td>Peanuts</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",
              "  </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",
              "1     17.86               60.71        17.86          17.86    0.63500   \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",
              "\n",
              "   reconstructed_energy                         product high_protein  \n",
              "0               2267.85  Banana Chips Sweetened (Whole)  low_protein  \n",
              "1               2032.23                         Peanuts  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  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "metadata": {
        "id": "1QSzM3o3Thyt",
        "colab_type": "code",
        "outputId": "c6bbcd19-322b-4991-aa78-03a37c4004e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "df.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 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>reconstructed_energy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>45028.000000</td>\n",
              "      <td>45028.000000</td>\n",
              "      <td>45028.000000</td>\n",
              "      <td>45028.000000</td>\n",
              "      <td>45028.000000</td>\n",
              "      <td>45028.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.765910</td>\n",
              "      <td>34.054788</td>\n",
              "      <td>16.005614</td>\n",
              "      <td>6.619437</td>\n",
              "      <td>1.469631</td>\n",
              "      <td>1111.332304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.930087</td>\n",
              "      <td>29.557017</td>\n",
              "      <td>21.495512</td>\n",
              "      <td>7.936770</td>\n",
              "      <td>12.794943</td>\n",
              "      <td>791.621634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.200000</td>\n",
              "      <td>-3.570000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.440000</td>\n",
              "      <td>1.570000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.063500</td>\n",
              "      <td>334.520000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.170000</td>\n",
              "      <td>22.390000</td>\n",
              "      <td>5.880000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.635000</td>\n",
              "      <td>1121.540000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>17.860000</td>\n",
              "      <td>61.540000</td>\n",
              "      <td>23.080000</td>\n",
              "      <td>9.520000</td>\n",
              "      <td>1.440180</td>\n",
              "      <td>1678.460000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2032.000000</td>\n",
              "      <td>4475.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           fat_100g  carbohydrates_100g   sugars_100g  proteins_100g  \\\n",
              "count  45028.000000        45028.000000  45028.000000   45028.000000   \n",
              "mean      10.765910           34.054788     16.005614       6.619437   \n",
              "std       14.930087           29.557017     21.495512       7.936770   \n",
              "min        0.000000            0.000000     -1.200000      -3.570000   \n",
              "25%        0.000000            7.440000      1.570000       0.000000   \n",
              "50%        3.170000           22.390000      5.880000       4.000000   \n",
              "75%       17.860000           61.540000     23.080000       9.520000   \n",
              "max      100.000000          100.000000    100.000000     100.000000   \n",
              "\n",
              "          salt_100g  reconstructed_energy  \n",
              "count  45028.000000          45028.000000  \n",
              "mean       1.469631           1111.332304  \n",
              "std       12.794943            791.621634  \n",
              "min        0.000000              0.000000  \n",
              "25%        0.063500            334.520000  \n",
              "50%        0.635000           1121.540000  \n",
              "75%        1.440180           1678.460000  \n",
              "max     2032.000000           4475.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "id": "ZrldedS-sFUS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Partial Functions"
      ]
    },
    {
      "metadata": {
        "id": "drjwAPlRsWLe",
        "colab_type": "code",
        "outputId": "a2abd261-507f-45ab-dbb7-75964acb05b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from functools import partial\n",
        "\n",
        "def multiple_sort(column_one, column_two):\n",
        "  \"\"\"Performs multiple sort on a pandas DataFrame\"\"\"\n",
        "  \n",
        "  sorted_df = df.sort_values(by=[column_one, column_two], \n",
        "                 ascending=[False, False])\n",
        "  return sorted_df\n",
        "  \n",
        "multisort = partial(multiple_sort, \"sugars_100g\")\n",
        "type(multisort)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "functools.partial"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "id": "ZFdOEsXMt-Yj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Find sugary and fatty food"
      ]
    },
    {
      "metadata": {
        "id": "NdmaOWEitzOj",
        "colab_type": "code",
        "outputId": "4aadd03a-6c25-47e2-8dfb-3e47b56ff492",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "df = multisort(\"fat_100g\")\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>reconstructed_energy</th>\n",
              "      <th>product</th>\n",
              "      <th>high_protein</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8254</th>\n",
              "      <td>25.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2675.00</td>\n",
              "      <td>Princess Mix Decorations</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8255</th>\n",
              "      <td>25.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2675.00</td>\n",
              "      <td>Frosted Mix</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8253</th>\n",
              "      <td>12.50</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2187.50</td>\n",
              "      <td>Holiday Happiness Mix</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9371</th>\n",
              "      <td>1.79</td>\n",
              "      <td>85.71</td>\n",
              "      <td>100.0</td>\n",
              "      <td>7.14</td>\n",
              "      <td>0.04572</td>\n",
              "      <td>1648.26</td>\n",
              "      <td>Organic Just Cherries</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>0.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1700.00</td>\n",
              "      <td>Tnt Exploding Candy</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",
              "8254     25.00              100.00        100.0           0.00    0.00000   \n",
              "8255     25.00              100.00        100.0           0.00    0.00000   \n",
              "8253     12.50              100.00        100.0           0.00    0.00000   \n",
              "9371      1.79               85.71        100.0           7.14    0.04572   \n",
              "222       0.00              100.00        100.0           0.00    0.00000   \n",
              "\n",
              "      reconstructed_energy                   product high_protein  \n",
              "8254               2675.00  Princess Mix Decorations  low_protein  \n",
              "8255               2675.00               Frosted Mix  low_protein  \n",
              "8253               2187.50     Holiday Happiness Mix  low_protein  \n",
              "9371               1648.26     Organic Just Cherries  low_protein  \n",
              "222                1700.00       Tnt Exploding Candy  low_protein  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "yV-V_lB_uCIe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Find sugary and salty food"
      ]
    },
    {
      "metadata": {
        "id": "cb7_LvMAuXnL",
        "colab_type": "code",
        "outputId": "175e1e94-4a85-4e65-ea4a-8524e238b068",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "df = multisort(\"salt_100g\")\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>reconstructed_energy</th>\n",
              "      <th>product</th>\n",
              "      <th>high_protein</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33151</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71.120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Turkey Brine Kit, Garlic &amp; Herb</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24783</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.130</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>Seasoning</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4073</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.620</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>Seasoning Rub, Sweet &amp; Spicy Seafood</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10282</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.540</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>Instant Pectin</td>\n",
              "      <td>low_protein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17880</th>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.635</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>Cranberry Cosmos Cocktail Rimming Sugar</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",
              "33151       0.0                 0.0        100.0            0.0     71.120   \n",
              "24783       0.0               100.0        100.0            0.0     24.130   \n",
              "4073        0.0               100.0        100.0            0.0      7.620   \n",
              "10282       0.0               100.0        100.0            0.0      2.540   \n",
              "17880       0.0               100.0        100.0            0.0      0.635   \n",
              "\n",
              "       reconstructed_energy                                  product  \\\n",
              "33151                   0.0          Turkey Brine Kit, Garlic & Herb   \n",
              "24783                1700.0                                Seasoning   \n",
              "4073                 1700.0     Seasoning Rub, Sweet & Spicy Seafood   \n",
              "10282                1700.0                           Instant Pectin   \n",
              "17880                1700.0  Cranberry Cosmos Cocktail Rimming Sugar   \n",
              "\n",
              "      high_protein  \n",
              "33151  low_protein  \n",
              "24783  low_protein  \n",
              "4073   low_protein  \n",
              "10282  low_protein  \n",
              "17880  low_protein  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    }
  ]
}