altair_debugging.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Altair Debugging Guide\n",
    "\n",
    "In this notebook we show you common debugging techniques that you can use if you run into issues with Altair. \n",
    "\n",
    "You can jump to the following sections:\n",
    "\n",
    "* [Installation and Setup](#Installation) when Altair is not installed correctly\n",
    "* [Display Issues](#Display-Troubleshooting) when you don't see a chart\n",
    "* [Invalid Specifications](#Invalid-Specifications) when you get an error\n",
    "* [Properties are Being Ignored](#Properties-are-Being-Ignored) when you don't see any errors or warnings\n",
    "* [Asking for Help](#Asking-for-Help) when you get stuck\n",
    "* [Reporting Issues](#Reporting-Issues) when you find a bug\n",
    "\n",
    "In addition to this notebook, you might find the [Frequently Asked Questions](https://altair-viz.github.io/user_guide/faq.html) and [Display Troubleshooting](https://altair-viz.github.io/user_guide/troubleshooting.html) guides helpful. \n",
    "\n",
    "_This notebook is part of the [data visualization curriculum](https://github.com/uwdata/visualization-curriculum)._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These instructions follow [the Altair documentation](https://altair-viz.github.io/getting_started/installation.html) but focus on some specifics for this series of notebooks. \n",
    "\n",
    "In every notebook, we will import the [Altair](https://github.com/altair-viz/altair) and [Vega Datasets](https://github.com/altair-viz/vega_datasets) packages. If you are running this notebook on [Colab](https://colab.research.google.com), Altair and Vega Datasets should be preinstalled and ready to go. The notebooks in this series are designed for Colab but should also work in Jupyter Lab or the Jupyter Notebook (the notebook requires a bit more setup [described below](#Special-Setup-for-the-Jupyter-Notebook)) but additional packages are required. \n",
    "\n",
    "If you are running in Jupyter Lab or Jupyter Notebooks, you have to install the necessary packages by running the following command in your terminal.\n",
    "\n",
    "```bash\n",
    "pip install altair vega_datasets\n",
    "```\n",
    "\n",
    "Or if you use [Conda](https://conda.io)\n",
    "\n",
    "```bash\n",
    "conda install -c conda-forge altair vega_datasets\n",
    "```\n",
    "\n",
    "You can run command line commands from a code cell by prefixing it with `!`. For example, to install Altair and Vega Datasets with [Pip](https://pip.pypa.io/), you can run the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: altair in /anaconda3/envs/jl/lib/python3.7/site-packages (3.2.0)\n",
      "Requirement already satisfied: vega_datasets in /anaconda3/envs/jl/lib/python3.7/site-packages (0.7.0)\n",
      "Requirement already satisfied: numpy in /anaconda3/envs/jl/lib/python3.7/site-packages (from altair) (1.17.0)\n",
      "Requirement already satisfied: six in /anaconda3/envs/jl/lib/python3.7/site-packages (from altair) (1.12.0)\n",
      "Requirement already satisfied: jsonschema in /anaconda3/envs/jl/lib/python3.7/site-packages (from altair) (3.0.2)\n",
      "Requirement already satisfied: jinja2 in /anaconda3/envs/jl/lib/python3.7/site-packages (from altair) (2.10.1)\n",
      "Requirement already satisfied: entrypoints in /anaconda3/envs/jl/lib/python3.7/site-packages (from altair) (0.3)\n",
      "Requirement already satisfied: toolz in /anaconda3/envs/jl/lib/python3.7/site-packages (from altair) (0.10.0)\n",
      "Requirement already satisfied: pandas in /anaconda3/envs/jl/lib/python3.7/site-packages (from altair) (0.25.0)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in /anaconda3/envs/jl/lib/python3.7/site-packages (from jsonschema->altair) (0.15.4)\n",
      "Requirement already satisfied: setuptools in /anaconda3/envs/jl/lib/python3.7/site-packages (from jsonschema->altair) (41.0.1)\n",
      "Requirement already satisfied: attrs>=17.4.0 in /anaconda3/envs/jl/lib/python3.7/site-packages (from jsonschema->altair) (19.1.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /anaconda3/envs/jl/lib/python3.7/site-packages (from jinja2->altair) (1.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /anaconda3/envs/jl/lib/python3.7/site-packages (from pandas->altair) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /anaconda3/envs/jl/lib/python3.7/site-packages (from pandas->altair) (2019.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install altair vega_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "from vega_datasets import data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make sure you are Using the Latest Version of Altair"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are running into issues with Altair, first make sure that you are running the latest version. To check the version of Altair that you have installed, run the cell below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.2.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check what the latest version of altair is, go to [this page](https://pypi.org/project/altair/) or run the cell below (requires Python 3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2.0\n"
     ]
    }
   ],
   "source": [
    "import urllib.request, json \n",
    "with urllib.request.urlopen(\"https://pypi.org/pypi/altair/json\") as url:\n",
    "    print(json.loads(url.read().decode())['info']['version'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are not running the latest version, you can update it with `pip`. You can update Altair and Vega Datasets by running this command in your terminal.\n",
    "\n",
    "```\n",
    "pip install -U altair vega_datasets\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try Making a Chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can create an Altair chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-a264acbd6e539a8b3afc0cb5f240fb57"
       },
       "datasets": {
        "data-a264acbd6e539a8b3afc0cb5f240fb57": [
         {
          "petalLength": 1.4,
          "petalWidth": 0.2,
          "sepalLength": 5.1,
          "sepalWidth": 3.5,
          "species": "setosa"
         },
         {
          "petalLength": 1.4,
          "petalWidth": 0.2,
          "sepalLength": 4.9,
          "sepalWidth": 3,
          "species": "setosa"
         },
         {
          "petalLength": 1.3,
          "petalWidth": 0.2,
          "sepalLength": 4.7,
          "sepalWidth": 3.2,
          "species": "setosa"
         },
         {
          "petalLength": 1.5,
          "petalWidth": 0.2,
          "sepalLength": 4.6,
          "sepalWidth": 3.1,
          "species": "setosa"
         },
         {
          "petalLength": 1.4,
          "petalWidth": 0.2,
          "sepalLength": 5,
          "sepalWidth": 3.6,
          "species": "setosa"
         },
         {
          "petalLength": 1.7000000000000002,
          "petalWidth": 0.4,
          "sepalLength": 5.4,
          "sepalWidth": 3.9,
          "species": "setosa"
         },
         {
          "petalLength": 1.4,
          "petalWidth": 0.30000000000000004,
          "sepalLength": 4.6,
          "sepalWidth": 3.4,
          "species": "setosa"
         },
         {
          "petalLength": 1.5,
          "petalWidth": 0.2,
          "sepalLength": 5,
          "sepalWidth": 3.4,
          "species": "setosa"
         },
         {
          "petalLength": 1.4,
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          "sepalLength": 4.4,
          "sepalWidth": 2.9,
          "species": "setosa"
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          "species": "setosa"
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          "sepalLength": 4.8,
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          "sepalLength": 4.3,
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          "petalLength": 1.3,
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         {
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          "species": "setosa"
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         {
          "petalLength": 1.2,
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          "sepalWidth": 3.2,
          "species": "setosa"
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          "species": "setosa"
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          "species": "setosa"
         },
         {
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          "petalWidth": 0.2,
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          "sepalWidth": 3.2,
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          "species": "setosa"
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         {
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          "species": "setosa"
         },
         {
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          "petalWidth": 0.2,
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          "sepalWidth": 3.2,
          "species": "setosa"
         },
         {
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          "species": "setosa"
         },
         {
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          "species": "versicolor"
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         {
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          "species": "versicolor"
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          "species": "versicolor"
         },
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          "species": "versicolor"
         },
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          "species": "versicolor"
         },
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          "species": "versicolor"
         },
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         },
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          "sepalWidth": 2.4,
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         },
         {
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          "sepalWidth": 2.9,
          "species": "versicolor"
         },
         {
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          "petalWidth": 1.4,
          "sepalLength": 5.2,
          "sepalWidth": 2.7,
          "species": "versicolor"
         },
         {
          "petalLength": 3.5,
          "petalWidth": 1,
          "sepalLength": 5,
          "sepalWidth": 2,
          "species": "versicolor"
         },
         {
          "petalLength": 4.2,
          "petalWidth": 1.5,
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          "sepalWidth": 3,
          "species": "versicolor"
         },
         {
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          "petalWidth": 1,
          "sepalLength": 6,
          "sepalWidth": 2.2,
          "species": "versicolor"
         },
         {
          "petalLength": 4.7,
          "petalWidth": 1.4,
          "sepalLength": 6.1,
          "sepalWidth": 2.9,
          "species": "versicolor"
         },
         {
          "petalLength": 3.6,
          "petalWidth": 1.3,
          "sepalLength": 5.6,
          "sepalWidth": 2.9,
          "species": "versicolor"
         },
         {
          "petalLength": 4.4,
          "petalWidth": 1.4,
          "sepalLength": 6.7,
          "sepalWidth": 3.1,
          "species": "versicolor"
         },
         {
          "petalLength": 4.5,
          "petalWidth": 1.5,
          "sepalLength": 5.6,
          "sepalWidth": 3,
          "species": "versicolor"
         },
         {
          "petalLength": 4.1,
          "petalWidth": 1,
          "sepalLength": 5.8,
          "sepalWidth": 2.7,
          "species": "versicolor"
         },
         {
          "petalLength": 4.5,
          "petalWidth": 1.5,
          "sepalLength": 6.2,
          "sepalWidth": 2.2,
          "species": "versicolor"
         },
         {
          "petalLength": 3.9,
          "petalWidth": 1.1,
          "sepalLength": 5.6,
          "sepalWidth": 2.5,
          "species": "versicolor"
         },
         {
          "petalLength": 4.8,
          "petalWidth": 1.8,
          "sepalLength": 5.9,
          "sepalWidth": 3.2,
          "species": "versicolor"
         },
         {
          "petalLength": 4,
          "petalWidth": 1.3,
          "sepalLength": 6.1,
          "sepalWidth": 2.8,
          "species": "versicolor"
         },
         {
          "petalLength": 4.9,
          "petalWidth": 1.5,
          "sepalLength": 6.3,
          "sepalWidth": 2.5,
          "species": "versicolor"
         },
         {
          "petalLength": 4.7,
          "petalWidth": 1.2,
          "sepalLength": 6.1,
          "sepalWidth": 2.8,
          "species": "versicolor"
         },
         {
          "petalLength": 4.3,
          "petalWidth": 1.3,
          "sepalLength": 6.4,
          "sepalWidth": 2.9,
          "species": "versicolor"
         },
         {
          "petalLength": 4.4,
          "petalWidth": 1.4,
          "sepalLength": 6.6,
          "sepalWidth": 3,
          "species": "versicolor"
         },
         {
          "petalLength": 4.8,
          "petalWidth": 1.4,
          "sepalLength": 6.8,
          "sepalWidth": 2.8,
          "species": "versicolor"
         },
         {
          "petalLength": 5,
          "petalWidth": 1.7000000000000002,
          "sepalLength": 6.7,
          "sepalWidth": 3,
          "species": "versicolor"
         },
         {
          "petalLength": 4.5,
          "petalWidth": 1.5,
          "sepalLength": 6,
          "sepalWidth": 2.9,
          "species": "versicolor"
         },
         {
          "petalLength": 3.5,
          "petalWidth": 1,
          "sepalLength": 5.7,
          "sepalWidth": 2.6,
          "species": "versicolor"
         },
         {
          "petalLength": 3.8,
          "petalWidth": 1.1,
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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = data.iris()\n",
    "\n",
    "alt.Chart(iris).mark_point().encode(\n",
    "    x='petalLength',\n",
    "    y='petalWidth',\n",
    "    color='species'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Special Setup for the Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are running in Colab or Jupyter Lab, you should be seeing a chart. If you are running in the Jupyter Notebook, you need to install an additional dependency and tell Altair to render charts for the Notebook.\n",
    "\n",
    "The additional dependency is the `vega` package, which you can install by running this command in your terminal\n",
    "\n",
    "```bash\n",
    "pip install vega\n",
    "```\n",
    "\n",
    "Then activate the Notebook renderer in a notebook cell\n",
    "\n",
    "```python\n",
    "# for the notebook only (not for JupyterLab) run this command once per session\n",
    "alt.renderers.enable('notebook')\n",
    "\n",
    "```\n",
    "\n",
    "These instruction follow [the instructions on the Altair website](https://altair-viz.github.io/getting_started/installation.html#installation-notebook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Troubleshooting\n",
    "\n",
    "If you are having issues with seeing a chart, make sure your setup is correct by following the [debugging instruction above](#installation). If you are still having issues, follow the [instruction about debugging display issues in the Altair documentation](https://altair-viz.github.io/user_guide/troubleshooting.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non Existent Fields\n",
    "\n",
    "A common error is [accidentally using a field that does not exit](https://altair-viz.github.io/user_guide/troubleshooting.html#plot-displays-but-the-content-is-empty). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-6ef4eb0433870443fb20a26352f02d1b"
       },
       "datasets": {
        "data-6ef4eb0433870443fb20a26352f02d1b": [
         {
          "x": 1,
          "y": 3
         },
         {
          "x": 2,
          "y": 1
         },
         {
          "x": 3,
          "y": 4
         }
        ]
       },
       "encoding": {
        "color": {
         "field": "color",
         "type": "quantitative"
        },
        "x": {
         "field": "x",
         "type": "quantitative"
        },
        "y": {
         "field": "y",
         "type": "quantitative"
        }
       },
       "mark": "point"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({'x': [1, 2, 3],\n",
    "                     'y': [3, 1, 4]})\n",
    "\n",
    "alt.Chart(df).mark_point().encode(\n",
    "    x='x:Q',\n",
    "    y='y:Q',\n",
    "    color='color:Q'  # <-- this field does not exist in the data!\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the spelling of your files and print the data source to confirm that the data and fields exist. For instance, here you see that `color` is not a vaid field. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y\n",
       "0  1  3\n",
       "1  2  1\n",
       "2  3  4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Invalid Specifications\n",
    "\n",
    "Another common issue is creating an invalid specification and getting an error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invalid Properties\n",
    "\n",
    "Altair might show an `SchemaValidationError` or `ValueError`. Read the error message carefully. Usually it will tell you what is going wrong. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, if you forget the mark type, you will see this `SchemaValidationError`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "SchemaValidationError",
     "evalue": "Invalid specification\n\n        altair.vegalite.v3.api.Chart, validating 'required'\n\n        'mark' is a required property\n        ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mSchemaValidationError\u001b[0m                     Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/envs/jl/lib/python3.7/site-packages/altair/vegalite/v3/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    382\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdct\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'validate'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'deep'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0mdct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTopLevelMixin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;31m# TODO: following entries are added after validation. Should they be validated?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/jl/lib/python3.7/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, validate, ignore, context)\u001b[0m\n\u001b[1;32m    300\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mjsonschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValidationError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mSchemaValidationError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSchemaValidationError\u001b[0m: Invalid specification\n\n        altair.vegalite.v3.api.Chart, validating 'required'\n\n        'mark' is a required property\n        "
     ]
    },
    {
     "data": {
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(data.cars()).encode(\n",
    "    y='Horsepower'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or if you use a non-existent channel, you get a `ValueError`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/jl/lib/python3.7/site-packages/altair/utils/core.py:494: UserWarning: Unrecognized encoding channel 'z'\n",
      "  warnings.warn(\"Unrecognized encoding channel '{}'\".format(encoding))\n"
     ]
    },
    {
     "ename": "SchemaValidationError",
     "evalue": "Invalid specification\n\n        altair.vegalite.v3.schema.core.FacetedEncoding, validating 'additionalProperties'\n\n        Additional properties are not allowed ('z' was unexpected)\n        ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mSchemaValidationError\u001b[0m                     Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/envs/jl/lib/python3.7/site-packages/altair/vegalite/v3/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    382\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdct\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'validate'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'deep'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0mdct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTopLevelMixin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;31m# TODO: following entries are added after validation. Should they be validated?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/jl/lib/python3.7/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, validate, ignore, context)\u001b[0m\n\u001b[1;32m    292\u001b[0m             result = _todict({k: v for k, v in self._kwds.items()\n\u001b[1;32m    293\u001b[0m                               if k not in ignore},\n\u001b[0;32m--> 294\u001b[0;31m                               validate=sub_validate, context=context)\n\u001b[0m\u001b[1;32m    295\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m             raise ValueError(\"{} instance has both a value and properties : \"\n",
      "\u001b[0;32m/anaconda3/envs/jl/lib/python3.7/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36m_todict\u001b[0;34m(obj, validate, context)\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_todict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         return {k: _todict(v, validate, context) for k, v in obj.items()\n\u001b[0m\u001b[1;32m     51\u001b[0m                 if v is not Undefined}\n\u001b[1;32m     52\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'to_dict'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/jl/lib/python3.7/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         return {k: _todict(v, validate, context) for k, v in obj.items()\n\u001b[0;32m---> 51\u001b[0;31m                 if v is not Undefined}\n\u001b[0m\u001b[1;32m     52\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'to_dict'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/jl/lib/python3.7/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36m_todict\u001b[0;34m(obj, validate, context)\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;34m\"\"\"Convert an object to a dict representation.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSchemaBase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_todict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/jl/lib/python3.7/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, validate, ignore, context)\u001b[0m\n\u001b[1;32m    300\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mjsonschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValidationError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mSchemaValidationError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSchemaValidationError\u001b[0m: Invalid specification\n\n        altair.vegalite.v3.schema.core.FacetedEncoding, validating 'additionalProperties'\n\n        Additional properties are not allowed ('z' was unexpected)\n        "
     ]
    },
    {
     "data": {
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(data.cars()).mark_point().encode(\n",
    "    z='Horsepower'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Properties are Being Ignored\n",
    "\n",
    "Altair might ignore a property that you specified. In the chart below, we are using a `text` channel, which is only compatible with `mark_text`. You do not see an error or a warning about this in the notebook. However, the underlying Vega-Lite library will show a warning in the browser console.  Press <kbd>Alt</kbd>+<kbd>Cmd</kbd>+<kbd>I</kbd> on Mac or <kbd>Alt</kbd>+<kbd>Ctrl</kbd>+<kbd>I</kbd> on Windows and Linux to open the developer tools and click on the `Console` tab. When you run the example in the cell below, you will see a the following warning.\n",
    "\n",
    "```\n",
    "WARN text dropped as it is incompatible with \"bar\".\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-f02450ab61490a1363517a0190416235"
       },
       "datasets": {
        "data-f02450ab61490a1363517a0190416235": [
         {
          "Acceleration": 12,
          "Cylinders": 8,
          "Displacement": 307,
          "Horsepower": 130,
          "Miles_per_Gallon": 18,
          "Name": "chevrolet chevelle malibu",
          "Origin": "USA",
          "Weight_in_lbs": 3504,
          "Year": "1970-01-01T00:00:00"
         },
         {
          "Acceleration": 11.5,
          "Cylinders": 8,
          "Displacement": 350,
          "Horsepower": 165,
          "Miles_per_Gallon": 15,
          "Name": "buick skylark 320",
          "Origin": "USA",
          "Weight_in_lbs": 3693,
          "Year": "1970-01-01T00:00:00"
         },
         {
          "Acceleration": 11,
          "Cylinders": 8,
          "Displacement": 318,
          "Horsepower": 150,
          "Miles_per_Gallon": 18,
          "Name": "plymouth satellite",
          "Origin": "USA",
          "Weight_in_lbs": 3436,
          "Year": "1970-01-01T00:00:00"
         },
         {
          "Acceleration": 12,
          "Cylinders": 8,
          "Displacement": 304,
          "Horsepower": 150,
          "Miles_per_Gallon": 16,
          "Name": "amc rebel sst",
          "Origin": "USA",
          "Weight_in_lbs": 3433,
          "Year": "1970-01-01T00:00:00"
         },
         {
          "Acceleration": 10.5,
          "Cylinders": 8,
          "Displacement": 302,
          "Horsepower": 140,
          "Miles_per_Gallon": 17,
          "Name": "ford torino",
          "Origin": "USA",
          "Weight_in_lbs": 3449,
          "Year": "1970-01-01T00:00:00"
         },
         {
          "Acceleration": 10,
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         },
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         },
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         },
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         },
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         },
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         },
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         },
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         },
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         },
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         },
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          "Year": "1978-01-01T00:00:00"
         },
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         },
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          "Year": "1978-01-01T00:00:00"
         },
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         },
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          "Year": "1978-01-01T00:00:00"
         },
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          "Horsepower": 95,
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          "Name": "chevrolet malibu",
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          "Year": "1978-01-01T00:00:00"
         },
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          "Year": "1978-01-01T00:00:00"
         },
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          "Year": "1978-01-01T00:00:00"
         },
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          "Acceleration": 17.2,
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          "Horsepower": 100,
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          "Name": "plymouth volare",
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          "Year": "1978-01-01T00:00:00"
         },
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          "Year": "1978-01-01T00:00:00"
         },
         {
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          "Miles_per_Gallon": 20.6,
          "Name": "buick century special",
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          "Year": "1978-01-01T00:00:00"
         },
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          "Name": "mercury zephyr",
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          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 18.7,
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          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 15.1,
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          "Horsepower": 120,
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          "Year": "1978-01-01T00:00:00"
         },
         {
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          "Horsepower": 145,
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          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 13.4,
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          "Horsepower": 165,
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          "Name": "buick regal sport coupe (turbo)",
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          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 11.2,
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          "Horsepower": 139,
          "Miles_per_Gallon": 18.1,
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          "Year": "1978-01-01T00:00:00"
         },
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          "Horsepower": 140,
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          "Year": "1978-01-01T00:00:00"
         },
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          "Horsepower": 68,
          "Miles_per_Gallon": 30,
          "Name": "chevrolet chevette",
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          "Year": "1978-01-01T00:00:00"
         },
         {
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          "Horsepower": 95,
          "Miles_per_Gallon": 27.5,
          "Name": "toyota corona",
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          "Year": "1978-01-01T00:00:00"
         },
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          "Horsepower": 97,
          "Miles_per_Gallon": 27.2,
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          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 14.5,
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          "Horsepower": 75,
          "Miles_per_Gallon": 30.9,
          "Name": "dodge omni",
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          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 14.8,
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          "Horsepower": 95,
          "Miles_per_Gallon": 21.1,
          "Name": "toyota celica gt liftback",
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          "Weight_in_lbs": 2515,
          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 16.7,
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          "Horsepower": 105,
          "Miles_per_Gallon": 23.2,
          "Name": "plymouth sapporo",
          "Origin": "USA",
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          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 17.6,
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          "Horsepower": 85,
          "Miles_per_Gallon": 23.8,
          "Name": "oldsmobile starfire sx",
          "Origin": "USA",
          "Weight_in_lbs": 2855,
          "Year": "1978-01-01T00:00:00"
         },
         {
          "Acceleration": 14.9,
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          "Horsepower": 97,
          "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,
          "Horsepower": 103,
          "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,
          "Horsepower": 125,
          "Miles_per_Gallon": 17,
          "Name": "volvo 264gl",
          "Origin": "Europe",
          "Weight_in_lbs": 3140,
          "Year": "1978-01-01T00:00:00"
         },
         {
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          "Acceleration": 16.6,
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         },
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         },
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         },
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         },
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          "Year": "1980-01-01T00:00:00"
         },
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         },
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          "Year": "1980-01-01T00:00:00"
         },
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         },
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         },
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          "Miles_per_Gallon": 34.3,
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         },
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         },
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         },
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          "Year": "1980-01-01T00:00:00"
         },
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          "Acceleration": 15.2,
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          "Year": "1980-01-01T00:00:00"
         },
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          "Year": "1980-01-01T00:00:00"
         },
         {
          "Acceleration": 14.4,
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          "Horsepower": 105,
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          "Year": "1980-01-01T00:00:00"
         },
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          "Miles_per_Gallon": 40.8,
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         },
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          "Name": "vw rabbit c (diesel)",
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          "Year": "1980-01-01T00:00:00"
         },
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         },
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          "Name": "audi 5000s (diesel)",
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         },
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          "Name": "mercedes-benz 240d",
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          "Year": "1980-01-01T00:00:00"
         },
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          "Acceleration": 13.8,
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          "Name": "honda civic 1500 gl",
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          "Year": "1980-01-01T00:00:00"
         },
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          "Acceleration": 17.3,
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          "Horsepower": null,
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          "Name": "renault lecar deluxe",
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         },
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          "Miles_per_Gallon": 33.8,
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          "Year": "1980-01-01T00:00:00"
         },
         {
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          "Name": "vokswagen rabbit",
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          "Year": "1980-01-01T00:00:00"
         },
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          "Name": "datsun 280-zx",
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          "Year": "1980-01-01T00:00:00"
         },
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          "Name": "mazda rx-7 gs",
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          "Year": "1980-01-01T00:00:00"
         },
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          "Horsepower": 88,
          "Miles_per_Gallon": 35,
          "Name": "triumph tr7 coupe",
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          "Year": "1980-01-01T00:00:00"
         },
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          "Acceleration": 14.3,
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          "Horsepower": null,
          "Miles_per_Gallon": 23.6,
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          "Year": "1980-01-01T00:00:00"
         },
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          "Acceleration": 17,
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          "Miles_per_Gallon": 32.4,
          "Name": "honda Accelerationord",
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          "Year": "1980-01-01T00:00:00"
         },
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          "Acceleration": 15.7,
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          "Horsepower": 84,
          "Miles_per_Gallon": 27.2,
          "Name": "plymouth reliant",
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          "Year": "1982-01-01T00:00:00"
         },
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          "Acceleration": 16.4,
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          "Horsepower": 84,
          "Miles_per_Gallon": 26.6,
          "Name": "buick skylark",
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          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 14.4,
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          "Horsepower": 92,
          "Miles_per_Gallon": 25.8,
          "Name": "dodge aries wagon (sw)",
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          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 12.6,
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          "Horsepower": 110,
          "Miles_per_Gallon": 23.5,
          "Name": "chevrolet citation",
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          "Year": "1982-01-01T00:00:00"
         },
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          "Acceleration": 12.9,
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          "Horsepower": 84,
          "Miles_per_Gallon": 30,
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          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16.9,
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          "Horsepower": 58,
          "Miles_per_Gallon": 39.1,
          "Name": "toyota starlet",
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          "Weight_in_lbs": 1755,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16.4,
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          "Displacement": 86,
          "Horsepower": 64,
          "Miles_per_Gallon": 39,
          "Name": "plymouth champ",
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          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16.1,
          "Cylinders": 4,
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          "Horsepower": 60,
          "Miles_per_Gallon": 35.1,
          "Name": "honda civic 1300",
          "Origin": "Japan",
          "Weight_in_lbs": 1760,
          "Year": "1982-01-01T00:00:00"
         },
         {
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          "Miles_per_Gallon": 32.3,
          "Name": "subaru",
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          "Year": "1982-01-01T00:00:00"
         },
         {
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          "Horsepower": 65,
          "Miles_per_Gallon": 37,
          "Name": "datsun 210",
          "Origin": "Japan",
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          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 17.3,
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          "Horsepower": 62,
          "Miles_per_Gallon": 37.7,
          "Name": "toyota tercel",
          "Origin": "Japan",
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          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16,
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          "Displacement": 91,
          "Horsepower": 68,
          "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,
          "Horsepower": 63,
          "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,
          "Horsepower": 65,
          "Miles_per_Gallon": 34.4,
          "Name": "ford escort 4w",
          "Origin": "USA",
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          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 20.7,
          "Cylinders": 4,
          "Displacement": 98,
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          "Miles_per_Gallon": 29.9,
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          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 14.2,
          "Cylinders": 4,
          "Displacement": 105,
          "Horsepower": 74,
          "Miles_per_Gallon": 33,
          "Name": "volkswagen jetta",
          "Origin": "Europe",
          "Weight_in_lbs": 2190,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 15.8,
          "Cylinders": 4,
          "Displacement": 100,
          "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,
          "Horsepower": 75,
          "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,
          "Horsepower": 75,
          "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,
          "Horsepower": 100,
          "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,
          "Horsepower": 74,
          "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,
          "Horsepower": 80,
          "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,
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          "Displacement": 121,
          "Horsepower": 110,
          "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,
          "Horsepower": 76,
          "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,
          "Horsepower": 116,
          "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,
          "Horsepower": 120,
          "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,
          "Horsepower": 110,
          "Miles_per_Gallon": 22.4,
          "Name": "buick century",
          "Origin": "USA",
          "Weight_in_lbs": 3415,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 19,
          "Cylinders": 8,
          "Displacement": 350,
          "Horsepower": 105,
          "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,
          "Horsepower": 88,
          "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,
          "Horsepower": 85,
          "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,
          "Horsepower": 88,
          "Miles_per_Gallon": 28,
          "Name": "chevrolet cavalier",
          "Origin": "USA",
          "Weight_in_lbs": 2605,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 18.6,
          "Cylinders": 4,
          "Displacement": 112,
          "Horsepower": 88,
          "Miles_per_Gallon": 27,
          "Name": "chevrolet cavalier wagon",
          "Origin": "USA",
          "Weight_in_lbs": 2640,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 18,
          "Cylinders": 4,
          "Displacement": 112,
          "Horsepower": 88,
          "Miles_per_Gallon": 34,
          "Name": "chevrolet cavalier 2-door",
          "Origin": "USA",
          "Weight_in_lbs": 2395,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16.2,
          "Cylinders": 4,
          "Displacement": 112,
          "Horsepower": 85,
          "Miles_per_Gallon": 31,
          "Name": "pontiac j2000 se hatchback",
          "Origin": "USA",
          "Weight_in_lbs": 2575,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16,
          "Cylinders": 4,
          "Displacement": 135,
          "Horsepower": 84,
          "Miles_per_Gallon": 29,
          "Name": "dodge aries se",
          "Origin": "USA",
          "Weight_in_lbs": 2525,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 18,
          "Cylinders": 4,
          "Displacement": 151,
          "Horsepower": 90,
          "Miles_per_Gallon": 27,
          "Name": "pontiac phoenix",
          "Origin": "USA",
          "Weight_in_lbs": 2735,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16.4,
          "Cylinders": 4,
          "Displacement": 140,
          "Horsepower": 92,
          "Miles_per_Gallon": 24,
          "Name": "ford fairmont futura",
          "Origin": "USA",
          "Weight_in_lbs": 2865,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 20.5,
          "Cylinders": 4,
          "Displacement": 151,
          "Horsepower": null,
          "Miles_per_Gallon": 23,
          "Name": "amc concord dl",
          "Origin": "USA",
          "Weight_in_lbs": 3035,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 15.3,
          "Cylinders": 4,
          "Displacement": 105,
          "Horsepower": 74,
          "Miles_per_Gallon": 36,
          "Name": "volkswagen rabbit l",
          "Origin": "Europe",
          "Weight_in_lbs": 1980,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 18.2,
          "Cylinders": 4,
          "Displacement": 91,
          "Horsepower": 68,
          "Miles_per_Gallon": 37,
          "Name": "mazda glc custom l",
          "Origin": "Japan",
          "Weight_in_lbs": 2025,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 17.6,
          "Cylinders": 4,
          "Displacement": 91,
          "Horsepower": 68,
          "Miles_per_Gallon": 31,
          "Name": "mazda glc custom",
          "Origin": "Japan",
          "Weight_in_lbs": 1970,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 14.7,
          "Cylinders": 4,
          "Displacement": 105,
          "Horsepower": 63,
          "Miles_per_Gallon": 38,
          "Name": "plymouth horizon miser",
          "Origin": "USA",
          "Weight_in_lbs": 2125,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 17.3,
          "Cylinders": 4,
          "Displacement": 98,
          "Horsepower": 70,
          "Miles_per_Gallon": 36,
          "Name": "mercury lynx l",
          "Origin": "USA",
          "Weight_in_lbs": 2125,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 14.5,
          "Cylinders": 4,
          "Displacement": 120,
          "Horsepower": 88,
          "Miles_per_Gallon": 36,
          "Name": "nissan stanza xe",
          "Origin": "Japan",
          "Weight_in_lbs": 2160,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 14.5,
          "Cylinders": 4,
          "Displacement": 107,
          "Horsepower": 75,
          "Miles_per_Gallon": 36,
          "Name": "honda Accelerationord",
          "Origin": "Japan",
          "Weight_in_lbs": 2205,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16.9,
          "Cylinders": 4,
          "Displacement": 108,
          "Horsepower": 70,
          "Miles_per_Gallon": 34,
          "Name": "toyota corolla",
          "Origin": "Japan",
          "Weight_in_lbs": 2245,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 15,
          "Cylinders": 4,
          "Displacement": 91,
          "Horsepower": 67,
          "Miles_per_Gallon": 38,
          "Name": "honda civic",
          "Origin": "Japan",
          "Weight_in_lbs": 1965,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 15.7,
          "Cylinders": 4,
          "Displacement": 91,
          "Horsepower": 67,
          "Miles_per_Gallon": 32,
          "Name": "honda civic (auto)",
          "Origin": "Japan",
          "Weight_in_lbs": 1965,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16.2,
          "Cylinders": 4,
          "Displacement": 91,
          "Horsepower": 67,
          "Miles_per_Gallon": 38,
          "Name": "datsun 310 gx",
          "Origin": "Japan",
          "Weight_in_lbs": 1995,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 16.4,
          "Cylinders": 6,
          "Displacement": 181,
          "Horsepower": 110,
          "Miles_per_Gallon": 25,
          "Name": "buick century limited",
          "Origin": "USA",
          "Weight_in_lbs": 2945,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 17,
          "Cylinders": 6,
          "Displacement": 262,
          "Horsepower": 85,
          "Miles_per_Gallon": 38,
          "Name": "oldsmobile cutlass ciera (diesel)",
          "Origin": "USA",
          "Weight_in_lbs": 3015,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 14.5,
          "Cylinders": 4,
          "Displacement": 156,
          "Horsepower": 92,
          "Miles_per_Gallon": 26,
          "Name": "chrysler lebaron medallion",
          "Origin": "USA",
          "Weight_in_lbs": 2585,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 14.7,
          "Cylinders": 6,
          "Displacement": 232,
          "Horsepower": 112,
          "Miles_per_Gallon": 22,
          "Name": "ford granada l",
          "Origin": "USA",
          "Weight_in_lbs": 2835,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 13.9,
          "Cylinders": 4,
          "Displacement": 144,
          "Horsepower": 96,
          "Miles_per_Gallon": 32,
          "Name": "toyota celica gt",
          "Origin": "Japan",
          "Weight_in_lbs": 2665,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 13,
          "Cylinders": 4,
          "Displacement": 135,
          "Horsepower": 84,
          "Miles_per_Gallon": 36,
          "Name": "dodge charger 2.2",
          "Origin": "USA",
          "Weight_in_lbs": 2370,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 17.3,
          "Cylinders": 4,
          "Displacement": 151,
          "Horsepower": 90,
          "Miles_per_Gallon": 27,
          "Name": "chevrolet camaro",
          "Origin": "USA",
          "Weight_in_lbs": 2950,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 15.6,
          "Cylinders": 4,
          "Displacement": 140,
          "Horsepower": 86,
          "Miles_per_Gallon": 27,
          "Name": "ford mustang gl",
          "Origin": "USA",
          "Weight_in_lbs": 2790,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 24.6,
          "Cylinders": 4,
          "Displacement": 97,
          "Horsepower": 52,
          "Miles_per_Gallon": 44,
          "Name": "vw pickup",
          "Origin": "Europe",
          "Weight_in_lbs": 2130,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 11.6,
          "Cylinders": 4,
          "Displacement": 135,
          "Horsepower": 84,
          "Miles_per_Gallon": 32,
          "Name": "dodge rampage",
          "Origin": "USA",
          "Weight_in_lbs": 2295,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 18.6,
          "Cylinders": 4,
          "Displacement": 120,
          "Horsepower": 79,
          "Miles_per_Gallon": 28,
          "Name": "ford ranger",
          "Origin": "USA",
          "Weight_in_lbs": 2625,
          "Year": "1982-01-01T00:00:00"
         },
         {
          "Acceleration": 19.4,
          "Cylinders": 4,
          "Displacement": 119,
          "Horsepower": 82,
          "Miles_per_Gallon": 31,
          "Name": "chevy s-10",
          "Origin": "USA",
          "Weight_in_lbs": 2720,
          "Year": "1982-01-01T00:00:00"
         }
        ]
       },
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      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(data.cars()).mark_bar().encode(\n",
    "    y='mean(Horsepower)',\n",
    "    text='mean(Acceleration)'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you find yourself debugging issues related to Vega-Lite, you can open the chart in the [Vega Editor](https://vega.github.io/editor/) either by clicking on the \"Open in Vega Editor\" link at the bottom of the chart or in the action menu (click to open) at the top right of a chart. The Vega Editor provides additional debugging but you will be writing Vega-Lite JSON instead of Altair in Python.\n",
    "\n",
    "**Note**: The Vega Editor may be using a newer version of Vega-Lite and so the behavior may vary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Asking for Help\n",
    "\n",
    "If you find a problem with Altair and get stuck, you can ask a question on Stack Overflow. Ask your question with the `altair` and `vega-lite` tags. You can find a list of questions people have asked before [here](https://stackoverflow.com/questions/tagged/altair). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reporting Issues\n",
    "\n",
    "If you find a problem with Altair and believe it is a bug, please [create an issue in the Altair GitHub repo](https://github.com/altair-viz/altair/issues/new) with a description of your problem. If you believe the issue is related to the underlying Vega-Lite library, please [create an issue in the Vega-Lite GitHub repo](https://github.com/vega/vega-lite/issues/new)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}