Public_Master_SafariOnline_Day1_Part1.ipynb

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    "colab": {
      "name": "Public-Master-SafariOnline-Day1-Part1.ipynb",
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/noahgift/functional_intro_to_python/blob/master/Public_Master_SafariOnline_Day1_Part1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4J2lIggJLUMS",
        "colab_type": "text"
      },
      "source": [
        "# **Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_Id55m6Jsbu",
        "colab_type": "text"
      },
      "source": [
        "## Pragmatic AI Labs\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5p96AqpSDZa",
        "colab_type": "text"
      },
      "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",
        "\n",
        "### Additional Related Topics from Noah Gift\n",
        "\n",
        "His most recent books are:\n",
        "\n",
        "*   [Pragmatic A.I.:   An introduction to Cloud-Based Machine Learning (Pearson, 2018)](https://www.amazon.com/Pragmatic-AI-Introduction-Cloud-Based-Analytics/dp/0134863860)\n",
        "*   [Python for DevOps (O'Reilly, 2020)](https://www.amazon.com/Python-DevOps-Ruthlessly-Effective-Automation/dp/149205769X). \n",
        "\n",
        "His most recent video courses are:\n",
        "\n",
        "*   [Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)](https://learning.oreilly.com/videos/essential-machine-learning/9780135261118)\n",
        "*   [AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)](https://learning.oreilly.com/videos/aws-certified-machine/9780135556597)\n",
        "*   [Python for Data Science Complete Video Course Video Training (Pearson, 2019)](https://learning.oreilly.com/videos/python-for-data/9780135687253)\n",
        "*   [AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)](https://learning.oreilly.com/videos/aws-certified-big/9780135772324)\n",
        "*   [Building A.I. Applications on Google Cloud Platform (Pearson, 2019)](https://learning.oreilly.com/videos/building-ai-applications/9780135973462)\n",
        "*   [Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)](https://learning.oreilly.com/videos/pragmatic-ai-and/9780136554714)\n",
        "*   [Data Engineering with Python and AWS Lambda (Pearson, 2019)](https://learning.oreilly.com/videos/data-engineering-with/9780135964330)\n",
        "\n",
        "His most recent online courses are:\n",
        "\n",
        "*   [Microservices with this Udacity DevOps Nanodegree (Udacity, 2019)](https://www.udacity.com/course/cloud-dev-ops-nanodegree--nd9991)\n",
        "*   [Command Line Automation in Python (DataCamp, 2019)](https://www.datacamp.com/instructors/ndgift)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWKUOfEBUMLS",
        "colab_type": "text"
      },
      "source": [
        "## Part 1.1: Introductory Concepts in Python, Jupyter and Colab\n",
        "\n",
        "* [Read related material covered in Chapter 1 of Pragmatic AI](https://www.safaribooksonline.com/library/view/pragmatic-ai-an/9780134863924/ch01.xhtml#ch01)\n",
        "\n",
        "* [Watch video section:  Lesson 1:  Introducing Data Science Coding with Python Fundamentals](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118/9780135261118-EMLA_01_01_00)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nP_6tcHM7yFm",
        "colab_type": "text"
      },
      "source": [
        "### Using IPython, Jupyter, Colab and Python executable\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7JKyOrPkITH",
        "colab_type": "text"
      },
      "source": [
        "#### Using IPython\n",
        "\n",
        "Very similar to Jupyter, but run from terminal:\n",
        "\n",
        "*   IPython predates Jupyter\n",
        "*   Both Jupyter and IPython accept *!ls -l* format to execute shell commands\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YR3ZddpWM-uh",
        "colab_type": "code",
        "outputId": "b5d41cf4-1e89-44fd-d46e-aa5b8efbadc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!ls -l"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 4\n",
            "drwxr-xr-x 1 root root 4096 Nov 21 16:30 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RETmEcecyCVf",
        "colab_type": "code",
        "outputId": "c550da6e-d934-4336-aa0e-da279950ab05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "var = !ls -l\n",
        "type(var)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IPython.utils.text.SList"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQMhyAm8sxSc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#var.fields?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGSSYqVOyJEP",
        "colab_type": "code",
        "outputId": "6cda1772-4401-4d13-90e8-be5bf1c24951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "var.grep(\"data\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drwxr-xr-x 1 root root 4096 Nov 21 16:30 sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pci2aotqPIME",
        "colab_type": "text"
      },
      "source": [
        "#### Jupyter Notebook\n",
        "\n",
        "\n",
        "Many flavors of Jupyter Notebook.  A few popular ones:\n",
        "\n",
        "* ![Jupyter](https://user-images.githubusercontent.com/58792/40282633-395be25c-5c27-11e8-9e40-357ea4216562.png)\n",
        "* ![JupyterHug](https://user-images.githubusercontent.com/58792/40282632-387efe32-5c27-11e8-9f02-6f95f2fee223.png)\n",
        "* ![Colab](https://user-images.githubusercontent.com/58792/40282631-384f8cf6-5c27-11e8-9209-3f0d22de0d81.png)\n",
        "* ![Kaggle](https://user-images.githubusercontent.com/58792/40282634-3985c3a6-5c27-11e8-8c53-06fbdedce847.png)\n",
        "* ![Sagemaker](https://user-images.githubusercontent.com/58792/40282635-39a3bdd4-5c27-11e8-81d5-6533a3b84771.png)\n",
        "\n",
        "#### Hosted Commercial Flavors\n",
        "\n",
        "* [Google Colaboratory](https://colab.research.google.com/notebook):  Free\n",
        "* [Kaggle](https://www.kaggle.com/):  Free\n",
        "\n",
        "\n",
        "#### Pure Open Source\n",
        "\n",
        "* [Jupyter](http://jupyter.org/) standalone, original\n",
        "* [JupyterHub](https://github.com/jupyterhub/jupyterhub) multi-user, docker friendly\n",
        "\n",
        "#### Hybrid Solutions\n",
        "\n",
        "* Running Jupyter on [AWS Spot Instances](https://aws.amazon.com/ec2/spot/)\n",
        "* [Google Data Lab](https://cloud.google.com/datalab/)\n",
        "* [Azure Data Science Virtual Machines](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/)\n",
        "* [AWS Sagemaker](https://aws.amazon.com/sagemaker/)\n",
        "* [Azure Machine Learning Studio](https://studio.azureml.net/)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lh4tsf3-p-lw",
        "colab_type": "text"
      },
      "source": [
        "#### Colab Notebook Key Features\n",
        "\n",
        "* [12.1 Perform Colaboratory basics ](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118/9780135261118-EMLA_01_12_01)\n",
        "\n",
        "* [12.2 Use Advanced Colab Features](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118/9780135261118-EMLA_01_12_02)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-xv1E3YqDK4",
        "colab_type": "text"
      },
      "source": [
        "*  Can enable both TPU and GPU runtimes\n",
        "*  Can upload regular Jupyter Notebooks into colab\n",
        "*  Can have a Google Drive Full of Colab Notebooks\n",
        "*  Can sync colab notebooks to Github.  Here is an example of a [gist of this notebook](https://gist.github.com/noahgift/c69200e05c057cf239fc7ea0be62e043)\n",
        "*  Can connect to a [local runtime](https://research.google.com/colaboratory/local-runtimes.html)\n",
        "* Can create [forms in Colab](https://colab.research.google.com/notebooks/forms.ipynb)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPWO_zyRopXN",
        "colab_type": "text"
      },
      "source": [
        "##### Mount GDrive\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XI73HZNLobp4",
        "colab_type": "code",
        "outputId": "f9810fa8-fc58-40ac-e9c3-61d47e6b1dd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNyzZwgmoxwm",
        "colab_type": "code",
        "outputId": "7a0d2711-c2ad-457e-ae40-86ec8b7dc706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os;os.listdir(\"/content/gdrive/My Drive/awsml\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['kaggle.json', 'credentials', 'config', 'cloudai-7ab42a4d8a43.json']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jX9349SpLbr",
        "colab_type": "text"
      },
      "source": [
        "#### Magic Commands"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zf35CYV9PrjS",
        "colab_type": "text"
      },
      "source": [
        "##### %timeit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMpMJH0rfAEg",
        "colab_type": "code",
        "outputId": "07047e16-3143-4cfb-dfcc-6993b53ddc54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "too_many_decimals = 1.912345897\n",
        "\n",
        "print(\"built in Python Round\")\n",
        "%timeit round(too_many_decimals, 2)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "built in Python Round\n",
            "The slowest run took 28.69 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1000000 loops, best of 3: 468 ns per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_KLutZKq5yN",
        "colab_type": "text"
      },
      "source": [
        "##### %alias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyIZLH6UpMou",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "alias lscsv ls -l sample_data/*.csv "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNJ6bKtGpVn3",
        "colab_type": "code",
        "outputId": "2307a6a0-dbb4-4d4f-e622-6aebf457c46c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "lscsv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root   301141 Oct 25 16:58 sample_data/california_housing_test.csv\n",
            "-rw-r--r-- 1 root root  1706430 Oct 25 16:58 sample_data/california_housing_train.csv\n",
            "-rw-r--r-- 1 root root 18289443 Oct 25 16:58 sample_data/mnist_test.csv\n",
            "-rw-r--r-- 1 root root 36523880 Oct 25 16:58 sample_data/mnist_train_small.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AE1cfp41rcwL",
        "colab_type": "text"
      },
      "source": [
        "[Reference These]\n",
        "https://ipython.readthedocs.io/en/stable/interactive/magics.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnsQIiDurIPp",
        "colab_type": "text"
      },
      "source": [
        "##### %who"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ljy17rdjrWD9",
        "colab_type": "text"
      },
      "source": [
        "Print variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUFS07KpPfE6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "var1=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACyoq3IsrNZV",
        "colab_type": "code",
        "outputId": "a4d09d85-6295-46b9-ed74-c74e156a45ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "who"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive\t os\t too_many_decimals\t var\t var1\t \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MURtyzAMrZ1Z",
        "colab_type": "code",
        "outputId": "25224048-22c8-4df3-b579-924a09f674d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "too_many_decimals"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.912345897"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9z-cAQow-BO",
        "colab_type": "text"
      },
      "source": [
        "##### %writefile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luIP2qKqxJmB",
        "colab_type": "code",
        "outputId": "33d40f6c-f110-4d99-ad95-e422ea1c7fe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%%writefile magic_stuff.py\n",
        "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 = df.drop(df.index[[1,11877]]) #drop outlier\n",
        "df.rename(index=str, columns={\"reconstructed_energy\": \"energy_100g\"}, inplace=True)\n",
        "print(df.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing magic_stuff.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uok6ppvvxRo9",
        "colab_type": "code",
        "outputId": "13ef744b-8830-4be8-bda9-1fd4dcf6dc47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        }
      },
      "source": [
        "cat magic_stuff.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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 = df.drop(df.index[[1,11877]]) #drop outlier\n",
            "df.rename(index=str, columns={\"reconstructed_energy\": \"energy_100g\"}, inplace=True)\n",
            "print(df.head())"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STjEo5XFPxM0",
        "colab_type": "code",
        "outputId": "702f080e-e89a-4443-ddeb-af0109a7fcaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        }
      },
      "source": [
        "!python magic_stuff.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   fat_100g  ...                         product\n",
            "0     28.57  ...  Banana Chips Sweetened (Whole)\n",
            "2     57.14  ...          Organic Salted Nut Mix\n",
            "3     18.75  ...                  Organic Muesli\n",
            "4     36.67  ...                   Zen Party Mix\n",
            "5     18.18  ...            Cinnamon Nut Granola\n",
            "\n",
            "[5 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jUCwijQ0_-t",
        "colab_type": "code",
        "outputId": "1afead35-1416-446f-97ee-b444aded21e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "!pip install -q pylint"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 307kB 3.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 5.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 215kB 46.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 737kB 41.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 7.4MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xf8MwJfy1FqQ",
        "colab_type": "code",
        "outputId": "d3c20211-3352-4109-b77a-4dfecb6a2a6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "!pylint magic_stuff.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "************* Module magic_stuff\n",
            "magic_stuff.py:3:0: C0301: Line too long (109/100) (line-too-long)\n",
            "magic_stuff.py:4:0: C0301: Line too long (110/100) (line-too-long)\n",
            "magic_stuff.py:5:24: C0326: Exactly one space required after comma\n",
            "df = df.drop(df.index[[1,11877]]) #drop outlier\n",
            "                        ^ (bad-whitespace)\n",
            "magic_stuff.py:7:0: C0304: Final newline missing (missing-final-newline)\n",
            "magic_stuff.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n",
            "magic_stuff.py:2:0: C0103: Constant name \"df\" doesn't conform to UPPER_CASE naming style (invalid-name)\n",
            "magic_stuff.py:5:0: C0103: Constant name \"df\" doesn't conform to UPPER_CASE naming style (invalid-name)\n",
            "\n",
            "------------------------------------\n",
            "Your code has been rated at -1.67/10\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtVDMB8EwJqG",
        "colab_type": "text"
      },
      "source": [
        "##### Bash"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YC03T7XUuF1k",
        "colab_type": "code",
        "outputId": "02522270-1f36-47aa-bb5f-f6fce6d2e5aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "%%bash\n",
        "uname -a\n",
        "ls\n",
        "ps"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linux bee9b4559c13 4.14.137+ #1 SMP Thu Aug 8 02:47:02 PDT 2019 x86_64 x86_64 x86_64 GNU/Linux\n",
            "gdrive\n",
            "magic_stuff.py\n",
            "sample_data\n",
            "    PID TTY          TIME CMD\n",
            "      1 ?        00:00:00 run.sh\n",
            "      9 ?        00:00:01 node\n",
            "     24 ?        00:00:03 jupyter-noteboo\n",
            "    119 ?        00:00:00 tail\n",
            "    127 ?        00:00:06 python3\n",
            "    298 ?        00:00:00 bash\n",
            "    299 ?        00:00:00 drive\n",
            "    300 ?        00:00:00 grep\n",
            "    383 ?        00:00:01 drive\n",
            "    394 ?        00:00:00 fusermount <defunct>\n",
            "    450 ?        00:00:00 bash\n",
            "    451 ?        00:00:00 tail\n",
            "    452 ?        00:00:00 grep\n",
            "    551 ?        00:00:00 bash\n",
            "    554 ?        00:00:00 ps\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HI8JuclMt0p2",
        "colab_type": "code",
        "outputId": "d8fcd487-28d4-4c83-bf10-394a66322620",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!uname -a"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linux bee9b4559c13 4.14.137+ #1 SMP Thu Aug 8 02:47:02 PDT 2019 x86_64 x86_64 x86_64 GNU/Linux\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohA_HAOewNMT",
        "colab_type": "text"
      },
      "source": [
        "##### Python2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eioZW4txwO7-",
        "colab_type": "code",
        "outputId": "bb8af4ac-d6b6-4313-f27e-1294dc1b3b39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%%python2\n",
        "print \"old school\"\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "old school\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2sW1aQ-wuPH",
        "colab_type": "text"
      },
      "source": [
        "##### HTML"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8ueccG-wv8Z",
        "colab_type": "code",
        "outputId": "be89511e-928c-4118-9e12-60f46bc23b9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "%%html\n",
        "<h1>Only The Best Tags and People</h>"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h1>Only The Best Tags and People</h>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lgxm56T_u38j",
        "colab_type": "text"
      },
      "source": [
        "##### Upload to Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXE_tpgYvr7p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install --upgrade -q gspread"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3OJaHBSvr69",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "import gspread\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "gc = gspread.authorize(GoogleCredentials.get_application_default())\n",
        "\n",
        "worksheet = gc.open('Your spreadsheet name').sheet1\n",
        "\n",
        "# get_all_values gives a list of rows.\n",
        "rows = worksheet.get_all_values()\n",
        "print(rows)\n",
        "\n",
        "# Convert to a DataFrame and render.\n",
        "import pandas as pd\n",
        "pd.DataFrame.from_records(rows)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3IMbpwPE2Aeb",
        "colab_type": "code",
        "outputId": "983acd82-4e89-4855-8953-5c2d8f0b8416",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "# load an example dataset\n",
        "from vega_datasets import data\n",
        "cars = data.cars()\n",
        "\n",
        "# plot the dataset, referencing dataframe column names\n",
        "import altair as alt\n",
        "alt.Chart(cars).mark_bar().encode(\n",
        "  x='mean(Miles_per_Gallon)',\n",
        "  y='Origin',\n",
        "  color='Origin'\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alt.Chart(...)"
            ],
            "text/html": [
              "<!DOCTYPE html>\n",
              "<html>\n",
              "<head>\n",
              "  <style>\n",
              "    .vega-actions a {\n",
              "        margin-right: 12px;\n",
              "        color: #757575;\n",
              "        font-weight: normal;\n",
              "        font-size: 13px;\n",
              "    }\n",
              "    .error {\n",
              "        color: red;\n",
              "    }\n",
              "  </style>\n",
              "  <script type=\"text/javascript\" src=\"https://cdn.jsdelivr.net/npm//vega@5\"></script>\n",
              "  <script type=\"text/javascript\" src=\"https://cdn.jsdelivr.net/npm//vega-lite@3.4.0\"></script>\n",
              "  <script type=\"text/javascript\" src=\"https://cdn.jsdelivr.net/npm//vega-embed@4\"></script>\n",
              "</head>\n",
              "<body>\n",
              "  <div id=\"altair-viz\"></div>\n",
              "  <script>\n",
              "    (function(vegaEmbed) {\n",
              "      var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"data\": {\"name\": \"data-f02450ab61490a1363517a0190416235\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Origin\"}, \"x\": {\"type\": \"quantitative\", \"aggregate\": \"mean\", \"field\": \"Miles_per_Gallon\"}, \"y\": {\"type\": \"nominal\", \"field\": \"Origin\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.4.0.json\", \"datasets\": {\"data-f02450ab61490a1363517a0190416235\": [{\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 307.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"chevrolet chevelle malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3504, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 11.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 165.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"buick skylark 320\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3693, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"plymouth satellite\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3436, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"amc rebel sst\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3433, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 10.5, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"ford torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3449, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 10.0, \"Cylinders\": 8, \"Displacement\": 429.0, \"Horsepower\": 198.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"ford galaxie 500\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4341, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 9.0, \"Cylinders\": 8, \"Displacement\": 454.0, \"Horsepower\": 220.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"chevrolet impala\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4354, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 8.5, \"Cylinders\": 8, \"Displacement\": 440.0, \"Horsepower\": 215.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"plymouth fury iii\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4312, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 10.0, \"Cylinders\": 8, \"Displacement\": 455.0, \"Horsepower\": 225.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"pontiac catalina\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4425, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 8.5, \"Cylinders\": 8, \"Displacement\": 390.0, \"Horsepower\": 190.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"amc ambassador dpl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3850, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 133.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": null, \"Name\": \"citroen ds-21 pallas\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3090, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 11.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 165.0, \"Miles_per_Gallon\": null, \"Name\": \"chevrolet chevelle concours (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4142, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 153.0, \"Miles_per_Gallon\": null, \"Name\": \"ford torino (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4034, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 10.5, \"Cylinders\": 8, \"Displacement\": 383.0, \"Horsepower\": 175.0, \"Miles_per_Gallon\": null, \"Name\": \"plymouth satellite (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4166, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 175.0, \"Miles_per_Gallon\": null, \"Name\": \"amc rebel sst (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3850, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 10.0, \"Cylinders\": 8, \"Displacement\": 383.0, \"Horsepower\": 170.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"dodge challenger se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3563, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 8.0, \"Cylinders\": 8, \"Displacement\": 340.0, \"Horsepower\": 160.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"plymouth 'cuda 340\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3609, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 8.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": null, \"Name\": \"ford mustang boss 302\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3353, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 9.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"chevrolet monte carlo\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3761, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 10.0, \"Cylinders\": 8, \"Displacement\": 455.0, \"Horsepower\": 225.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"buick estate wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3086, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 113.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"toyota corona mark ii\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2372, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 198.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"plymouth duster\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2833, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 199.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"amc hornet\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2774, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 21.0, \"Name\": \"ford maverick\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2587, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"datsun pl510\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2130, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 20.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 46.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"volkswagen 1131 deluxe sedan\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1835, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 110.0, \"Horsepower\": 87.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2672, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"audi 100 ls\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2430, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 104.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"saab 99e\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2375, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 113.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"bmw 2002\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2234, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 6, \"Displacement\": 199.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 21.0, \"Name\": \"amc gremlin\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2648, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 215.0, \"Miles_per_Gallon\": 10.0, \"Name\": \"ford f250\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4615, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 307.0, \"Horsepower\": 200.0, \"Miles_per_Gallon\": 10.0, \"Name\": \"chevy c20\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4376, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 210.0, \"Miles_per_Gallon\": 11.0, \"Name\": \"dodge d200\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4382, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 18.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 193.0, \"Miles_per_Gallon\": 9.0, \"Name\": \"hi 1200d\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4732, \"Year\": \"1970-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"datsun pl510\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2130, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet vega 2300\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2264, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 113.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2228, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": null, \"Miles_per_Gallon\": 25.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2046, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 20.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": null, \"Name\": \"volkswagen super beetle 117\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1978, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"amc gremlin\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2634, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"plymouth satellite custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3439, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"chevrolet chevelle malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3329, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"ford torino 500\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3302, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 18.0, 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\"Displacement\": 116.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"opel 1900\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2123, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 19.5, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"peugeot 304\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2074, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 88.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"fiat 124b\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2065, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 71.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"toyota corolla 1200\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1773, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 72.0, \"Horsepower\": 69.0, \"Miles_per_Gallon\": 35.0, 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{\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 175.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"pontiac catalina\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4385, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"plymouth fury iii\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4135, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 153.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"ford galaxie 500\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4129, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 11.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"amc ambassador sst\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3672, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 429.0, \"Horsepower\": 208.0, \"Miles_per_Gallon\": 11.0, \"Name\": \"mercury marquis\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4633, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"buick lesabre custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4502, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 160.0, \"Miles_per_Gallon\": 12.0, \"Name\": \"oldsmobile delta 88 royale\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4456, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chrysler newport royal\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4422, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"mazda rx2 coupe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2330, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"amc matador (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3892, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 307.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevrolet chevelle concours (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4098, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford gran torino (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4294, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"plymouth satellite custom 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\"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"ford pinto (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2395, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"datsun 510 (sw)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2288, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"toyouta corona mark ii (sw)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2506, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"dodge colt (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2164, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"toyota corolla 1600 (sw)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2100, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 175.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"buick century 350\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4100, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 11.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3672, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevrolet malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3988, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 137.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"ford gran torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4042, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"dodge coronet custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3777, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 11.5, \"Cylinders\": 8, \"Displacement\": 429.0, \"Horsepower\": 198.0, \"Miles_per_Gallon\": 12.0, \"Name\": \"mercury marquis brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4952, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4464, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 158.0, \"Miles_per_Gallon\": 13.0, \"Name\": 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{\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 198.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"plymouth duster\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2904, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 21.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 46.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"volkswagen super beetle\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1950, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 11.0, \"Name\": \"chevrolet impala\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4997, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 167.0, \"Miles_per_Gallon\": 12.0, \"Name\": \"ford country\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4906, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 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21.0, \"Cylinders\": 4, \"Displacement\": 71.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"toyota corolla 1200\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1836, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"chevrolet vega\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2542, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"chevrolet chevelle malibu classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3781, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3632, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 6, \"Displacement\": 225.0, 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14.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"fiat 124 tc\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2246, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2489, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 93.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2391, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"fiat x1.9\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2000, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, 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\"Displacement\": 262.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"chevrolet monza 2+2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3221, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford mustang ii\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3169, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2171, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2639, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"amc gremlin\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2914, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"pontiac astro\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2592, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2702, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2223, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"datsun 710\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2545, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 171.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2984, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"volkswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1937, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"amc pacer\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3211, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 115.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"audi 100ls\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2694, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2957, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 98.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"volvo 244dl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2945, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"saab 99le\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2671, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"honda civic cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1795, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"fiat 131\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2464, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 81.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"opel 1900\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2220, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"capri ii\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2572, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 79.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2255, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 101.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"renault 12tl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2202, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet chevelle malibu classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"dodge coronet brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4190, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.9, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3962, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 152.0, \"Miles_per_Gallon\": 14.5, \"Name\": \"ford gran torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"plymouth valiant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3233, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"chevrolet nova\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3353, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.6, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 81.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"ford maverick\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3012, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.6, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 22.5, \"Name\": \"amc hornet\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3085, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 22.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2035, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 22.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"chevrolet woody\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2164, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1937, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1795, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"dodge aspen se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3651, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"ford granada ghia\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3574, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 16.2, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"pontiac ventura sj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3645, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.8, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"amc pacer d/l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3193, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 12.2, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 29.5, \"Name\": \"volkswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1825, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"datsun b-210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1990, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2155, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 26.5, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2565, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 130.0, \"Horsepower\": 102.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"volvo 245\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3150, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"plymouth volare premier v8\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 21.9, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3270, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 108.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"toyota mark ii\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"mercedes-benz 280s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3820, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 12.1, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"cadillac seville\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4380, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevy c10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4055, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford f108\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3870, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"dodge d100\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3755, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"honda Accelerationord cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2045, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 111.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"buick opel isuzu deluxe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"renault 5 gtl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1825, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"plymouth arrow gs\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2300, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"datsun f-10 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1945, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3880, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"oldsmobile cutlass supreme\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4060, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 13.7, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"dodge monaco brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4140, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"mercury cougar brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4295, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet concours\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3520, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.9, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"buick skylark\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3425, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 17.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth volare custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3630, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 98.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"ford granada\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3525, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 11.1, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4220, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 11.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 170.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4165, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 12.2, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chrysler cordoba\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4325, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 149.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"ford thunderbird\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4335, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"volkswagen rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1940, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"pontiac sunbird coupe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2740, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"toyota corolla liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 89.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"ford mustang ii 2+2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2755, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2051, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 15.9, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"dodge colt m/m\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2075, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.1, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"datsun 810\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2815, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 12.8, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"bmw 320i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2600, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 80.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"mazda rx-4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2720, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 21.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.1, \"Name\": \"volkswagen rabbit custom diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 66.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"ford fiesta\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 78.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 32.8, \"Name\": \"mazda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 39.4, \"Name\": \"datsun b210 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2070, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"honda civic cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 19.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3365, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"dodge diplomat\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3735, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"mercury monarch ghia\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3570, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 19.2, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"pontiac phoenix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3535, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"chevrolet malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3155, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford fairmont (auto)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2965, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 25.1, \"Name\": \"ford fairmont (man)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"plymouth volare\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3430, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3210, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"buick century special\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3380, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.8, \"Name\": \"mercury zephyr\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3070, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 18.6, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3620, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.1, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"amc concord d/l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3425, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 165.0, \"Miles_per_Gallon\": 17.7, \"Name\": \"buick regal sport coupe (turbo)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3445, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 11.2, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"ford futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3205, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.7, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"dodge magnum xe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4080, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 27.5, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2560, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"datsun 510\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2300, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 30.9, \"Name\": \"dodge omni\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2230, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 21.1, \"Name\": \"toyota celica gt liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2515, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.7, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 23.2, \"Name\": \"plymouth sapporo\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2745, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 23.8, \"Name\": \"oldsmobile starfire sx\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2855, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"datsun 200-sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2405, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.9, \"Cylinders\": 5, \"Displacement\": 131.0, \"Horsepower\": 103.0, \"Miles_per_Gallon\": 20.3, \"Name\": \"audi 5000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2830, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.6, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"volvo 264gl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3140, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.6, \"Name\": \"saab 99gle\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2795, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 133.0, \"Miles_per_Gallon\": 16.2, \"Name\": \"peugeot 604sl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"volkswagen scirocco\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1990, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.6, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 29.5, \"Name\": \"honda Accelerationord lx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2135, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"pontiac lemans v6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3245, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 19.8, \"Name\": \"mercury zephyr 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2990, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 22.3, \"Name\": \"ford fairmont 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2890, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"amc concord dl 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3265, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"dodge aspen 6\", \"Origin\": 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\"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Miles_per_Gallon\": 16.9, \"Name\": \"buick estate wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4360, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.3, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 142.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"ford country squire (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4054, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 267.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet malibu classic (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3605, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"chrysler lebaron town @ country (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.9, \"Name\": \"vw rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1925, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 34.1, \"Name\": \"maxda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 35.7, \"Name\": \"dodge colt hatchback custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1915, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 27.4, \"Name\": \"amc spirit dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2670, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 20.1, \"Cylinders\": 5, \"Displacement\": 183.0, \"Horsepower\": 77.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"mercedes benz 300d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3530, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 17.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"cadillac eldorado\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3900, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 24.8, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3190, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 22.2, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3420, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 13.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.2, \"Name\": \"plymouth horizon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2200, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.5, \"Name\": \"plymouth horizon tc3\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2150, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 31.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2020, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 69.0, \"Miles_per_Gallon\": 37.3, \"Name\": \"fiat strada custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.4, \"Name\": \"buick skylark limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2670, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 11.3, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 28.8, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2595, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 12.9, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 26.8, \"Name\": \"oldsmobile omega brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2700, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 13.2, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2556, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 41.5, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2144, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 18.8, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 38.1, \"Name\": \"toyota corolla tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1968, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 32.1, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2120, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.2, \"Name\": \"datsun 310\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2019, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2678, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 18.1, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 26.4, \"Name\": \"ford fairmont\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2870, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 20.1, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 24.3, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3003, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.1, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3381, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 34.3, \"Name\": \"audi 4000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2188, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"toyota corona liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2711, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 31.3, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2542, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 510 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2434, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.2, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 17.9, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 46.6, \"Name\": \"mazda glc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 27.9, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2800, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 40.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 21.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 44.3, \"Name\": \"vw rabbit c (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2085, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 23.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.4, \"Name\": \"vw dasher (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2335, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 19.9, \"Cylinders\": 5, \"Displacement\": 121.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 36.4, \"Name\": \"audi 5000s (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2950, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 21.8, \"Cylinders\": 4, \"Displacement\": 146.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"mercedes-benz 240d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3250, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 13.8, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 44.6, \"Name\": \"honda civic 1500 gl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1850, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": null, \"Miles_per_Gallon\": 40.9, \"Name\": \"renault lecar deluxe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1835, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 33.8, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2145, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"vokswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1845, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 11.4, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 132.0, \"Miles_per_Gallon\": 32.7, \"Name\": \"datsun 280-zx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2910, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 23.7, \"Name\": \"mazda rx-7 gs\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2420, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.1, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 35.0, \"Name\": \"triumph tr7 coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2500, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 14.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.6, \"Name\": \"ford mustang cobra\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2905, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2290, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2490, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"buick skylark\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 25.8, \"Name\": \"dodge aries wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2620, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 23.5, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2725, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 12.9, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2385, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 39.1, \"Name\": \"toyota starlet\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1755, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 64.0, \"Miles_per_Gallon\": 39.0, \"Name\": \"plymouth champ\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1875, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.1, \"Cylinders\": 4, \"Displacement\": 81.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 35.1, \"Name\": \"honda civic 1300\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1760, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.3, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2065, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 37.7, \"Name\": \"toyota tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2050, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 34.1, \"Name\": \"mazda glc 4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 34.7, \"Name\": \"plymouth horizon 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2215, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 34.4, \"Name\": \"ford escort 4w\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2045, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 20.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 29.9, \"Name\": \"ford escort 2h\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2380, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"volkswagen jetta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 100.0, \"Horsepower\": null, \"Miles_per_Gallon\": 34.5, \"Name\": \"renault 18i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2320, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 33.7, \"Name\": \"honda prelude\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2210, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2350, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 32.9, \"Name\": \"datsun 200sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2615, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.3, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 31.6, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 20.4, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 28.1, \"Name\": \"peugeot 505s turbo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3230, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": null, \"Name\": \"saab 900s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2800, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.6, \"Cylinders\": 6, \"Displacement\": 145.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 30.7, \"Name\": \"volvo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3160, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 116.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"toyota cressida\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2900, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 13.8, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 24.2, \"Name\": \"datsun 810 maxima\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 22.4, \"Name\": \"buick century\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3415, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"oldsmobile cutlass ls\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3725, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.1, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford granada gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3060, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 17.6, \"Name\": \"chrysler lebaron salon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3465, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet cavalier\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2605, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet cavalier wagon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2640, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"chevrolet cavalier 2-door\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2395, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"pontiac j2000 se hatchback\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2575, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"dodge aries se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2525, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2735, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"ford fairmont futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2865, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 20.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.0, \"Name\": \"amc concord dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3035, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"volkswagen rabbit l\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1980, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"mazda glc custom l\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2025, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"mazda glc custom\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1970, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"plymouth horizon miser\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"mercury lynx l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"nissan stanza xe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2160, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2205, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2245, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"honda civic (auto)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"datsun 310 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1995, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 6, \"Displacement\": 181.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"buick century limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2945, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 262.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"oldsmobile cutlass ciera (diesel)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3015, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"chrysler lebaron medallion\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2585, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.7, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 112.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"ford granada l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2835, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 13.9, \"Cylinders\": 4, \"Displacement\": 144.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"toyota celica gt\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2665, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"dodge charger 2.2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2370, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet camaro\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2950, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"ford mustang gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2790, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 24.6, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 44.0, \"Name\": \"vw pickup\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 11.6, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"dodge rampage\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2295, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 79.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"ford ranger\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2625, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 82.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"chevy s-10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1982-01-01T00:00:00\"}]}};\n",
              "      var embedOpt = {\"mode\": \"vega-lite\"};\n",
              "\n",
              "      function showError(el, error){\n",
              "          el.innerHTML = ('<div class=\"error\" style=\"color:red;\">'\n",
              "                          + '<p>JavaScript Error: ' + error.message + '</p>'\n",
              "                          + \"<p>This usually means there's a typo in your chart specification. \"\n",
              "                          + \"See the javascript console for the full traceback.</p>\"\n",
              "                          + '</div>');\n",
              "          throw error;\n",
              "      }\n",
              "      const el = document.getElementById('altair-viz');\n",
              "      vegaEmbed(\"#altair-viz\", spec, embedOpt)\n",
              "        .catch(error => showError(el, error));\n",
              "    })(vegaEmbed);\n",
              "\n",
              "  </script>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S64tLQHY16jh",
        "colab_type": "text"
      },
      "source": [
        "Upload your file here (request.csv)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ww-HcdVwuwEM",
        "colab_type": "code",
        "outputId": "83cbc822-7382-4809-80c5-f7fd8882ef3e",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-17b9450d-cbde-48e5-a303-82c85ce63faf\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-17b9450d-cbde-48e5-a303-82c85ce63faf\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving index.html to index (1).html\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbYIZtptbpTG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load an example dataset\n",
        "from vega_datasets import data\n",
        "cars = data.cars()\n",
        "\n",
        "import altair as alt\n",
        "\n",
        "interval = alt.selection_interval()\n",
        "\n",
        "base = alt.Chart(cars).mark_point().encode(\n",
        "  y='Miles_per_Gallon',\n",
        "  color=alt.condition(interval, 'Origin', alt.value('lightgray'))\n",
        ").properties(\n",
        "  selection=interval\n",
        ")\n",
        "\n",
        "base.encode(x='Acceleration') | base.encode(x='Horsepower')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1cgxOH25NmQ",
        "colab_type": "code",
        "outputId": "8f314e55-1e71-404f-f38f-92c967dd160d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "source": [
        "!ls -l"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 712\n",
            "drwx------ 4 root root   4096 Sep  9 16:16 gdrive\n",
            "-rw-r--r-- 1 root root    407 Sep  9 16:20 magic_stuff.py\n",
            "-rw-r--r-- 1 root root 712814 Sep  9 16:24 pytorch.pptx\n",
            "drwxr-xr-x 1 root root   4096 Aug 27 16:17 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4bssBq6QngE",
        "colab_type": "code",
        "outputId": "2d7678b6-95b9-4e9b-f8cf-f9039ea9d1f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        }
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"automl-tables_notebooks_census_income.csv\")\n",
        "df.head(2)"
      ],
      "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>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education_num</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  fnlwgt  ... hours_per_week  native_country  income\n",
              "0   39          State-gov   77516  ...             40   United-States   <=50K\n",
              "1   50   Self-emp-not-inc   83311  ...             13   United-States   <=50K\n",
              "\n",
              "[2 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJYd_o2GujXf",
        "colab_type": "code",
        "outputId": "c5dfa89b-9e56-4bef-f29c-8162bcf193b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# load an example dataset\n",
        "from vega_datasets import data\n",
        "cars = data.cars()\n",
        "\n",
        "# plot the dataset, referencing dataframe column names\n",
        "import altair as alt\n",
        "alt.Chart(cars).mark_bar().encode(\n",
        "  x=alt.X('Miles_per_Gallon', bin=True),\n",
        "  y='count()',\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Chart({\n",
              "  data:      Acceleration  Cylinders  Displacement  ...  Origin  Weight_in_lbs       Year\n",
              "  0            12.0          8         307.0  ...     USA           3504 1970-01-01\n",
              "  1            11.5          8         350.0  ...     USA           3693 1970-01-01\n",
              "  2            11.0          8         318.0  ...     USA           3436 1970-01-01\n",
              "  3            12.0          8         304.0  ...     USA           3433 1970-01-01\n",
              "  4            10.5          8         302.0  ...     USA           3449 1970-01-01\n",
              "  5            10.0          8         429.0  ...     USA           4341 1970-01-01\n",
              "  6             9.0          8         454.0  ...     USA           4354 1970-01-01\n",
              "  7             8.5          8         440.0  ...     USA           4312 1970-01-01\n",
              "  8            10.0          8         455.0  ...     USA           4425 1970-01-01\n",
              "  9             8.5          8         390.0  ...     USA           3850 1970-01-01\n",
              "  10           17.5          4         133.0  ...  Europe           3090 1970-01-01\n",
              "  11           11.5          8         350.0  ...     USA           4142 1970-01-01\n",
              "  12           11.0          8         351.0  ...     USA           4034 1970-01-01\n",
              "  13           10.5          8         383.0  ...     USA           4166 1970-01-01\n",
              "  14           11.0          8         360.0  ...     USA           3850 1970-01-01\n",
              "  15           10.0          8         383.0  ...     USA           3563 1970-01-01\n",
              "  16            8.0          8         340.0  ...     USA           3609 1970-01-01\n",
              "  17            8.0          8         302.0  ...     USA           3353 1970-01-01\n",
              "  18            9.5          8         400.0  ...     USA           3761 1970-01-01\n",
              "  19           10.0          8         455.0  ...     USA           3086 1970-01-01\n",
              "  20           15.0          4         113.0  ...   Japan           2372 1970-01-01\n",
              "  21           15.5          6         198.0  ...     USA           2833 1970-01-01\n",
              "  22           15.5          6         199.0  ...     USA           2774 1970-01-01\n",
              "  23           16.0          6         200.0  ...     USA           2587 1970-01-01\n",
              "  24           14.5          4          97.0  ...   Japan           2130 1970-01-01\n",
              "  25           20.5          4          97.0  ...  Europe           1835 1970-01-01\n",
              "  26           17.5          4         110.0  ...  Europe           2672 1970-01-01\n",
              "  27           14.5          4         107.0  ...  Europe           2430 1970-01-01\n",
              "  28           17.5          4         104.0  ...  Europe           2375 1970-01-01\n",
              "  29           12.5          4         121.0  ...  Europe           2234 1970-01-01\n",
              "  ..            ...        ...           ...  ...     ...            ...        ...\n",
              "  376          18.6          4         112.0  ...     USA           2640 1982-01-01\n",
              "  377          18.0          4         112.0  ...     USA           2395 1982-01-01\n",
              "  378          16.2          4         112.0  ...     USA           2575 1982-01-01\n",
              "  379          16.0          4         135.0  ...     USA           2525 1982-01-01\n",
              "  380          18.0          4         151.0  ...     USA           2735 1982-01-01\n",
              "  381          16.4          4         140.0  ...     USA           2865 1982-01-01\n",
              "  382          20.5          4         151.0  ...     USA           3035 1982-01-01\n",
              "  383          15.3          4         105.0  ...  Europe           1980 1982-01-01\n",
              "  384          18.2          4          91.0  ...   Japan           2025 1982-01-01\n",
              "  385          17.6          4          91.0  ...   Japan           1970 1982-01-01\n",
              "  386          14.7          4         105.0  ...     USA           2125 1982-01-01\n",
              "  387          17.3          4          98.0  ...     USA           2125 1982-01-01\n",
              "  388          14.5          4         120.0  ...   Japan           2160 1982-01-01\n",
              "  389          14.5          4         107.0  ...   Japan           2205 1982-01-01\n",
              "  390          16.9          4         108.0  ...   Japan           2245 1982-01-01\n",
              "  391          15.0          4          91.0  ...   Japan           1965 1982-01-01\n",
              "  392          15.7          4          91.0  ...   Japan           1965 1982-01-01\n",
              "  393          16.2          4          91.0  ...   Japan           1995 1982-01-01\n",
              "  394          16.4          6         181.0  ...     USA           2945 1982-01-01\n",
              "  395          17.0          6         262.0  ...     USA           3015 1982-01-01\n",
              "  396          14.5          4         156.0  ...     USA           2585 1982-01-01\n",
              "  397          14.7          6         232.0  ...     USA           2835 1982-01-01\n",
              "  398          13.9          4         144.0  ...   Japan           2665 1982-01-01\n",
              "  399          13.0          4         135.0  ...     USA           2370 1982-01-01\n",
              "  400          17.3          4         151.0  ...     USA           2950 1982-01-01\n",
              "  401          15.6          4         140.0  ...     USA           2790 1982-01-01\n",
              "  402          24.6          4          97.0  ...  Europe           2130 1982-01-01\n",
              "  403          11.6          4         135.0  ...     USA           2295 1982-01-01\n",
              "  404          18.6          4         120.0  ...     USA           2625 1982-01-01\n",
              "  405          19.4          4         119.0  ...     USA           2720 1982-01-01\n",
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28.0, \"Name\": \"opel 1900\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2123, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 19.5, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"peugeot 304\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2074, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 88.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"fiat 124b\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2065, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 71.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"toyota corolla 1200\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1773, \"Year\": \"1971-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 72.0, \"Horsepower\": 69.0, \"Miles_per_Gallon\": 35.0, \"Name\": \"datsun 1200\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1613, 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175.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"pontiac catalina\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4385, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"plymouth fury iii\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4135, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 153.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"ford galaxie 500\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4129, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 11.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"amc ambassador sst\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3672, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 429.0, \"Horsepower\": 208.0, \"Miles_per_Gallon\": 11.0, \"Name\": \"mercury marquis\", 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88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"toyota corolla 1600 (sw)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2100, \"Year\": \"1972-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 175.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"buick century 350\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4100, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 11.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3672, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevrolet malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3988, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 137.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"ford gran 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\"Miles_per_Gallon\": 13.0, \"Name\": \"plymouth custom suburb\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4654, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 12.0, \"Name\": \"oldsmobile vista cruiser\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4499, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"amc gremlin\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2789, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"toyota carina\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2279, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 19.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 21.0, \"Name\": \"chevrolet vega\", 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{\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"fiat 124 sport coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2265, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"chevrolet monte carlo s\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4082, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 9.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 230.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4278, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 19.5, \"Cylinders\": 4, \"Displacement\": 68.0, \"Horsepower\": 49.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"fiat 128\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1867, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"opel manta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2158, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 114.0, \"Horsepower\": 91.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"audi 100ls\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2582, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 112.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"volvo 144ea\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2868, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"dodge dart custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3399, \"Year\": \"1973-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"saab 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71.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"toyota corolla 1200\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1836, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"chevrolet vega\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2542, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"chevrolet chevelle malibu classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3781, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3632, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"plymouth satellite sebring\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3613, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"ford gran torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4141, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"buick century luxus (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4699, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"dodge coronet custom (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4457, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"ford gran torino 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116.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"fiat 124 tc\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2246, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2489, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 93.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2391, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"fiat x1.9\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2000, \"Year\": \"1974-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth valiant 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\"Displacement\": 250.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"chevroelt chevelle malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3897, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3730, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"plymouth fury\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3785, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.0, \"Name\": \"buick skyhawk\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3039, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 262.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"chevrolet monza 2+2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3221, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford mustang ii\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3169, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2171, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2639, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"amc gremlin\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2914, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"pontiac astro\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2592, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2702, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2223, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"datsun 710\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2545, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 14.5, 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\"Miles_per_Gallon\": 23.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2957, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 98.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"volvo 244dl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2945, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"saab 99le\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2671, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"honda civic cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1795, \"Year\": \"1975-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"fiat 131\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2464, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 81.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"opel 1900\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2220, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"capri ii\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2572, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 79.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2255, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 101.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"renault 12tl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2202, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet chevelle malibu classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"dodge coronet brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4190, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.9, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3962, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 152.0, \"Miles_per_Gallon\": 14.5, \"Name\": \"ford gran torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"plymouth valiant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3233, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"chevrolet nova\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3353, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.6, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 81.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"ford maverick\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3012, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.6, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 22.5, \"Name\": \"amc hornet\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3085, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 22.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2035, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 22.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"chevrolet woody\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2164, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1937, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1795, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"dodge aspen se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3651, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"ford granada ghia\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3574, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 16.2, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"pontiac ventura sj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3645, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.8, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"amc pacer d/l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3193, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 12.2, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 29.5, \"Name\": \"volkswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1825, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"datsun b-210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1990, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2155, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 26.5, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2565, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 130.0, \"Horsepower\": 102.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"volvo 245\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3150, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"plymouth volare premier v8\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 21.9, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3270, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 108.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"toyota mark ii\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"mercedes-benz 280s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3820, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 12.1, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"cadillac seville\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4380, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevy c10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4055, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford f108\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3870, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"dodge d100\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3755, \"Year\": \"1976-01-01T00:00:00\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"honda Accelerationord cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2045, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 111.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"buick opel isuzu deluxe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"renault 5 gtl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1825, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"plymouth arrow gs\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2300, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"datsun f-10 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1945, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3880, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"oldsmobile cutlass supreme\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4060, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 13.7, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"dodge monaco brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4140, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"mercury cougar brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4295, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet concours\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3520, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.9, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"buick skylark\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3425, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 17.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth volare custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3630, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 98.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"ford granada\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3525, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 11.1, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4220, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 11.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 170.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4165, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 12.2, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chrysler cordoba\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4325, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 149.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"ford thunderbird\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4335, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"volkswagen rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1940, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"pontiac sunbird coupe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2740, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"toyota corolla liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 89.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"ford mustang ii 2+2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2755, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2051, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 15.9, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"dodge colt m/m\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2075, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.1, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"datsun 810\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2815, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 12.8, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"bmw 320i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2600, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 80.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"mazda rx-4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2720, \"Year\": \"1977-01-01T00:00:00\"}, {\"Acceleration\": 21.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.1, \"Name\": \"volkswagen rabbit custom diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 66.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"ford fiesta\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 78.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 32.8, \"Name\": \"mazda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 39.4, \"Name\": \"datsun b210 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2070, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"honda civic cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 19.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3365, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"dodge diplomat\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3735, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"mercury monarch ghia\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3570, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 19.2, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"pontiac phoenix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3535, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"chevrolet malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3155, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford fairmont (auto)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2965, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 25.1, \"Name\": \"ford fairmont (man)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"plymouth volare\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3430, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3210, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"buick century special\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3380, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.8, \"Name\": \"mercury zephyr\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3070, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 18.6, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3620, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.1, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"amc concord d/l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3425, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 165.0, \"Miles_per_Gallon\": 17.7, \"Name\": \"buick regal sport coupe (turbo)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3445, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 11.2, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"ford futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3205, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.7, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"dodge magnum xe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4080, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 27.5, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2560, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"datsun 510\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2300, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 30.9, \"Name\": \"dodge omni\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2230, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 21.1, \"Name\": \"toyota celica gt liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2515, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.7, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 23.2, \"Name\": \"plymouth sapporo\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2745, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 23.8, \"Name\": \"oldsmobile starfire sx\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2855, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"datsun 200-sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2405, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.9, \"Cylinders\": 5, \"Displacement\": 131.0, \"Horsepower\": 103.0, \"Miles_per_Gallon\": 20.3, \"Name\": \"audi 5000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2830, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 13.6, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"volvo 264gl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3140, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.6, \"Name\": \"saab 99gle\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2795, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 133.0, \"Miles_per_Gallon\": 16.2, \"Name\": \"peugeot 604sl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"volkswagen scirocco\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1990, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 16.6, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 29.5, \"Name\": \"honda Accelerationord lx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2135, \"Year\": \"1978-01-01T00:00:00\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"pontiac lemans v6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3245, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 19.8, \"Name\": \"mercury zephyr 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2990, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 22.3, \"Name\": \"ford fairmont 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2890, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"amc concord dl 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3265, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"dodge aspen 6\", \"Origin\": 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\"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.9, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Miles_per_Gallon\": 16.9, \"Name\": \"buick estate wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4360, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 14.3, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 142.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"ford country squire (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4054, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 267.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet malibu classic (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3605, \"Year\": \"1979-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"chrysler lebaron town @ country (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": 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\"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 18.8, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 38.1, \"Name\": \"toyota corolla tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1968, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 32.1, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2120, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.2, \"Name\": \"datsun 310\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2019, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2678, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 18.1, 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29.8, \"Name\": \"toyota corona liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2711, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 31.3, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2542, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 510 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2434, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.2, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 17.9, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 46.6, \"Name\": \"mazda glc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 27.9, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2800, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 40.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 21.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 44.3, \"Name\": \"vw rabbit c (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2085, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 23.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.4, \"Name\": \"vw dasher (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2335, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 19.9, \"Cylinders\": 5, \"Displacement\": 121.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 36.4, \"Name\": \"audi 5000s (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2950, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 21.8, \"Cylinders\": 4, \"Displacement\": 146.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"mercedes-benz 240d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3250, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 13.8, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 44.6, \"Name\": \"honda civic 1500 gl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1850, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": null, \"Miles_per_Gallon\": 40.9, \"Name\": \"renault lecar deluxe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1835, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 33.8, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2145, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"vokswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1845, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 11.4, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 132.0, \"Miles_per_Gallon\": 32.7, \"Name\": \"datsun 280-zx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2910, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 12.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 23.7, \"Name\": \"mazda rx-7 gs\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2420, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.1, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 35.0, \"Name\": \"triumph tr7 coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2500, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 14.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.6, \"Name\": \"ford mustang cobra\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2905, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2290, \"Year\": \"1980-01-01T00:00:00\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2490, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"buick skylark\", \"Origin\": \"USA\", 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{\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 64.0, \"Miles_per_Gallon\": 39.0, \"Name\": \"plymouth champ\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1875, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.1, \"Cylinders\": 4, \"Displacement\": 81.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 35.1, \"Name\": \"honda civic 1300\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1760, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.3, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2065, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, 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\"Weight_in_lbs\": 2380, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"volkswagen jetta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 100.0, \"Horsepower\": null, \"Miles_per_Gallon\": 34.5, \"Name\": \"renault 18i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2320, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 33.7, \"Name\": \"honda prelude\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2210, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2350, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 32.9, \"Name\": \"datsun 200sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2615, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.3, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 31.6, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 20.4, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 28.1, \"Name\": \"peugeot 505s turbo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3230, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": null, \"Name\": \"saab 900s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2800, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.6, \"Cylinders\": 6, \"Displacement\": 145.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 30.7, \"Name\": \"volvo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3160, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 116.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"toyota cressida\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2900, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 13.8, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 24.2, \"Name\": \"datsun 810 maxima\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 22.4, \"Name\": \"buick century\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3415, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"oldsmobile cutlass ls\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3725, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.1, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford granada gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3060, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 17.6, \"Name\": \"chrysler lebaron salon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3465, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet cavalier\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2605, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet cavalier wagon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2640, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"chevrolet cavalier 2-door\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2395, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"pontiac j2000 se hatchback\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2575, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"dodge aries se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2525, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2735, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"ford fairmont futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2865, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 20.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.0, \"Name\": \"amc concord dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3035, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"volkswagen rabbit l\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1980, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"mazda glc custom l\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2025, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"mazda glc custom\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1970, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"plymouth horizon miser\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"mercury lynx l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"nissan stanza xe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2160, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2205, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2245, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"honda civic (auto)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"datsun 310 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1995, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 16.4, \"Cylinders\": 6, \"Displacement\": 181.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"buick century limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2945, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 262.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"oldsmobile cutlass ciera (diesel)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3015, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"chrysler lebaron medallion\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2585, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 14.7, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 112.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"ford granada l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2835, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 13.9, \"Cylinders\": 4, \"Displacement\": 144.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"toyota celica gt\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2665, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 13.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"dodge charger 2.2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2370, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet camaro\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2950, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 15.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"ford mustang gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2790, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 24.6, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 44.0, \"Name\": \"vw pickup\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 11.6, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"dodge rampage\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2295, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 79.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"ford ranger\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2625, \"Year\": \"1982-01-01T00:00:00\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 82.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"chevy s-10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1982-01-01T00:00:00\"}]}};\n",
              "      var embedOpt = {\"mode\": \"vega-lite\"};\n",
              "\n",
              "      function showError(el, error){\n",
              "          el.innerHTML = ('<div class=\"error\" style=\"color:red;\">'\n",
              "                          + '<p>JavaScript Error: ' + error.message + '</p>'\n",
              "                          + \"<p>This usually means there's a typo in your chart specification. \"\n",
              "                          + \"See the javascript console for the full traceback.</p>\"\n",
              "                          + '</div>');\n",
              "          throw error;\n",
              "      }\n",
              "      const el = document.getElementById('altair-viz');\n",
              "      vegaEmbed(\"#altair-viz\", spec, embedOpt)\n",
              "        .catch(error => showError(el, error));\n",
              "\n",
              "  </script>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUqRwiunUDxP",
        "colab_type": "code",
        "outputId": "a361182c-6990-4b9f-e68d-3f78aa6ac1f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/noahgift/sugar/master/data/education_sugar_cdc_2003.csv\")\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>State</th>\n",
              "      <th>Employed</th>\n",
              "      <th>Not employed</th>\n",
              "      <th>Retired</th>\n",
              "      <th>&lt;High school</th>\n",
              "      <th>High school</th>\n",
              "      <th>Some college</th>\n",
              "      <th>College graduate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>26.2 (23.6–28.9)</td>\n",
              "      <td>32.1 (27.8–36.8)</td>\n",
              "      <td>16.0 (12.6–20.2)</td>\n",
              "      <td>47.1 (37.8–56.5)</td>\n",
              "      <td>34.9 (31.1–38.9)</td>\n",
              "      <td>24.2 (21.0–27.8)</td>\n",
              "      <td>12.9 (10.5–15.7)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>33.0 (28.5–37.8)</td>\n",
              "      <td>28.7 (23.5–34.5)</td>\n",
              "      <td>13.8 (10.8–17.5)</td>\n",
              "      <td>40.4 (30.9–50.7)</td>\n",
              "      <td>36.5 (30.7–42.7)</td>\n",
              "      <td>24.4 (19.9–29.4)</td>\n",
              "      <td>14.6 (11.6–18.3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>California</td>\n",
              "      <td>22.9 (20.9–25.1)</td>\n",
              "      <td>30.2 (27.1–33.4)</td>\n",
              "      <td>15.0 (12.2–18.2)</td>\n",
              "      <td>38.5 (34.2–43.0)</td>\n",
              "      <td>29.9 (26.5–33.7)</td>\n",
              "      <td>21.4 (18.8–24.2)</td>\n",
              "      <td>11.5 (9.8–13.5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Connecticut</td>\n",
              "      <td>18.9 (17.1–20.9)</td>\n",
              "      <td>24.3 (20.8–28.2)</td>\n",
              "      <td>15.0 (12.7–17.7)</td>\n",
              "      <td>27.8 (22.4–33.9)</td>\n",
              "      <td>26.9 (23.7–30.3)</td>\n",
              "      <td>19.9 (17.2–23.0)</td>\n",
              "      <td>10.2 (8.7–12.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>District of Columbia</td>\n",
              "      <td>18.5 (15.7–21.7)</td>\n",
              "      <td>34.6 (29.5–40.1)</td>\n",
              "      <td>18.5 (15.3–22.1)</td>\n",
              "      <td>45.6 (36.4–55.2)</td>\n",
              "      <td>39.0 (33.1–45.2)</td>\n",
              "      <td>28.9 (23.4–35.0)</td>\n",
              "      <td>8.4 (7.0–10.1)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  State          Employed      Not employed           Retired  \\\n",
              "0                Alaska  26.2 (23.6–28.9)  32.1 (27.8–36.8)  16.0 (12.6–20.2)   \n",
              "1               Arizona  33.0 (28.5–37.8)  28.7 (23.5–34.5)  13.8 (10.8–17.5)   \n",
              "2            California  22.9 (20.9–25.1)  30.2 (27.1–33.4)  15.0 (12.2–18.2)   \n",
              "3           Connecticut  18.9 (17.1–20.9)  24.3 (20.8–28.2)  15.0 (12.7–17.7)   \n",
              "4  District of Columbia  18.5 (15.7–21.7)  34.6 (29.5–40.1)  18.5 (15.3–22.1)   \n",
              "\n",
              "       <High school       High school      Some college  College graduate  \n",
              "0  47.1 (37.8–56.5)  34.9 (31.1–38.9)  24.2 (21.0–27.8)  12.9 (10.5–15.7)  \n",
              "1  40.4 (30.9–50.7)  36.5 (30.7–42.7)  24.4 (19.9–29.4)  14.6 (11.6–18.3)  \n",
              "2  38.5 (34.2–43.0)  29.9 (26.5–33.7)  21.4 (18.8–24.2)   11.5 (9.8–13.5)  \n",
              "3  27.8 (22.4–33.9)  26.9 (23.7–30.3)  19.9 (17.2–23.0)   10.2 (8.7–12.0)  \n",
              "4  45.6 (36.4–55.2)  39.0 (33.1–45.2)  28.9 (23.4–35.0)    8.4 (7.0–10.1)  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X97XO2e8_reX",
        "colab_type": "code",
        "outputId": "e6745419-5256-4c7e-fd20-2583ad26fd29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "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>State</th>\n",
              "      <th>Employed</th>\n",
              "      <th>Not employed</th>\n",
              "      <th>Retired</th>\n",
              "      <th>&lt;High school</th>\n",
              "      <th>High school</th>\n",
              "      <th>Some college</th>\n",
              "      <th>College graduate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Iowa</td>\n",
              "      <td>26.2 (23.6–28.9)</td>\n",
              "      <td>31.5 (28.3–34.9)</td>\n",
              "      <td>13.8 (10.8–17.5)</td>\n",
              "      <td>32.9 (26.0–40.6)</td>\n",
              "      <td>34.9 (31.1–38.9)</td>\n",
              "      <td>24.2 (21.0–27.8)</td>\n",
              "      <td>11.5 (9.8–13.5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       State          Employed      Not employed           Retired  \\\n",
              "count     24                24                24                24   \n",
              "unique    24                24                24                24   \n",
              "top     Iowa  26.2 (23.6–28.9)  31.5 (28.3–34.9)  13.8 (10.8–17.5)   \n",
              "freq       1                 1                 1                 1   \n",
              "\n",
              "            <High school       High school      Some college College graduate  \n",
              "count                 24                24                24               24  \n",
              "unique                24                24                23               24  \n",
              "top     32.9 (26.0–40.6)  34.9 (31.1–38.9)  24.2 (21.0–27.8)  11.5 (9.8–13.5)  \n",
              "freq                   1                 1                 2                1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kmgxW7Sxa6j",
        "colab_type": "text"
      },
      "source": [
        "##### Forms in Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZqHMr_brxdkA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Use_Python = False #@param [\"False\", \"True\"] {type:\"raw\"}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1z9fz5_x_bf",
        "colab_type": "code",
        "outputId": "51e94991-fe40-4eec-be4d-feb24fd283ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(f\"You select it is {Use_Python} you use Python\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You select it is False you use Python\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kn-P-e_hjvK5",
        "colab_type": "text"
      },
      "source": [
        "#### Python executable\n",
        "\n",
        "Can run scripts, REPL and even run python statements with -c flag and semicolon to string together multiple statements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2aDegQxjegv",
        "colab_type": "code",
        "outputId": "417a8e03-80a3-43bd-e272-c502f294523f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "!python -c \"import os;print(os.listdir())\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.config', 'magic_stuff.py', 'gdrive', 'sample_data']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WaXhDeUM_49R",
        "colab_type": "code",
        "outputId": "09858dd1-ae6b-40e5-8bfc-538ad926fb87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "source": [
        "!ls -l"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 712\n",
            "drwx------ 4 root root   4096 Sep  9 16:16 gdrive\n",
            "-rw-r--r-- 1 root root    407 Sep  9 16:20 magic_stuff.py\n",
            "-rw-r--r-- 1 root root 712814 Sep  9 16:25 pytorch.pptx\n",
            "drwxr-xr-x 1 root root   4096 Aug 27 16:17 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58zCAVUC_8JP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q yellowbrick"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egHxTJUGNYCA",
        "colab_type": "code",
        "outputId": "fa298362-f0fa-4fa2-cfd1-0b6a02da5bb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "#this is how you capture input to a program\n",
        "import sys;sys.argv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py',\n",
              " '-f',\n",
              " '/root/.local/share/jupyter/runtime/kernel-559953d0-ac45-4a1a-a716-8951070eaab5.json']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HJtGuiKUYFU",
        "colab_type": "text"
      },
      "source": [
        "### Introductory Concepts\n",
        "*  **Procedural Statements**\n",
        "*  Strings and String Formatting\n",
        "*  Numbers and Arithmetic Operations\n",
        "*  Data Structures\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwmnQEziVQ9k",
        "colab_type": "text"
      },
      "source": [
        " #### Procedural Statements\n",
        " Procedural statements are literally statements that  can be issued one line at a time.  Below are types of procedural statements.  These statements can be run in:\n",
        " * Jupyter Notebook\n",
        " * IPython shell\n",
        " * Python interpreter\n",
        " * Python scripts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTePE3imVqfP",
        "colab_type": "text"
      },
      "source": [
        "**Printing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1r0n2V3Vs70",
        "colab_type": "code",
        "outputId": "dcb2498e-0111-4d42-f0a6-4b7ea4ce5637",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Hello world\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello world\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nUjNnApV6cC",
        "colab_type": "text"
      },
      "source": [
        "**Create Variable and Use Variable**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhjO2LpSVx9V",
        "colab_type": "code",
        "outputId": "1f7238b5-7bbe-49f4-d028-b1f7b672ae25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "variable = \"armbar\"\n",
        "variable"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'armbar'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H004n8R-WDGF",
        "colab_type": "text"
      },
      "source": [
        "**Multiple procedural statements**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrZWZEFmV_bN",
        "colab_type": "code",
        "outputId": "63e00f25-e587-4e99-d362-910db2b106c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "attack_one = \"kimura\"\n",
        "attack_two = \"arm triangle\"\n",
        "print(\"In Brazilian Jiu Jitsu a common attack is a:\", attack_one)\n",
        "print(\"Another common attack is a:\", attack_two)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In Brazilian Jiu Jitsu a common attack is a: kimura\n",
            "Another common attack is a: arm triangle\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6IiZ6RnLWKsq",
        "colab_type": "text"
      },
      "source": [
        "**Adding Numbers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h049FhlhWPRm",
        "colab_type": "code",
        "outputId": "cca01c75-16c4-40a3-d80e-7c30b8a3cfda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "1+1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlszsN87WmcO",
        "colab_type": "text"
      },
      "source": [
        "**Adding Phrases**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaE5GnCaWgbI",
        "colab_type": "code",
        "outputId": "ef871d14-8cf9-4bc8-c5b9-ddbd6a6480cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\"arm\" + \" bar\"+\" 4\"+\" morestuff \" + \"lemon\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'arm bar 4 morestuff lemon'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSrfrIO1XYts",
        "colab_type": "text"
      },
      "source": [
        "**Complex statements**\n",
        "\n",
        "More complex statements can be created that use data structures like the belts variable, which is a list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nf0W0e3XSQS",
        "colab_type": "code",
        "outputId": "1af4ee6a-83ba-4b65-a08d-9a3d2751c7fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "belts = [\"white\", \"blue\", \"purple\", \"brown\", \"black\"]\n",
        "for belt in belts:\n",
        "    if \"black\" in belt:\n",
        "        print(\"The belt I want to be is:\", belt)\n",
        "    else:\n",
        "        print(\"This is not the belt I want to end up at:\", belt)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is not the belt I want to end up at: white\n",
            "This is not the belt I want to end up at: blue\n",
            "This is not the belt I want to end up at: purple\n",
            "This is not the belt I want to end up at: brown\n",
            "The belt I want to be is: black\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYzPEDtnXlbx",
        "colab_type": "text"
      },
      "source": [
        "#### Strings and String Formatting\n",
        "\n",
        "Strings are a sequence of characters and they are often programmatically formatted.  Almost all Python programs have strings because they can be used to send messages to users who use the program.  When creating strings there are few core concepts to understand:\n",
        "\n",
        "* Strings can be create with the single, double and triple/double quotes\n",
        "* Strings are can be formatted\n",
        "* One complication of strings is they can be encoded in several formats including unicode\n",
        "* Many methods are available to operate on strings.  In an editor or IPython shell you can see these methods by tab completion: \n",
        "```\n",
        "basic_string.\n",
        "            capitalize()   format()       islower()      lower()        rpartition()   title()         \n",
        "            casefold()     format_map()   isnumeric()    lstrip()       rsplit()       translate()     \n",
        "            center()       index()        isprintable()  maketrans()    rstrip()       upper()         \n",
        "            count()        isalnum()      isspace()      partition()    split()        zfill()         \n",
        "            encode()       isalpha()      istitle()      replace()      splitlines()                  \n",
        "            endswith()     isdecimal()    isupper()      rfind()        startswith()                  \n",
        "            expandtabs()   isdigit()      join()         rindex()       strip()                       \n",
        "            find()         isidentifier() ljust()        rjust()        swapcase()        \n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHJX2ZFiXbFC",
        "colab_type": "code",
        "outputId": "b280f88a-82f3-4f73-dc10-0ee613124c67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "my_string = \"this is a string I am using this time\"\n",
        "my_string.split()\n",
        "#my_string.upper()\n",
        "#my_string.title()\n",
        "#my_string.count(\"this\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['this', 'is', 'a', 'string', 'I', 'am', 'using', 'this', 'time']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMBeWJv5claa",
        "colab_type": "code",
        "outputId": "afe09459-a2c4-4e30-9737-3ad05f759b78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "my_string.capitalize()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'This is a string i am using this time'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qK6mSH0xxCY",
        "colab_type": "code",
        "outputId": "2310dc3c-af6e-40a0-92bd-a30d09524fef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "my_string.isnumeric()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKa_uTtpwwtm",
        "colab_type": "code",
        "outputId": "5d1b664e-cbf2-4311-dd38-c03aa83fc312",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "print(my_string)\n",
        "var2 = my_string.swapcase()\n",
        "print(var2)\n",
        "print(var2.swapcase())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "this is a string I am using this time\n",
            "THIS IS A STRING i AM USING THIS TIME\n",
            "this is a string I am using this time\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8afQjS25YDvQ",
        "colab_type": "text"
      },
      "source": [
        "**Basic String**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfhfvyIpX-pc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "basic_string = \"Brazilian Jiu Jitsu\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMYqc-cKYJlL",
        "colab_type": "text"
      },
      "source": [
        "**Splitting String**\n",
        "\n",
        "Turn a string in a list by splitting on spaces, or some other thing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJaoPyTzYGSk",
        "colab_type": "code",
        "outputId": "d826cccd-dbbf-49f4-aa1a-6cf77f5ea342",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#split on spaces (default)\n",
        "basic_string.split()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Brazilian', 'Jiu', 'Jitsu']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jt4NbwL15MBl",
        "colab_type": "code",
        "outputId": "1ab6e74c-f0e2-4fbc-bff5-a8366018f4c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "result = basic_string.split()\n",
        "len(result)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z94Ajl0NZXBP",
        "colab_type": "code",
        "outputId": "eb99242a-36a8-4153-d6f7-18572ed78469",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#split on hyphen\n",
        "string_with_hyphen = \"Brazilian-Jiu-Jitsu\"\n",
        "string_with_hyphen.split(\"-\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Brazilian', 'Jiu', 'Jitsu']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsPKeSMA5dnP",
        "colab_type": "code",
        "outputId": "64fc3052-1b73-450a-ea9b-d68b176f7f82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#split on comma\n",
        "string_with_hyphen = \"Brazilian,Jiu,Jitsu\"\n",
        "string_with_hyphen.split(\",\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Brazilian', 'Jiu', 'Jitsu']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ti9hySuGZbbX",
        "colab_type": "text"
      },
      "source": [
        "**All Capital**\n",
        "\n",
        "Turn a string into all Capital Letter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2NEPM8PZZT_",
        "colab_type": "code",
        "outputId": "a060ef90-cbdc-4a96-928a-73d21e13d104",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "basic_string.capitalize()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Brazilian jiu jitsu'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZlifO6c4Zk5W",
        "colab_type": "text"
      },
      "source": [
        "**Slicing Strings**\n",
        "\n",
        "Strings can be referenced by length and sliced"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBXlXz0TZh1Q",
        "colab_type": "code",
        "outputId": "30a158fa-7075-45cd-ee9b-36bc183d66a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Get the last character\n",
        "basic_string[-1:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'u'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvNrJzBNyvZz",
        "colab_type": "code",
        "outputId": "e6eef812-36ee-44c3-e85c-8377956990e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(basic_string[2:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhgFdziaZtPv",
        "colab_type": "code",
        "outputId": "8a849e27-ccca-4cff-8793-c773c673b236",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Get length of string\n",
        "len(basic_string)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neXScIYHD75C",
        "colab_type": "code",
        "outputId": "e60c9fb5-0b25-4dce-b4d3-b47575888e67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "basic_string[-18:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'razilian Jiu Jitsu'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxvtolozZy1f",
        "colab_type": "text"
      },
      "source": [
        "**Strings Can Be Added Together**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIVgqMsRZvNQ",
        "colab_type": "code",
        "outputId": "f3a9358a-322d-4e4e-ea5f-4e547bc68845",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "basic_string + \" is my favorite Martial Art\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Brazilian Jiu Jitsu is my favorite Martial Art'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdga91RBzMb_",
        "colab_type": "code",
        "outputId": "2a858ab9-60ab-4939-ff72-20da23cba7bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "items = [\"-\",1,2,3]\n",
        "for item in items:\n",
        "  basic_string += str(item)\n",
        "basic_string"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Brazilian Jiu Jitsu-123'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trK8HQJ1Mx6E",
        "colab_type": "code",
        "outputId": "175737c8-9fa5-4b8b-fbeb-02f3f13e6954",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\"this is a string format: %s\" % \"wow\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'this is a string format: wow'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNYxshNHZ6O8",
        "colab_type": "text"
      },
      "source": [
        "**F-Strings Can Be Formatted in More Complex Ways**\n",
        "\n",
        "One of the best ways to format a string in modern Python 3 is to use f-strings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_NAcD5sZ1l4",
        "colab_type": "code",
        "outputId": "37a3ef35-d47b-4a30-db32-bcc80894dd4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "f'I love practicing my favorite Martial Art, {basic_string}'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'I love practicing my favorite Martial Art, Brazilian Jiu Jitsu-123'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlpMuwdmbui0",
        "colab_type": "text"
      },
      "source": [
        "**Strings Can Use Triple Quotes to Wrap**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWmNAWzqbT4k",
        "colab_type": "code",
        "outputId": "bcbfb5bb-7fe1-4c50-df91-3deb8d5ceebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "f\"\"\"\n",
        "This phrase is multiple sentenances long.\n",
        "There phrase can be formatted like simpler sentances,\n",
        "for example, I can still talk about my favorite Martial Art {basic_string}\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nThis phrase is multiple sentenances long.\\nThere phrase can be formatted like simpler sentances,\\nfor example, I can still talk about my favorite Martial Art Brazilian Jiu Jitsu-123\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBuMTMQWb239",
        "colab_type": "text"
      },
      "source": [
        "**Line Breaks Can Be Removed with Replace**\n",
        "\n",
        "The last long line contained line breaks, which are the **\\n** character, and they can be removed by using the replace method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8AVf6Pibz3H",
        "colab_type": "code",
        "outputId": "dad008f0-8a07-4c45-9d3e-96c6c6d55b80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "f\"\"\"\n",
        "This phrase is multiple sentenances long.\n",
        "There phrase can be formatted like simpler sentances,\n",
        "for example, I can still talk about my favorite Martial Art {basic_string}\n",
        "\"\"\".replace(\"\\n\", \"|\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'|This phrase is multiple sentenances long.|There phrase can be formatted like simpler sentances,|for example, I can still talk about my favorite Martial Art Brazilian Jiu Jitsu-123|'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eRmipgSocZ9i",
        "colab_type": "text"
      },
      "source": [
        "#### Numbers and Arithmetic Operations\n",
        "\n",
        "Python is also a built-in calculator. Without installing any additional libraries it can do many simple and complex arithmetic operations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xd6LxgEXcgP8",
        "colab_type": "text"
      },
      "source": [
        "**Adding and Subtracting Numbers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKwZGBoMb6_A",
        "colab_type": "code",
        "outputId": "d9d86b3f-5c2d-4019-8380-430ab622d5fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "steps = (1+1)-1\n",
        "print(f\"Two Steps Forward:  One Step Back = {steps}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Two Steps Forward:  One Step Back = 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMKS0ZYkdjvp",
        "colab_type": "text"
      },
      "source": [
        "**Multiplication with Decimals**\n",
        "\n",
        "Can use float type to solve decimal problems"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xgrw0LDucinh",
        "colab_type": "code",
        "outputId": "bbf89a19-6d0e-4d2a-b0c7-076f73dfeae7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "body_fat_percentage = 0.10\n",
        "weight = 200\n",
        "fat_total = body_fat_percentage * weight\n",
        "print(f\"I weight 200lbs, and {fat_total}lbs of that is fat\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I weight 200lbs, and 20.0lbs of that is fat\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjMkBnuxiCQW",
        "colab_type": "text"
      },
      "source": [
        "Can also use Decimal Library to set precision and deal with repeating decimal\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GN_PYDuDiBHg",
        "colab_type": "code",
        "outputId": "e4ffe66f-57f7-4642-93dc-5b2a5f279ac3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from decimal import (Decimal, getcontext)\n",
        "\n",
        "getcontext().prec = 6\n",
        "Decimal(1)/Decimal(3)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Decimal('0.333333')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1_B6OUrdxlU",
        "colab_type": "text"
      },
      "source": [
        "**Using Exponents**\n",
        "\n",
        "Using the Python math library it is straightforward to call 2 to the 3rd power"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2oC1HVhdmB9",
        "colab_type": "code",
        "outputId": "5110d69c-1ac9-435f-e622-c829dacbecab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import math\n",
        "math.pow(2,4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkPcDry7jWt-",
        "colab_type": "text"
      },
      "source": [
        "Can also use built in exponent operator to accomplish same thing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aV-R-DljXEj",
        "colab_type": "code",
        "outputId": "5c315017-2a5f-4ea3-afaf-e5b04478cbf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "2**3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOHXLdCU8R6i",
        "colab_type": "code",
        "outputId": "2fbbca70-3d59-4737-d256-83925ace735e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "2**4"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYA_mApaI-Y7",
        "colab_type": "text"
      },
      "source": [
        "this is regular multiplication"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6A_Vdvl_I8lg",
        "colab_type": "code",
        "outputId": "0decdece-bd94-4dcd-f172-1c06a3bef9ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "2*3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6gOf9qtd6Nt",
        "colab_type": "text"
      },
      "source": [
        "**Converting Between different numerical types**\n",
        "\n",
        "There are many numerical forms to be aware of in Python.\n",
        "A couple of the most common are:\n",
        "\n",
        "* Integers\n",
        "* Floats"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCWg7yX-d210",
        "colab_type": "code",
        "outputId": "d3247d47-06e5-480c-ee95-75a5bf87051d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "number = 100\n",
        "num_type = type(number).__name__\n",
        "print(f\"{number} is type [{num_type}]\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100 is type [int]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iW2f1Ik7eAzd",
        "colab_type": "code",
        "outputId": "87097b0e-b417-4a72-f41c-c73b291c8963",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "number = float(100)\n",
        "num_type = type(number).__name__\n",
        "print(f\"{number} is type [{num_type}]\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100.0 is type [float]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrlLg22C6KbR",
        "colab_type": "code",
        "outputId": "bbf61388-0cbb-4749-dc07-eebd223f9a35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "num2 = 100.20\n",
        "type(num2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dnSfoXZFQuP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Foo:pass\n",
        "f = Foo()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44v5W-HdFU4J",
        "colab_type": "code",
        "outputId": "8ee7ce84-2d53-4497-bba7-ddca3cc11b10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(f)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "__main__.Foo"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrrFXb99gQ1Z",
        "colab_type": "text"
      },
      "source": [
        "**Numbers can also be rounded**\n",
        "\n",
        "Python Built in round "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wjtnfol2iZDL",
        "colab_type": "code",
        "outputId": "68cae531-7145-4c95-d801-26fd00815904",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "too_many_decimals = 1.912345897\n",
        "round(too_many_decimals, 6)\n",
        "#get more info\n",
        "#round?"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.912346"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SuJDUTLFWUJz",
        "colab_type": "text"
      },
      "source": [
        "Numpy round"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEabuAGGWXIF",
        "colab_type": "code",
        "outputId": "b070ae58-007d-48a7-db1b-c46195dd3f81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "np.round(too_many_decimals, 6)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.912346"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3aHIe6qW8ab",
        "colab_type": "text"
      },
      "source": [
        "Pandas round"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-x_sjiZW-gB",
        "colab_type": "code",
        "outputId": "3837f9a2-aebd-472b-888b-5218edf60784",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame([too_many_decimals], columns=[\"A\"], index=[\"first\"])\n",
        "df.round(2)\n"
      ],
      "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>A</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>first</th>\n",
              "      <td>1.91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          A\n",
              "first  1.91"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elnncfa0XtOt",
        "colab_type": "text"
      },
      "source": [
        "Simple benchmark of all three (**Python**, **numpy** and **Pandas** round):   using **%timeit**\n",
        "\n",
        "*Depending on what is getting rounded (i.e. a very large DataFrame, performance may very, so knowing how to benchmark performance is important with round) *\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yr1SXcF5Xw-L",
        "colab_type": "code",
        "outputId": "987aed9e-a705-421f-8cc7-3ace4563a5dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "print(\"built in Python Round\")\n",
        "%timeit round(too_many_decimals, 2)\n",
        "\n",
        "print(\"numpy round\")\n",
        "%timeit np.round(too_many_decimals, 2)\n",
        "\n",
        "print(\"Pandas DataFrame round\")\n",
        "%timeit df.round(2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "built in Python Round\n",
            "The slowest run took 19.75 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1000000 loops, best of 3: 474 ns per loop\n",
            "numpy round\n",
            "The slowest run took 8.19 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "100000 loops, best of 3: 10.9 µs per loop\n",
            "Pandas DataFrame round\n",
            "1000 loops, best of 3: 1.03 ms per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuJ2OPSZiewO",
        "colab_type": "text"
      },
      "source": [
        "### Data Structures\n",
        "Python has a couple of core Data Structures that are used very frequently\n",
        "\n",
        "* Lists\n",
        "* Dictionaries\n",
        "\n",
        "Dictionaries and lists are the real workhorses of Python, but there are also other Data Structers like tuples, sets, Counters, etc, that are worth exploring too."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYrg60aboNTc",
        "colab_type": "text"
      },
      "source": [
        "#### Python Dictionaries\n",
        "\n",
        "The workhorse of Python datastructures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJgbX5wb7yHD",
        "colab_type": "text"
      },
      "source": [
        "##### Creating Python Dictionaries\n",
        "\n",
        "Creating Python Dictionaries can be done with* brackets {}*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPJxAjedZGGg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#bad_dictionary = {[2]:\"one\"}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtlC50y3GLam",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_dictionary = {\"one\":1}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEi6YTS2iZj7",
        "colab_type": "code",
        "outputId": "abf25071-510d-4e48-944b-c97690e9ead3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "submissions = {\"armbar\": \"upper_body\", \n",
        "               \"arm_triangle\": \"upper_body\", \n",
        "               \"heel_hook\": \"lower_body\", \n",
        "               \"knee_bar\": \"lower_body\"}\n",
        "#type(submissions)\n",
        "#submissions.items?\n",
        "submissions"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'arm_triangle': 'upper_body',\n",
              " 'armbar': 'upper_body',\n",
              " 'heel_hook': 'lower_body',\n",
              " 'knee_bar': 'lower_body'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4guKbqgfink8",
        "colab_type": "code",
        "outputId": "d3fae2a1-e64d-4c2f-e5d4-774611d993e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "new_dict =dict(upper_body=\"lower_body\")\n",
        "new_dict"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'upper_body': 'lower_body'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjNRS-YOjYxw",
        "colab_type": "text"
      },
      "source": [
        "##### Using Python Dictionaries\n",
        "A common dictionary usage pattern is to *iterate* on a dictionary by using the items method. In the example below the key and the value are printed:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oE1bzsiFGaOj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#submissions.items?\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kW1UR_yMi5bl",
        "colab_type": "code",
        "outputId": "97e71f4f-dc8a-46c1-d04e-dd970670e9fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "for submission, body_part in submissions.items():\n",
        "    print(f\"The {submission} is an attack on the {body_part}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The armbar is an attack on the upper_body\n",
            "The arm_triangle is an attack on the upper_body\n",
            "The heel_hook is an attack on the lower_body\n",
            "The knee_bar is an attack on the lower_body\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cue3xbzjfhF",
        "colab_type": "text"
      },
      "source": [
        "Dictionaries can also be used to *filter*.  In the example below, only the submission attacks on the lower body are displayed:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c00Rxx84HAES",
        "colab_type": "code",
        "outputId": "267e21d6-6225-46a3-de22-5cda10abba78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "for _, body_parts in submissions.items():\n",
        "  print(body_parts)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "upper_body\n",
            "upper_body\n",
            "lower_body\n",
            "lower_body\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buIoEo_Ajc1-",
        "colab_type": "code",
        "outputId": "61e5b2b9-0c95-449b-b890-6b521d620422",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "print(f\"These are lower_body submission attacks in Brazilian Jiu Jitsu:\")\n",
        "for submission, body_part in submissions.items():\n",
        "    if body_part == \"lower_body\":\n",
        "        print(submission)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "These are lower_body submission attacks in Brazilian Jiu Jitsu:\n",
            "heel_hook\n",
            "knee_bar\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jkR5VTHjsc2",
        "colab_type": "text"
      },
      "source": [
        "Dictionary keys and values can also be selected with built in *keys() * and *values()* methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SviUgzEfjjD-",
        "colab_type": "code",
        "outputId": "2c55eaf6-eb0a-4e02-9a06-4356bf4da84c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(f\"These are keys: {submissions.keys()}\")\n",
        "print(f\"These are values: {submissions.values()}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "These are keys: dict_keys(['armbar', 'arm_triangle', 'heel_hook', 'knee_bar'])\n",
            "These are values: dict_values(['upper_body', 'upper_body', 'lower_body', 'lower_body'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sgPM6V_slmoy",
        "colab_type": "text"
      },
      "source": [
        "Key lookup is very performant, and one of the most common ways to use a dictionary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOn5k2kRmk4K",
        "colab_type": "code",
        "outputId": "bf3e5261-fc77-44a9-fc01-281ba61d5520",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "if \"armbar\" in submissions:\n",
        "  print(\"found key\")\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "found key\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORI3ZxbqntAG",
        "colab_type": "code",
        "outputId": "230d438f-2ff7-44f8-e9c2-7868404befb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "print(\"timing key membership\")\n",
        "%timeit if \"armbar\" in submissions: pass "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "timing key membership\n",
            "The slowest run took 49.76 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "10000000 loops, best of 3: 37.2 ns per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IyHkox1Rj66y",
        "colab_type": "text"
      },
      "source": [
        "#### Python Lists\n",
        "\n",
        "Lists are also very commonly used in Python. They allow for sequential collections. Lists can hold dictionaries, just as dictionaries can hold lists."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xo7IMK567yHQ",
        "colab_type": "text"
      },
      "source": [
        "##### Creating Lists\n",
        "\n",
        "One way to create lists is with *[] syntax*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI-S0FStj1o4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_of_bjj_positions = [\"mount\", \"full-guard\", \"half-guard\", \n",
        "                         \"turtle\", \"side-control\", \"rear-mount\", \n",
        "                         \"knee-on-belly\", \"north-south\", \"open-guard\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUgrRDDsoobr",
        "colab_type": "text"
      },
      "source": [
        "Another method os creating lists is with built in *list()* method\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-3IP2FOw9yi",
        "colab_type": "code",
        "outputId": "3acc65b0-36af-4c18-d3fa-ce5d1b38e372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "bjj_dominant_positions = list()\n",
        "bjj_dominant_positions.append(\"side-control\")\n",
        "bjj_dominant_positions.append(\"mount\")\n",
        "bjj_dominant_positions\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['side-control', 'mount']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1gjso964T1T",
        "colab_type": "text"
      },
      "source": [
        "Yet another way, very performant way to create lists is to use list comprehension syntax"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QeQt8XD_4T-D",
        "colab_type": "code",
        "outputId": "509e2669-f5e3-4597-b6b0-7c819132d431",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "guards = \"full, half, open\"\n",
        "guard_list = [f\"{guard}-guard\" for guard in guards.split(\",\")]\n",
        "guard_list\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['full-guard', ' half-guard', ' open-guard']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBDIYoFB7yHS",
        "colab_type": "text"
      },
      "source": [
        "##### Using Lists\n",
        "\n",
        "For loops are one of the simplist ways to use a list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EoFWVpKYj94o",
        "colab_type": "code",
        "outputId": "b2c1bf0d-c14a-4279-cf42-6417156e0145",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "for position in list_of_bjj_positions:\n",
        "    if \"open\" in position: #explore on your own \"guard\"\n",
        "        print(position)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "open-guard\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAMxQJobkCy8",
        "colab_type": "text"
      },
      "source": [
        "Lists can also be used to select elements by slicing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zi6PKsRIkAl_",
        "colab_type": "code",
        "outputId": "cd374704-dc04-4de1-d0e9-21ca3df11570",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "print(f'First position: {list_of_bjj_positions[:1]}')\n",
        "print(f'Last position: {list_of_bjj_positions[-1:]}')\n",
        "print(f'First three positions: {list_of_bjj_positions[0:3]}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "First position: ['mount']\n",
            "Last position: ['open-guard']\n",
            "First three positions: ['mount', 'full-guard', 'half-guard']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgxwgI1I7Ex3",
        "colab_type": "text"
      },
      "source": [
        "Lists can also be used to unpack powerful, succinct statements when used with built-in functions like zip.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkaMbTx77ddw",
        "colab_type": "code",
        "outputId": "acb769f6-865d-4846-bd6e-009553d38dfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "bjj_position_matrix = [\n",
        "    [\"dominant\", \"top-mount\", \"back-mount\", \"side-control\"],\n",
        "    [\"neutral\", \"open-guard\", \"full-guard\", \"standing\"],\n",
        "    [\"weak\", \"turtle\", \"bottom-back-mount\", \"bottom-mount\"]\n",
        "]\n",
        "list(zip(*bjj_position_matrix))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('dominant', 'neutral', 'weak'),\n",
              " ('top-mount', 'open-guard', 'turtle'),\n",
              " ('back-mount', 'full-guard', 'bottom-back-mount'),\n",
              " ('side-control', 'standing', 'bottom-mount')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZA34gKvCi39",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "zip?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXalviiD7yHZ",
        "colab_type": "text"
      },
      "source": [
        "#### Python Sets\n",
        "\n",
        "Sets are unordered unique collections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1y5ahhJ699Gp",
        "colab_type": "text"
      },
      "source": [
        "##### Creating Python Sets\n",
        "\n",
        "Sets can be created by using built-in *sets()* method\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZT4d5Dxw9V4X",
        "colab_type": "code",
        "outputId": "37cd69e0-c0c1-49bf-b54b-8dfdfedff06a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "unique_attacks = set((\"armbar\",\"armbar\", \"armbar\", \"kimura\", \"kimura\"))\n",
        "print(type(unique_attacks))\n",
        "unique_attacks"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'set'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'armbar', 'kimura'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kj_JgH317yHZ",
        "colab_type": "text"
      },
      "source": [
        "##### Using Sets\n",
        "\n",
        "One of the most powerful ways to use sets is to find the differences between to collections"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbAUyYgk_c0s",
        "colab_type": "code",
        "outputId": "c967f1bc-fea3-4275-9801-c3b2f82d8f05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "attacks_set_one = set((\"armbar\", \"kimura\", \"heal-hook\"))\n",
        "attacks_set_two = set((\"toe-hold\", \"knee-bar\", \"heal-hook\"))\n",
        "unique_set_one_attacks = attacks_set_one - attacks_set_two\n",
        "print(f\"Unique Set One Attacks {unique_set_one_attacks}\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unique Set One Attacks {'armbar', 'kimura'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTmBkJG0Xud_",
        "colab_type": "text"
      },
      "source": [
        "Question:  \n",
        "\n",
        "Q: set() is used to select unique values. what is its performance for a deep learning large data sets. in large data sets, if set() is not performant enough, what are the alternatives?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wY-GUQc3te-",
        "colab_type": "text"
      },
      "source": [
        "## Part 1.2 Functions \n",
        "\n",
        "* [Read related material covered in Chapter 1 (Functions Section) of Pragmatic AI](https://www.safaribooksonline.com/library/view/pragmatic-ai-an/9780134863924/ch01.xhtml#ch01lev1sub17)\n",
        "\n",
        "* [Watch video section 2:  Writing and Applying Functions](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118/9780135261118-EMLA_01_02_00) \n",
        "\n",
        "*  **Writing Functions**\n",
        "*  Function arguments:  positional, keyword\n",
        "*  Functional Currying:  Passing uncalled functions\n",
        "*  Functions that Yield\n",
        "*  Decorators:  Functions that wrap other functions\n",
        "*  Making Classes Behave Like Functions\n",
        "*  Applying a Function to a Pandas DataFrame\n",
        "*  Writing Lambdas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JmjS2H33wZP",
        "colab_type": "text"
      },
      "source": [
        "#### Writing Functions\n",
        "Learning to write a function is the most fundamental skill to learn in Python.  With a basic mastery of functions, it is possible to have an almost full command of the language."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHsiAy8N3-Cj",
        "colab_type": "text"
      },
      "source": [
        "**Simple function**\n",
        "\n",
        "The simplest functions just return a value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsngH1Yh4BP3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def favorite_martial_art():\n",
        "    return \"bjj\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hL4eSHZjjLSe",
        "colab_type": "code",
        "outputId": "15c09d60-072f-477c-a045-6dc073ddb5cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(favorite_martial_art())\n",
        "# This is the same output\n",
        "my_variable = \"bjj\"\n",
        "my_variable"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "bjj\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'bjj'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lq9VQm0V4BTE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def myfunc():pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCYhFgvvjGHt",
        "colab_type": "code",
        "outputId": "b7b78b63-f38a-4b96-d6bb-e175fb397e9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "res = myfunc()\n",
        "print(res)\n",
        "#result = myfunc()\n",
        "#print(result)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMPU657F4JWF",
        "colab_type": "text"
      },
      "source": [
        "**Documenting Functions**\n",
        "\n",
        "It is a very good idea to document functions.  \n",
        "In Jupyter Notebook and IPython docstrings can be viewed by referring to the function with a ?.  ie.\n",
        "\n",
        "```\n",
        "In [2]: favorite_martial_art_with_docstring?\n",
        "Signature: favorite_martial_art_with_docstring()\n",
        "Docstring: This function returns the name of my favorite martial art\n",
        "File:      ~/src/functional_intro_to_python/<ipython-input-1-bef983c31735>\n",
        "Type:      function\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKOHmIxg4Ply",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def favorite_martial_art_with_docstring():\n",
        "    \"\"\"This function returns the name of my favorite martial art\n",
        "    This is more\n",
        "    This is even more\n",
        "    return \"string\"\n",
        "    \"\"\"\n",
        "    return \"bjj\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OW1SbDg4Rd1",
        "colab_type": "text"
      },
      "source": [
        "**Docstrings of functions can be printed out by referring to *```__doc__```*** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SuI6CO-E4ViO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#favorite_martial_art_with_docstring.__doc__\n",
        "favorite_martial_art_with_docstring?\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeanvrdvKTV6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#favorite_martial_art_with_docstring?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ew4-TwH84YJa",
        "colab_type": "text"
      },
      "source": [
        "#### Function arguments: positional, keyword\n",
        "\n",
        "A function is most useful when arguments are passed to the function. New values for times are processed inside the function. This function is also a 'positional' argument, vs a keyword argument. Positional arguments are processed in the order they are created in."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxL-hyuL4aws",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def practice(times):\n",
        "    print(f\"I like to practice {times} times a day\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqDUN33f4cyf",
        "colab_type": "code",
        "outputId": "9aebaabe-b88c-4f2a-ed3d-8ffd6819d6cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "practice(2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I like to practice 2 times a day\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8h7Jo0y4ggH",
        "colab_type": "code",
        "outputId": "048c9a70-9971-4b0b-9bf3-7cb7845ebbee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "practice(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I like to practice 3 times a day\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LILwF-Nt4iTm",
        "colab_type": "text"
      },
      "source": [
        "**Positional Arguments are processed in order**\n",
        "\n",
        "Note, *position* is the key to pay attention to.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aw6DSXR64oVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def practice(times, technique, duration):\n",
        "    print(f\"I like to practice {technique}, {times} times a day, for {duration} minutes\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Gq1XUkB4qtM",
        "colab_type": "code",
        "outputId": "71cd602e-8b80-4755-bc02-d391d174a23e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "practice(3, \"piano\", 45)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I like to practice piano, 3 times a day, for 45 minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sv9WBxWzuWAy",
        "colab_type": "code",
        "outputId": "7c4b3907-e688-453a-bec5-e45d59c6d3a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Order is important, now the entire is incorrect and prints out nonsense\n",
        "practice(\"piano\", 7,60)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I like to practice 7, piano times a day, for 60 minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ntX-r2Bp4uTw",
        "colab_type": "text"
      },
      "source": [
        "**Keyword Arguments are processed by key, value and can have default values**\n",
        "\n",
        "One handy feature of keyword arguments is that you can set defaults and only change the defaults you want to change."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSIg9mKs4zg_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def practice(times=2, technique=\"python\", duration=60):\n",
        "    print(f\"I like to practice {technique}, {times} times a day, for {duration} minutes\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Be5uDA_d41dg",
        "colab_type": "code",
        "outputId": "54936603-cf64-4439-c01f-6b08c56ff7e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "practice()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I like to practice python, 2 times a day, for 60 minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOwB-4Dz43HQ",
        "colab_type": "code",
        "outputId": "6f2b0502-eb95-4fd6-a69d-3a41f59f9bb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "practice(duration=90, times=4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I like to practice python, 4 times a day, for 90 minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wBL_tQ846Qd",
        "colab_type": "text"
      },
      "source": [
        "*****args and ****kwargs\n",
        "\n",
        "allow dynamic argument passing to functions\n",
        "Should be used with discretion because it can make code hard to understand"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJtpL1Ad5IUH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def attack_techniques(**kwargs):\n",
        "    \"\"\"This accepts any number of keyword arguments\"\"\"\n",
        "    \n",
        "    for name, attack in kwargs.items():\n",
        "        print(f\"This is an attack I would like to practice: {attack}\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODHgMleb5KVa",
        "colab_type": "code",
        "outputId": "cc9b835d-3d77-4346-ad0f-9150e42efa46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "attack_techniques(arm_attack=\"kimura\", \n",
        "                  leg_attack=\"straight_ankle_lock\", \n",
        "                  neck_attack=\"arm_triangle\",\n",
        "                 body_attack=\"charge\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is an attack I would like to practice: kimura\n",
            "This is an attack I would like to practice: straight_ankle_lock\n",
            "This is an attack I would like to practice: arm_triangle\n",
            "This is an attack I would like to practice: charge\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rdvgasCNvwKt",
        "colab_type": "code",
        "outputId": "d3f93844-b76a-4726-cb77-298548750e03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "#I also can pass as many things as I wants\n",
        "attack_techniques(arm_attack=\"kimura\", \n",
        "                  leg_attack=\"straight_ankle_lock\", \n",
        "                  neck_attach=\"arm_triangle\",\n",
        "                  attack4=\"rear nake choke\", attack5=\"key lock\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is an attack I would like to practice: kimura\n",
            "This is an attack I would like to practice: straight_ankle_lock\n",
            "This is an attack I would like to practice: arm_triangle\n",
            "This is an attack I would like to practice: rear nake choke\n",
            "This is an attack I would like to practice: key lock\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Bd0PwiD5NnI",
        "colab_type": "text"
      },
      "source": [
        "**passing dictionary of keywords to function**\n",
        "\n",
        "**kwargs syntax can also be used to pass in arguments all at once"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rxjc3U7-5QlB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "attacks = {\"arm_attack\":\"kimura\", \n",
        "           \"leg_attack\":\"straight_ankle_lock\", \n",
        "           \"neck_attach\":\"arm_triangle\"}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BmMRBOC5SG-",
        "colab_type": "code",
        "outputId": "fa346c95-3a78-4e6b-e3db-f934e3e1f944",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "attack_techniques(**attacks)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is an attack I would like to practice: kimura\n",
            "This is an attack I would like to practice: straight_ankle_lock\n",
            "This is an attack I would like to practice: arm_triangle\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Srgz3C65Vdk",
        "colab_type": "text"
      },
      "source": [
        "**Passing Around Functions**\n",
        "\n",
        "Object-Oriented programming is a very popular way to program, but it isn't the only style available in Python. For concurrency and for Data Science, functional programming fits as a complementary style.\n",
        "\n",
        "In the example, below a function can be used inside of another function by being passed into the function itself as an argument."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inzYwQSq5XPy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def attack_location(technique):\n",
        "    \"\"\"Return the location of an attack\"\"\"\n",
        "    \n",
        "    attacks = {\"kimura\": \"arm_attack\",\n",
        "           \"straight_ankle_lock\":\"leg_attack\", \n",
        "           \"arm_triangle\":\"neck_attach\"}\n",
        "    if technique in attacks:\n",
        "        return attacks[technique]\n",
        "    return \"Unknown\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfnYsD895ZFC",
        "colab_type": "code",
        "outputId": "470499d9-5026-45a7-b733-58f4ee8d6605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "attack_location(\"kimura\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'arm_attack'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eK6NR4F-5aqT",
        "colab_type": "code",
        "outputId": "960f8d44-317c-4ad0-f7d7-0ad710dfdc99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "attack_location(\"bear hug\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Unknown'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOARK2b65f-k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def multiple_attacks(attack_location_function):\n",
        "    \"\"\"Takes a function that categorizes attacks and returns location\"\"\"\n",
        "    \n",
        "    new_attacks_list = [\"rear_naked_choke\", \"americana\", \"kimura\"]\n",
        "    for attack in new_attacks_list:\n",
        "        attack_location = attack_location_function(attack)\n",
        "        print(f\"The location of attack {attack} is {attack_location}\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzgM8VU85iju",
        "colab_type": "code",
        "outputId": "925b64ea-81a4-4ed3-a839-3bc1c680ab79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "multiple_attacks(attack_location)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The location of attack rear_naked_choke is Unknown\n",
            "The location of attack americana is Unknown\n",
            "The location of attack kimura is arm_attack\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCdDA35S5mQ4",
        "colab_type": "text"
      },
      "source": [
        "#### Closures and Functional Currying\n",
        "\n",
        "Closures are functions that contain other nested functions with state from outer function.\n",
        "\n",
        "In Python, a common way to use them is to keep track of the state. In the example below, the outer function, attack_counter keeps track of counts of attacks. The inner fuction attack_filter uses the \"nonlocal\" keyword in Python3, to modify the variable in the outer function.\n",
        "\n",
        "This approach is called \"functional currying\". It allows for a specialized function to be created from general functions. As shown below, this style of function could be the basis of a simple video game or maybe for the statistics crew of a mma match."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QeG9UZfL6THb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#nonlocal cannot modify this variable\n",
        "#lower_body_counter=5\n",
        "def attack_counter():\n",
        "    \"\"\"Counts number of attacks on part of body\"\"\"\n",
        "    lower_body_counter = 0\n",
        "    upper_body_counter = 0\n",
        "    #print(lower_body_counter)\n",
        "    def attack_filter(attack):\n",
        "        nonlocal lower_body_counter\n",
        "        nonlocal upper_body_counter\n",
        "        attacks = {\"kimura\": \"upper_body\",\n",
        "           \"straight_ankle_lock\":\"lower_body\", \n",
        "           \"arm_triangle\":\"upper_body\",\n",
        "            \"keylock\": \"upper_body\",\n",
        "            \"knee_bar\": \"lower_body\"}\n",
        "        if attack in attacks:\n",
        "            if attacks[attack] == \"upper_body\":\n",
        "                upper_body_counter +=1\n",
        "            if attacks[attack] == \"lower_body\":\n",
        "                lower_body_counter +=1\n",
        "        print(f\"Upper Body Attacks {upper_body_counter}, Lower Body Attacks {lower_body_counter}\")\n",
        "    return attack_filter"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YT_Tk-iT6WiR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fight = attack_counter()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1p7hvGb6Y3m",
        "colab_type": "code",
        "outputId": "a103055f-8677-4c16-eeda-00acc21eab75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "fight(\"kimura\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Upper Body Attacks 1, Lower Body Attacks 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCoFnKDJ6aTS",
        "colab_type": "code",
        "outputId": "12f6606f-3f85-4ac7-9857-cef4ab781dff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "fight(\"knee_bar\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Upper Body Attacks 1, Lower Body Attacks 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3MRMBkS6bmy",
        "colab_type": "code",
        "outputId": "414628a7-578f-4b6e-f4b4-860fa76a4887",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "fight(\"keylock\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Upper Body Attacks 2, Lower Body Attacks 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZx708yd7lpU",
        "colab_type": "text"
      },
      "source": [
        "#### Partial Functions\n",
        "\n",
        "Useful to partial assign default values to functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOhajVSK7oeX",
        "colab_type": "code",
        "outputId": "0868e441-8d64-4ba1-ae73-8c415d8c0a87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from functools import partial\n",
        "\n",
        "def multiple_attacks(attack_one, attack_two):\n",
        "  \"\"\"Performs two attacks\"\"\"\n",
        "  \n",
        "  print(f\"First Attack {attack_one}\")\n",
        "  print(f\"Second Attack {attack_two}\")\n",
        "  \n",
        "attack_this = partial(multiple_attacks, \"kimura\")\n",
        "type(attack_this)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "functools.partial"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymZDwSka7rrh",
        "colab_type": "text"
      },
      "source": [
        "By using this partial function, only one argument is needed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJv6HePw7tkH",
        "colab_type": "code",
        "outputId": "ee29befa-63c1-44d1-fcc6-f3a4205de01b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "attack_this(\"knee-bar\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "First Attack kimura\n",
            "Second Attack knee-bar\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CIWXwv27uPW",
        "colab_type": "text"
      },
      "source": [
        "Alternately, the original function can also be called with a different two attacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVudEj-h7wO_",
        "colab_type": "code",
        "outputId": "c53546e4-4d3b-43c5-bfaf-a228bcebe235",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "multiple_attacks(\"Darce Choke\", \"Bicep Slicer\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "First Attack Darce Choke\n",
            "Second Attack Bicep Slicer\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-tKvYXt6ftS",
        "colab_type": "text"
      },
      "source": [
        "#### Lazy Evaluated Functions (Generators)\n",
        "\n",
        "A very useful style of programming is \"lazy evaluation\". A generator is an example of that. Generators yield an items at a time.\n",
        "\n",
        "The example below return an \"infinite\" random sequence of attacks. The lazy portion comes into play in that while there is an infinite amount of values, they are only returned when the function is called."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-hOWZyV6jCt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def lazy_return_random_attacks():\n",
        "    \"\"\"Yield attacks each time\"\"\"\n",
        "    import random\n",
        "    attacks = {\"kimura\": \"upper_body\",\n",
        "           \"straight_ankle_lock\":\"lower_body\", \n",
        "           \"arm_triangle\":\"upper_body\",\n",
        "            \"keylock\": \"upper_body\",\n",
        "            \"knee_bar\": \"lower_body\"}\n",
        "    while True:\n",
        "        random_attack = random.choices(list(attacks.keys()))\n",
        "        yield random_attack"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrhrDOAL6u0b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "attack = lazy_return_random_attacks()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyxMi57a6wA4",
        "colab_type": "code",
        "outputId": "286063a0-8cde-4376-bd2c-275a85685ad5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(attack)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "generator"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0IxoCkJu6y-B",
        "colab_type": "code",
        "outputId": "f421e5d8-9d92-4e7c-899e-3579587c108d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "for _ in range(6):\n",
        "    print(next(attack))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['straight_ankle_lock']\n",
            "['keylock']\n",
            "['knee_bar']\n",
            "['arm_triangle']\n",
            "['arm_triangle']\n",
            "['knee_bar']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hlJXHUVq6391",
        "colab_type": "text"
      },
      "source": [
        "#### Decorators:   Functions that wrap other functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KQ2Cmcelcy-",
        "colab_type": "text"
      },
      "source": [
        "##### Randomized Sleep Decorator\n",
        "\n",
        "Another useful technique in Python is to use the decorator syntax to wrap one function with another function. In the example below, a decorator is written that adds random sleep to each function call. When combined with the previous \"infinite\" attack generator, it generates random sleeps between each function call."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvNzAVBT6695",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def randomized_speed_attack_decorator(function):\n",
        "    \"\"\"Randomizes the speed of attacks\"\"\"\n",
        "    \n",
        "    import time\n",
        "    import random\n",
        "    \n",
        "    def wrapper_func(*args, **kwargs):\n",
        "        sleep_time = random.randint(0,3)\n",
        "        print(f\"Attacking after {sleep_time} seconds\")\n",
        "        time.sleep(sleep_time)\n",
        "        return function(*args, **kwargs)\n",
        "    return wrapper_func"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BGrwIl96-PX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@randomized_speed_attack_decorator\n",
        "def lazy_return_random_attacks():\n",
        "    \"\"\"Yield attacks each time\"\"\"\n",
        "    import random\n",
        "    attacks = {\"kimura\": \"upper_body\",\n",
        "           \"straight_ankle_lock\":\"lower_body\", \n",
        "           \"arm_triangle\":\"upper_body\",\n",
        "            \"keylock\": \"upper_body\",\n",
        "            \"knee_bar\": \"lower_body\"}\n",
        "    while True:\n",
        "        random_attack = random.choices(list(attacks.keys()))\n",
        "        yield random_attack"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAEY8I7M7AL5",
        "colab_type": "code",
        "outputId": "ffeedffa-b321-45cb-e112-8b844b801c53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        }
      },
      "source": [
        "for _ in range(5):\n",
        "    print(next(lazy_return_random_attacks()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Attacking after 1 seconds\n",
            "['arm_triangle']\n",
            "Attacking after 3 seconds\n",
            "['arm_triangle']\n",
            "Attacking after 2 seconds\n",
            "['knee_bar']\n",
            "Attacking after 2 seconds\n",
            "['arm_triangle']\n",
            "Attacking after 2 seconds\n",
            "['kimura']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qO-uoYUClvqr",
        "colab_type": "text"
      },
      "source": [
        "##### Timing Decorator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McePAV81l2Cm",
        "colab_type": "text"
      },
      "source": [
        "Using a decorator to time code is very common"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRtnp32Rl5jt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from functools import wraps\n",
        "from time import time\n",
        "\n",
        "def timing(f):\n",
        "    @wraps(f)\n",
        "    def wrap(*args, **kw):\n",
        "        ts = time()\n",
        "        result = f(*args, **kw)\n",
        "        te = time()\n",
        "        print(f\"fun: {f.__name__}, args: [{args}, {kw}] took: {te-ts} sec\")\n",
        "        return result\n",
        "    return wrap"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFisqs8tmJer",
        "colab_type": "text"
      },
      "source": [
        "Using decorator to time execution of a function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3B4RLEbmpie",
        "colab_type": "code",
        "outputId": "d519db4e-0b34-450d-fd55-3f59adfbc4cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "@timing\n",
        "def some_attacks():\n",
        "  attack = lazy_return_random_attacks()\n",
        "  for _ in range(5):\n",
        "    print(next(attack))\n",
        "    \n",
        "some_attacks()\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Attacking after 3 seconds\n",
            "['arm_triangle']\n",
            "['keylock']\n",
            "['straight_ankle_lock']\n",
            "['arm_triangle']\n",
            "['arm_triangle']\n",
            "fun: some_attacks, args: [(), {}] took: 3.0046119689941406 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WT4sChux989o",
        "colab_type": "text"
      },
      "source": [
        "#### Making Classes Behave Like Functions\n",
        "\n",
        "Creating callable functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2ykyfI79_6E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AttackFinder:\n",
        "  \"\"\"Finds the attack location\"\"\"\n",
        "  \n",
        "  \n",
        "  def __init__(self, attack):\n",
        "    self.attack = attack\n",
        "  \n",
        "  def __call__(self):\n",
        "    attacks = {\"kimura\": \"upper_body\",\n",
        "           \"straight_ankle_lock\":\"lower_body\", \n",
        "           \"arm_triangle\":\"upper_body\",\n",
        "            \"keylock\": \"upper_body\",\n",
        "            \"knee_bar\": \"lower_body\"}\n",
        "    if not self.attack in attacks:\n",
        "      return \"unknown location\"\n",
        "    return attacks[self.attack]\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKyd_XhOJCSk",
        "colab_type": "code",
        "outputId": "de2dd8ec-e552-4b5e-d753-499ad8968df2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "my_attack = AttackFinder(\"kimura\")\n",
        "my_attack()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'upper_body'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQpf0vu_-Cnk",
        "colab_type": "text"
      },
      "source": [
        "#### Applying Functions to Pandas DataFrames\n",
        "\n",
        "The final lesson on functions is to take this knowledge and use it on a DataFrame in Pandas. One of the more fundamental concepts in Pandas is use apply on a column vs iterating through all of the values. An example is shown below where all of the numbers are rounded to a whole digit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLWOOacP-KDz",
        "colab_type": "code",
        "outputId": "553cd572-2ea9-475d-f847-30c09f5bc05e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "import pandas as pd\n",
        "iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
        "iris.head(3)"
      ],
      "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZTzZku3hZFM",
        "colab_type": "code",
        "outputId": "3e1c80de-1000-462b-c87d-81811c0fa17c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "iris.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8jXLfVq-Nrb",
        "colab_type": "code",
        "outputId": "d98f0e4f-1eb3-484e-c0de-6de1f8946211",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "iris['rounded_sepal_length'] = iris[['sepal_length']].apply(pd.Series.round)\n",
        "iris.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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "      <th>rounded_sepal_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  ...  species  rounded_sepal_length\n",
              "0           5.1          3.5  ...   setosa                   5.0\n",
              "1           4.9          3.0  ...   setosa                   5.0\n",
              "2           4.7          3.2  ...   setosa                   5.0\n",
              "3           4.6          3.1  ...   setosa                   5.0\n",
              "4           5.0          3.6  ...   setosa                   5.0\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBxKbJk8kjit",
        "colab_type": "code",
        "outputId": "1d833eb4-a426-44b9-9d80-e88360e6a12b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "iris.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QrQiWvRC-QY9",
        "colab_type": "text"
      },
      "source": [
        "This was done with a built in function, but a custom function can also be written and applied to a column. In the example below, the values are multiplied by 100. The alternative way to accomplish this would be to create a loop, transform the data and then write it back. In Pandas, it is straightforward and simple to apply custom functions instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6WlPQ823-SMS",
        "colab_type": "code",
        "outputId": "4c2dca6e-2767-4f11-bfd6-8b9e019b65f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "def multiply_by_100(x):\n",
        "    \"\"\"Multiplies by 100\"\"\"\n",
        "    \n",
        "    res = x * 100\n",
        "    #print(f\"This was passed in {x}, and this result was generated {res}\")\n",
        "    return res\n",
        "  \n",
        "  \n",
        "iris['100x_sepal_length'] = iris[['sepal_length']].apply(multiply_by_100)\n",
        "iris.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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "      <th>rounded_sepal_length</th>\n",
              "      <th>100x_sepal_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>510.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>490.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>470.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>460.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  ...  rounded_sepal_length  100x_sepal_length\n",
              "0           5.1          3.5  ...                   5.0              510.0\n",
              "1           4.9          3.0  ...                   5.0              490.0\n",
              "2           4.7          3.2  ...                   5.0              470.0\n",
              "3           4.6          3.1  ...                   5.0              460.0\n",
              "4           5.0          3.6  ...                   5.0              500.0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYg2Lz05ly7Q",
        "colab_type": "code",
        "outputId": "9b5ac39e-3399-4d9e-eeb7-8c6fad43bf3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        }
      },
      "source": [
        "iris[\"new_column\"] = iris[['sepal_length']]\n",
        "iris.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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "      <th>rounded_sepal_length</th>\n",
              "      <th>100x_sepal_length</th>\n",
              "      <th>new_column</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>510.0</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>470.0</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "      <td>5.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  ...  100x_sepal_length  new_column\n",
              "0           5.1          3.5  ...              510.0         5.1\n",
              "1           4.9          3.0  ...              490.0         4.9\n",
              "2           4.7          3.2  ...              470.0         4.7\n",
              "3           4.6          3.1  ...              460.0         4.6\n",
              "4           5.0          3.6  ...              500.0         5.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T66Ll8etnuxK",
        "colab_type": "code",
        "outputId": "1d90cf4e-d9d1-4464-bdef-30a6da2b1e34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        }
      },
      "source": [
        "iris.groupby(\"species\").max()"
      ],
      "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>rounded_sepal_length</th>\n",
              "      <th>100x_sepal_length</th>\n",
              "      <th>new_column</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>species</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>setosa</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>580.0</td>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>versicolor</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>700.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>virginica</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>790.0</td>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            sepal_length  sepal_width  ...  100x_sepal_length  new_column\n",
              "species                                ...                               \n",
              "setosa               5.8          4.4  ...              580.0         5.8\n",
              "versicolor           7.0          3.4  ...              700.0         7.0\n",
              "virginica            7.9          3.8  ...              790.0         7.9\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nerC1KunT8u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#iris.apply(pd.Series.round, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PaPz8q3pjWZQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#def sepal_category(x):\n",
        "  \n",
        "#  if x == 4:\n",
        "#  return \"big\"\n",
        "\n",
        "#iris['sepal_category'] = iris[['sepal_width']].apply(sepal_category)\n",
        "#iris.head()\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gq3i_yeDWWHt",
        "colab_type": "code",
        "outputId": "fea43304-cf2a-4a76-acd1-bdeb66e51476",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "#example of a smarter function\n",
        "def smart_multiply_by_100(x):\n",
        "  if x > 5:\n",
        "    return 1\n",
        "  return x\n",
        "\n",
        "inputs = [1,2,6,10]\n",
        "for input in inputs:\n",
        "  print(smart_multiply_by_100(input))\n",
        "  \n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "1\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ox8_LbHW_vQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C95FIjW5-rJc",
        "colab_type": "text"
      },
      "source": [
        "#### Writing Lambdas\n",
        "\n",
        "Generally considered to be unnecessary.  A Python lambda is an inline python and it can often lead to confusing code.  \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Zibl40rD6Qg",
        "colab_type": "code",
        "outputId": "cb2b08c7-6009-4bc6-e28d-3d1c4f436897",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEvuisbJ-tc2",
        "colab_type": "code",
        "outputId": "717a48c7-fcb9-4db4-f1d0-c1d4b990fc9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "func = lambda x: x**2\n",
        "func(4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cq9-b_1N-tpI",
        "colab_type": "code",
        "outputId": "2efdb7db-759c-45ca-d8f0-54f152cbe534",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "def regular_func(x):\n",
        "  return x**2\n",
        "\n",
        "regular_func(4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlIAJo2GK7KL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def regular_func2(x):\n",
        "  \"\"\"This makes my variable go to the second power\"\"\"\n",
        "  return x**2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOMmTXtSLF9W",
        "colab_type": "code",
        "outputId": "4089a1d4-a04b-4e56-c949-2496b8740d60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "regular_func2(2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnXLQ6rZLKj5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QS-zJFR-DlPI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YAcj5rn5DnEI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "random.seed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1EBc2ohAonjw",
        "colab_type": "text"
      },
      "source": [
        "## Homework Excercises\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10NtU6hV4nT6",
        "colab_type": "text"
      },
      "source": [
        "### Watch Colab Videos on Safari Live Lesson:  *Essential Machine Learning and AI with Python and Jupyter Notebook*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwCKU8ug4sNB",
        "colab_type": "text"
      },
      "source": [
        "Get Familiar with Colab and Master basic and advanced Features\n",
        "\n",
        "* [12.1 Perform Colaboratory basics ](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118/9780135261118-EMLA_01_12_01)\n",
        "\n",
        "* [12.2 Use Advanced Colab Features](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118/9780135261118-EMLA_01_12_02)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdm3kA-Fo4pI",
        "colab_type": "text"
      },
      "source": [
        "### Can you Return random attacks without repeating?\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "btp0LY4U5fhY",
        "colab_type": "text"
      },
      "source": [
        "* keep track of previous calls and run random again if it finds a previous\n",
        "\n",
        "```\n",
        "def lazy_return_random_attacks():\n",
        "    \"\"\"Yield attacks each time\"\"\"\n",
        "    import random\n",
        "    attacks = {\"kimura\": \"upper_body\",\n",
        "           \"straight_ankle_lock\":\"lower_body\", \n",
        "           \"arm_triangle\":\"upper_body\",\n",
        "            \"keylock\": \"upper_body\",\n",
        "            \"knee_bar\": \"lower_body\"}\n",
        "    while True:\n",
        "        random_attack = random.choices(list(attacks.keys()))\n",
        "        yield random_attack\n",
        "        \n",
        "```\n",
        "\n",
        "pseudo code:  if attack in previous_attacks:\n",
        "                              ...new random\n",
        "                          random_attack    "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTj3ny5vo8GN",
        "colab_type": "code",
        "outputId": "fcc7db55-7d83-4807-dd58-a2f9585549a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def func():\n",
        "  l = [1,2,3]\n",
        "  return l\n",
        "func()\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLKSr48YKgSR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def lazy_func():\n",
        "  l = [1,2,3]\n",
        "  for item in l:\n",
        "    yield item\n",
        "gen = lazy_func()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4mJZ3TnKmbd",
        "colab_type": "code",
        "outputId": "ed742705-ba31-42bf-8415-0cfb1c165866",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "next(gen)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "StopIteration",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-162-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mStopIteration\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IoYYSGXKsnR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EO5CzCfH5ejj",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Q_zzw-nXHWE",
        "colab_type": "code",
        "outputId": "8a78d301-8ed9-4bdb-9ebe-48bebc39d752",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "def myfunc():\n",
        "  return \"apple\"\n",
        "\n",
        "def myfunc2():\n",
        "  return 1\n",
        "\n",
        "print(myfunc())\n",
        "print(myfunc2())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "apple\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-gzLbJfXnuf",
        "colab_type": "code",
        "outputId": "24b7af1d-83cb-4bce-b312-1ee806974ea1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        }
      },
      "source": [
        "def practise(times):\n",
        "    print(f\"I like to exercise {times} a day\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-a3e816d8793c>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(f\"I like to exercise {times} a day\")\u001b[0m\n\u001b[0m                                            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0F-jzlBYeOKC",
        "colab_type": "text"
      },
      "source": [
        "## FAQ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "anKepcmNg4TE",
        "colab_type": "text"
      },
      "source": [
        "### Q:  Magic Commands"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSSaqHFXg7wI",
        "colab_type": "code",
        "outputId": "1522a1b3-21d6-4bc1-9f70-5bd4a428d1b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#shell command (import subprocess)\n",
        "!pwd"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcX2XQAphB2c",
        "colab_type": "code",
        "outputId": "983bf4d1-cb09-49d3-b68c-db934735597e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# magic\n",
        "%%python2\n",
        "print \"hello\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJi5VdlqhHlG",
        "colab_type": "code",
        "outputId": "8ad61fe5-8f18-4c4c-f336-1e828c6fba14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "#built into IPython\n",
        "who"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AttackFinder\t Decimal\t Foo\t Use_Python\t alt\t attack\t attack_counter\t attack_location\t attack_one\t \n",
            "attack_techniques\t attack_this\t attack_two\t attacks\t attacks_set_one\t attacks_set_two\t basic_string\t belt\t belts\t \n",
            "bjj_dominant_positions\t bjj_position_matrix\t body_fat_percentage\t body_part\t body_parts\t cars\t data\t df\t drive\t \n",
            "f\t fat_total\t favorite_martial_art\t favorite_martial_art_with_docstring\t fight\t files\t func\t getcontext\t guard_list\t \n",
            "guards\t iris\t item\t items\t lazy_return_random_attacks\t list_of_bjj_positions\t math\t multiple_attacks\t multiply_by_100\t \n",
            "my_attack\t my_string\t my_variable\t myfunc\t new_dict\t new_dictionary\t np\t num_type\t number\t \n",
            "os\t partial\t pd\t position\t practice\t randomized_speed_attack_decorator\t regular_func\t regular_func2\t res\t \n",
            "some_attacks\t steps\t string_with_hyphen\t submission\t submissions\t sys\t time\t timing\t too_many_decimals\t \n",
            "unique_attacks\t unique_set_one_attacks\t uploaded\t var1\t variable\t weight\t wraps\t \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7z3bvOVhO0D",
        "colab_type": "code",
        "outputId": "58511e2e-b76f-485f-f936-a5354bc04483",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#built in python function\n",
        "l = [1]\n",
        "len(l)\n",
        "max(l)\n",
        "min(l)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QtSOcXrOh62G",
        "colab_type": "text"
      },
      "source": [
        "### None"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQgi83s0h9Po",
        "colab_type": "code",
        "outputId": "7b22bd9f-d18d-464c-d5b0-2bbf79155dd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "var = None\n",
        "if var == None:\n",
        "  print(\"this is None\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "this is None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVCOi8BYiEyk",
        "colab_type": "code",
        "outputId": "8a37c934-52af-44a7-b6a6-06e6859226e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ll = []\n",
        "ll == None\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jG30QNNpiMpd",
        "colab_type": "code",
        "outputId": "03e1bb34-8bd3-4d21-cf78-d0d31074ed5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ll == True"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAV0VvUDiU-N",
        "colab_type": "code",
        "outputId": "dab18184-b775-4005-e247-5bf427fb547b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "def my_func():\n",
        "  return 1\n",
        "my_func() == None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ms2AzZCPindt",
        "colab_type": "code",
        "outputId": "315e3eb0-1948-480a-e396-1c1022b64501",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "def my_func2():\n",
        "  \"\"\"Returns None\"\"\"\n",
        "  pass\n",
        "my_func2() == None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01P38heKj_H_",
        "colab_type": "text"
      },
      "source": [
        "### Next"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbwF5dzaj-R0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def funky():\n",
        "  l = [1,2,3]\n",
        "  for item in l:\n",
        "    yield item\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAVSHP_kkMhC",
        "colab_type": "code",
        "outputId": "aec43c1f-2d43-4369-ade6-05c136ec31e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "f = funky()\n",
        "next(f)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Og3Yn8uJkQ4w",
        "colab_type": "code",
        "outputId": "a3681e06-477d-4e88-f25b-e5bf87cfa702",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "next(f)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qteWFePOkYwF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##check value\n",
        "val = False\n",
        "if val == True:\n",
        "  print(\"yes\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_pr0ArieP7-",
        "colab_type": "text"
      },
      "source": [
        "### Q:  Why are Python Dictionaries so commonly used vs other data structures?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0k7pv2zFeZ2p",
        "colab_type": "text"
      },
      "source": [
        "A:  They are highly performant, easy to use, and flexible to program with"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laOR73PneuHn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3xpdAnD3aqo",
        "colab_type": "text"
      },
      "source": [
        "### Q: Can use ‘+=1’ to keep adding to the variable instead of replacing it with ‘upper_body_counter = upper_body_counter + 1’?\n",
        "\n",
        "A:  yes ```var +=1``` is *syntatic sugar*\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVYPRQHE3uUJ",
        "colab_type": "code",
        "outputId": "c92ef341-5580-4c9f-8033-e04c35784a71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "count = 0\n",
        "for i in range(3):\n",
        "  count +=1\n",
        "  print(count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKvaFr4T32T6",
        "colab_type": "code",
        "outputId": "aa1f333e-e3c5-4f41-f694-0da6341e560f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "count = 3\n",
        "for i in range(3):\n",
        "  count -=1\n",
        "  print(count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "1\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSZHcS0x37yc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Kh5LRGT4CA1",
        "colab_type": "text"
      },
      "source": [
        "### Q: Does a function with ‘return’ instead of using ‘print’ do the same thing?\n",
        "\n",
        "A:  No, these are different mostly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KmJMZvt4HLT",
        "colab_type": "code",
        "outputId": "01a2f350-080f-4e3c-f6ec-8d6d4dc5bc42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#in jupyter notebook, the last line prints automatically\n",
        "var = \"blue\"\n",
        "var"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'blue'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udtd73bF4Mbr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def simple_color():\n",
        "  print(\"blue\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DRH-i3b4YWP",
        "colab_type": "code",
        "outputId": "3fa220e0-2f1f-4fc2-e4b6-3ee018fcc279",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "simple_color()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "blue\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0P7FTeA24aUE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def simple_color_with_return():\n",
        "  return \"blue\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cy_LkeXc4iA4",
        "colab_type": "code",
        "outputId": "09818a86-836b-430a-b837-dffcdc9e27bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "simple_color_with_return()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'blue'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsvZudbT4lb8",
        "colab_type": "code",
        "outputId": "7226c525-9b46-43b3-80c9-52c3dd354cfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "if simple_color_with_return() == \"blue\":\n",
        "  print(\"green\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "green\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fnq5fmgH4r0m",
        "colab_type": "code",
        "outputId": "23da5e12-8918-45fe-eed1-8964ca08127f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "if simple_color() == \"blue\":\n",
        "  print(\"green\")\n",
        "else:\n",
        "  print(\"the function had no return value\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "blue\n",
            "the function had no return value\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yV2-t0f742vt",
        "colab_type": "code",
        "outputId": "41078e3e-5e2d-4cad-cf2c-84299cd93258",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "simple_color() == None\n",
        "simple_color() == \"blue\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "blue\n",
            "blue\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBJKnr3h5P0L",
        "colab_type": "text"
      },
      "source": [
        "A function with no Return value in python return None"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kutwVeaP47b-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def some_function_with_no_return():\n",
        "  var = \"blue\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dO9To4Kd5XdR",
        "colab_type": "code",
        "outputId": "849480ec-f1d0-40af-a799-959814d252fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(some_function_with_no_return())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg0DIphT5bBQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def some_function_with_return():\n",
        "  var = \"blue\"\n",
        "  return var"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COjEYIo15f2g",
        "colab_type": "code",
        "outputId": "bbfeb73e-d538-4772-e100-b1b65baffa87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(some_function_with_return())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "blue\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RekRZ8485hGB",
        "colab_type": "code",
        "outputId": "8261d591-bad9-4f16-ea44-7710ac1eb98f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "result = some_function_with_return()\n",
        "print(f\"The color that is my favorite is {result}, but I also like green\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The color that is my favorite is blue, but I also like green\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYqiw7R45zm4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9zJtAd5K8AK",
        "colab_type": "text"
      },
      "source": [
        "### Q: the concept of **args and **Kwargs is unclear \n",
        "A:  See below, it allows arbitrary or \"N\" arguments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvyV5WfuK7Qo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def func_kwargs(**kwargs):\n",
        "  for k,v in kwargs.items():\n",
        "    print(k)\n",
        "    print(v)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwztjJyMLH4i",
        "colab_type": "code",
        "outputId": "9d7c15cc-0824-4b0c-b691-b0584c17f7c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "func_kwargs(one=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "one\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APp3w_bPLLEO",
        "colab_type": "code",
        "outputId": "6de40486-8bd2-4b01-f5e1-6368536ba919",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "func_kwargs(one=1, two=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "one\n",
            "1\n",
            "two\n",
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UrKgeVuLOnG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "378G9oDxLd7J",
        "colab_type": "text"
      },
      "source": [
        "### Q: What is a dataframe\n",
        "A:  Another data structure in Python, but like an excel spreadsheet:  columns and rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkKST1NYLfS3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "mydict= {}\n",
        "mylist = []\n",
        "mydf = pd.DataFrame()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-a8rJmRLlBg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cs7ArpjKLuwo",
        "colab_type": "text"
      },
      "source": [
        "####Q: how to call python from Java ? Is it common to do that ? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrumlDYtLygc",
        "colab_type": "text"
      },
      "source": [
        "* use jython\n",
        "* common in Spark"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LfUglt-sMJYX",
        "colab_type": "text"
      },
      "source": [
        "How does the performance effect while handling huge files in python?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YfphOknLw3I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QDEkX_VWYNWb",
        "colab_type": "text"
      },
      "source": [
        "#### Q: How to import module written in Colab into another Colab script?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJ5nd70ib29V",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhFN9Oi3k8jX",
        "colab_type": "text"
      },
      "source": [
        "### How is a string stored in Memory is it like Java?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcCijtOYlMK2",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30kFSdZDmw1H",
        "colab_type": "text"
      },
      "source": [
        "### How do I format a number with f-string"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGks5H3hm4YE",
        "colab_type": "text"
      },
      "source": [
        "I want 1.200 with the two trailing zeros, rather than the default 1.2.\n",
        "\n",
        "Check out this [stackoverflow article](https://stackoverflow.com/questions/45310254/fixed-digits-after-decimal-with-f-strings).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTPw7MjaPcVc",
        "colab_type": "code",
        "outputId": "59f14533-3021-4fbe-88e3-102d143c6761",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "fancy_formatting = 3.141592653\n",
        "print(f'Only print two digits: {fancy_formatting:.2f}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Only print two digits: 3.14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cr5eNnd8lLiO",
        "colab_type": "text"
      },
      "source": [
        "## Could give me an example of OOP kind of idea implemented in functional style"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jx9nJqXSlUQ8",
        "colab_type": "text"
      },
      "source": [
        "Yes, the nested function with local state is an example of keeping and modify"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWYlnEcaoGfx",
        "colab_type": "text"
      },
      "source": [
        "### What are some examples of filtering rows in a pandas DataFrame\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7PLV9pJoL4f",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQhEoJu1tNRe",
        "colab_type": "text"
      },
      "source": [
        "## References"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6BaXDS7tQTD",
        "colab_type": "text"
      },
      "source": [
        "*   [Pragmatic AI: An Introduction to Cloud-Based Machine Learning-Physical Book](http://www.informit.com/store/pragmatic-ai-an-introduction-to-cloud-based-machine-9780134863917)\n",
        "*   [Pragmatic AI: Pragmatic AI: An Introduction to Cloud-Based Machine Learning-SafariOnline Book](https://www.safaribooksonline.com/library/view/pragmatic-ai-an/9780134863924/)\n",
        "*  [Essential Machine Learning and AI with Python and Jupyter Notebook-Video-SafariOnline](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118)\n",
        "* [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",
        "*   [noahgift.com](https://noahgift.com/)\n",
        "*   [paiml.com](https://paiml.com/)\n",
        "* [Forbes:  Here come the notebooks](https://www.forbes.com/sites/forbestechcouncil/2018/08/17/here-come-the-notebooks/#766563807609)\n",
        "* [Circleci:  Increase reliability in data science and ML projects](https://circleci.com/blog/increase-reliability-in-data-science-and-machine-learning-projects-with-circleci/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfXrpizuK5Sm",
        "colab_type": "text"
      },
      "source": [
        "![ecosystem](https://user-images.githubusercontent.com/58792/64554498-8a4d5100-d309-11e9-8aa5-bbbd8ebcf130.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfEYCS0MK-15",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}