class/7-tables-styling-performance.ipynb

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   "source": [
    "# 551 Lec 7 - Tables, styling, performance\n",
    "\n",
    "You need to download this notebook to view images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lecture learning goals\n",
    "\n",
    "By the end of the lecture you will be able to:\n",
    "\n",
    "1. Create dash tables\n",
    "2. Use interactive dash table components in callbacks\n",
    "3. More readily implement best practices discussed before for layout and titles\n",
    "4. Cache callback values of expensive computations\n",
    "5. Recognize performance tweaks for plots such as WebGL, and datashader\n",
    "\n",
    "-------"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dash tables\n",
    "\n",
    "### Creating tables with pagination, filters and sorting\n",
    "\n",
    "Dash has tables that can filter and sort data,\n",
    "as well as send and respond to signals.\n",
    "let's see how to create them!\n",
    "\n",
    "```python\n",
    "import dash\n",
    "import dash_table\n",
    "import dash_bootstrap_components as dbc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Output, Input\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "\n",
    "cars = data.cars()\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "app.layout = dbc.Container(\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            dash_table.DataTable(\n",
    "                id='table',\n",
    "                columns=[{\"name\": col, \"id\": col} for col in cars.columns[:5]], \n",
    "                data=cars.to_dict('records'),\n",
    "            ),\n",
    "        ])\n",
    "    ])\n",
    ")\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "We only pass the five first columns of the data frame to make it a bit easier to look at for our purposes.\n",
    "In general,\n",
    "we probably don't want to display massive amounts of data in this manner anyways.\n",
    "\n",
    "![](images/7/7.01.png)\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of viewing all the data in one long table,\n",
    "we can enable pagination by specifying e.g. `page_size=10`.\n",
    "\n",
    "![](images/7/7.02.png)\n",
    "\n",
    "\n",
    "We can enable sorting by setting `sort_action='native'`,\n",
    "which gives us small arrows to the click on the left side of each column header.\n",
    "\n",
    "![](images/7/7.03.png)\n",
    "\n",
    "Filtering can be enabled in the same manner \n",
    "by setting `filter_action='native'`.\n",
    "[Dash has it own filter syntax](https://dash.plotly.com/datatable/filtering),\n",
    "which is similar to what we have used previously with dataframes.\n",
    "\n",
    "![](images/7/7.04.png)\n",
    "\n",
    "To make columns selectable\n",
    "with a radio button,\n",
    "we need to add three lines\n",
    "(also showing how to avoid making all columns selectable,\n",
    "we're skipping `'name'` here:\n",
    "\n",
    "```python\n",
    "columns=[{\"name\": col, \"id\": col, 'selectable': True if col != 'Name' else False} for col in cars.columns[:5]],  \n",
    "column_selectable=\"single\",\n",
    "selected_columns=['Miles_per_Gallon'], \n",
    "```\n",
    "![](images/7/7.05.png)\n",
    "\n",
    "Finally,\n",
    "we can make our table a bit prettier\n",
    "by styling it with the following segment,\n",
    "which color the rows in alternating dark and light colors.\n",
    "\n",
    "```python\n",
    "style_cell={'padding': '5px'},\n",
    "style_data_conditional=[{\n",
    "    'if': {'row_index': 'odd'},\n",
    "    'backgroundColor': 'rgb(248, 248, 248)'}],\n",
    " style_header={\n",
    "    'backgroundColor': 'rgb(230, 230, 230)',\n",
    "    'fontWeight': 'bold'}\n",
    "```\n",
    "\n",
    "\n",
    "![](images/7/7.06.png)\n",
    "\n",
    "Our entire app now looks like this:\n",
    "\n",
    "```python\n",
    "import dash\n",
    "import dash_table\n",
    "import dash_bootstrap_components as dbc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Output, Input\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "\n",
    "cars = data.cars()\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "app.layout = dbc.Container(\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            dash_table.DataTable(\n",
    "                id='table',\n",
    "                columns=[{\"name\": col, \"id\": col, 'selectable': True if col != 'Name' else False} for col in cars.columns[:5]],  \n",
    "                data=cars.to_dict('records'),\n",
    "                sort_action=\"native\",\n",
    "                column_selectable=\"single\",\n",
    "                selected_columns=['Miles_per_Gallon'], \n",
    "                page_size=10,\n",
    "                filter_action='native',\n",
    "                style_cell={'padding': '5px'},\n",
    "                style_data_conditional=[{\n",
    "                    'if': {'row_index': 'odd'},\n",
    "                    'backgroundColor': 'rgb(248, 248, 248)'}],\n",
    "                 style_header={\n",
    "                    'backgroundColor': 'rgb(230, 230, 230)',\n",
    "                    'fontWeight': 'bold'}\n",
    "            ),\n",
    "        ])\n",
    "    ])\n",
    ")\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table callbacks\n",
    "\n",
    "Tables have several emitted signals/events that we can listen for,\n",
    "the most relevant in our case are the rows which change when we filter,\n",
    "and the column that is selected.\n",
    "First we add an empty div as a placeholder for the output area,\n",
    "but we will not use it in the beginning.\n",
    "Instead,\n",
    "let's just print the value\n",
    "which will show you that a list of columns is always returned.\n",
    "\n",
    "```python\n",
    "@app.callback(\n",
    "    Output('output-div', \"children\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_graphs(selected_column):\n",
    "    print(selected_column)\n",
    "```\n",
    "\n",
    "The other intersting signal,\n",
    "the rows of the table,\n",
    "are a bit more cryptically named (`'derived_virtual_data'`).\n",
    "\n",
    "```python\n",
    "@app.callback(\n",
    "    Output('output-div', \"children\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_graphs(rows, selected_column):\n",
    "    print(rows)\n",
    "```\n",
    "\n",
    "When we print the `rows`, \n",
    "you can see that the format is a list with one dictionary per row.\n",
    "To get this back to a dataframe,\n",
    "we will use `pd.DataFrame(rows)`.\n",
    "If you try filtering,\n",
    "you will see that fewer rows are returned here."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Controlling plots from the table callback\n",
    "\n",
    "We can now use these callbacks to drive plotting events,\n",
    "just as we would have if we had used a dash core components widget.\n",
    "The following would plot a histogram of the selected column and the filtered data points:\n",
    "\n",
    "```python\n",
    "# An Iframe with the 'histogram' ID is also needed\n",
    "\n",
    "@app.callback(\n",
    "    Output('histogram', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_graphs(rows, selected_column):\n",
    "    chart = alt.Chart(pd.DataFrame(rows)).mark_bar().encode(\n",
    "        alt.X(selected_column[0], bin=True),\n",
    "        alt.Y('count()'))\n",
    "    return chart.to_html()\n",
    "```\n",
    "\n",
    "Here is the entire app when plotting both a histogram and scatter plot\n",
    "based on the state of the table.\n",
    "\n",
    "```python\n",
    "import dash\n",
    "import dash_table\n",
    "import dash_bootstrap_components as dbc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Output, Input\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "\n",
    "cars = data.cars()\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "app.layout = dbc.Container(\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            dash_table.DataTable(\n",
    "                id='table',\n",
    "                columns=[{\"name\": col, \"id\": col, 'selectable': True if col != 'Name' else False} for col in cars.columns[:5]], \n",
    "                data=cars.to_dict('records'),\n",
    "                style_cell={'padding': '5px'},\n",
    "                sort_action=\"native\",\n",
    "                page_action='native',\n",
    "                column_selectable=\"single\",\n",
    "                selected_columns=['Miles_per_Gallon'], \n",
    "                page_size= 10,\n",
    "                filter_action='native',\n",
    "                style_data_conditional=[{\n",
    "                    'if': {'row_index': 'odd'},\n",
    "                    'backgroundColor': 'rgb(248, 248, 248)'}],\n",
    "                 style_header={\n",
    "                    'backgroundColor': 'rgb(230, 230, 230)',\n",
    "                    'fontWeight': 'bold'}),\n",
    "            html.Div(id='output-div'),\n",
    "            html.Iframe(\n",
    "                id='histogram',\n",
    "                style={'border-width': '0', 'width': '100%', 'height': '400px'}\n",
    "            )\n",
    "        ])\n",
    "    ])\n",
    ")\n",
    "\n",
    "@app.callback(\n",
    "    Output('histogram', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_graphs(rows, selected_column):\n",
    "    chart = alt.Chart(pd.DataFrame(rows)).mark_bar().encode(\n",
    "        alt.X(selected_column[0], bin=True),\n",
    "        alt.Y('count()'))\n",
    "    chart2 = alt.Chart(pd.DataFrame(rows)).mark_point().encode(\n",
    "        alt.X(selected_column[0]),\n",
    "        alt.Y('Horsepower'),\n",
    "        tooltip='Name')\n",
    "    return (chart | chart2).to_html()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True\n",
    "```\n",
    "\n",
    "![](images/7/7.07.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Updating a table based on a callback\n",
    "\n",
    "For callbacks from other widgets or from plotly plots,\n",
    "you can treat tables as any other output element\n",
    "and target their `columns` and `data` attributes to update them\n",
    "via a callback function.\n",
    "\n",
    "```python\n",
    "import dash\n",
    "import dash_table\n",
    "import dash_bootstrap_components as dbc\n",
    "import dash_core_components as dcc\n",
    "from dash.dependencies import Output, Input\n",
    "from vega_datasets import data\n",
    "\n",
    "\n",
    "cars = data.cars()\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "app.layout = dbc.Container(\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            dash_table.DataTable(id='table', page_size=5),\n",
    "            dcc.Dropdown(\n",
    "                id='dropdown',\n",
    "                options=[{\"label\": col, \"value\": col} for col in cars.columns],\n",
    "                multi=True,\n",
    "                value=['Horsepower']\n",
    "            ),\n",
    "        ])\n",
    "    ])\n",
    ")\n",
    "\n",
    "@app.callback(\n",
    "    Output('table', 'data'),\n",
    "    Output('table', 'columns'),\n",
    "    Input('dropdown', 'value'))\n",
    "def update_table(cols):\n",
    "    columns=[{\"name\": col, \"id\": col} for col in cols]\n",
    "    data=cars[cols].to_dict('records')\n",
    "    return data, columns\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "![](images/7/7.08.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Updating a table from an Altair plot\n",
    "\n",
    "From altair plots,\n",
    "there is no way to update a dash table,\n",
    "but if you just need to show a few numbers based on a plot selection,\n",
    "you can [use `mark_text()`\n",
    "to update a plot area that looks like a table](https://altair-viz.github.io/gallery/scatter_linked_table.html)."
   ]
  },
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\"Weight_in_lbs\": 2933, \"Acceleration\": 14.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volkswagen 411 (sw)\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 2511, \"Acceleration\": 18.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 504 (sw)\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 87.0, \"Weight_in_lbs\": 2979, \"Acceleration\": 19.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"renault 12 (sw)\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 96.0, \"Horsepower\": 69.0, \"Weight_in_lbs\": 2189, \"Acceleration\": 18.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"ford pinto (sw)\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2395, \"Acceleration\": 16.0, \"Year\": 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{\"Name\": \"buick century 350\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 4100, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3672, \"Acceleration\": 11.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet malibu\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 3988, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 137.0, \"Weight_in_lbs\": 4042, \"Acceleration\": 14.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge coronet custom\", \"Miles_per_Gallon\": 15.0, 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\"Origin\": \"USA\"}, {\"Name\": \"volkswagen super beetle\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 46.0, \"Weight_in_lbs\": 1950, \"Acceleration\": 21.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet impala\", \"Miles_per_Gallon\": 11.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4997, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford country\", \"Miles_per_Gallon\": 12.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 167.0, \"Weight_in_lbs\": 4906, \"Acceleration\": 12.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth custom suburb\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 4654, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile vista 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\"Horsepower\": 94.0, \"Weight_in_lbs\": 2379, \"Acceleration\": 16.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"maxda rx3\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2124, \"Acceleration\": 13.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2310, \"Acceleration\": 18.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury capri v6\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 6, \"Displacement\": 155.0, \"Horsepower\": 107.0, \"Weight_in_lbs\": 2472, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 124 sport coupe\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 15.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet monte carlo s\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4082, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac grand prix\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 230.0, \"Weight_in_lbs\": 4278, \"Acceleration\": 9.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 128\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 68.0, \"Horsepower\": 49.0, \"Weight_in_lbs\": 1867, \"Acceleration\": 19.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"opel manta\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2158, \"Acceleration\": 15.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"audi 100ls\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 4, \"Displacement\": 114.0, \"Horsepower\": 91.0, \"Weight_in_lbs\": 2582, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo 144ea\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 112.0, \"Weight_in_lbs\": 2868, \"Acceleration\": 15.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"dodge dart custom\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3399, \"Acceleration\": 11.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"saab 99le\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2660, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota mark ii\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 122.0, \"Weight_in_lbs\": 2807, \"Acceleration\": 13.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"oldsmobile omega\", \"Miles_per_Gallon\": 11.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 3664, \"Acceleration\": 11.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth duster\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 198.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3102, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": null, \"Weight_in_lbs\": 2875, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2901, \"Acceleration\": 16.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3336, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun b210\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1950, \"Acceleration\": 19.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2451, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla 1200\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 71.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1836, \"Acceleration\": 21.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet vega\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2542, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevelle malibu classic\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3781, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3632, \"Acceleration\": 18.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth satellite sebring\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3613, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino\", 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\"Displacement\": 304.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4257, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"audi fox\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2219, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volkswagen dasher\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1963, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"opel manta\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 14.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 76.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 1649, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 710\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 83.0, \"Horsepower\": 61.0, \"Weight_in_lbs\": 2003, \"Acceleration\": 19.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 14.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 128\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2108, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"fiat 124 tc\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2246, \"Acceleration\": 14.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2489, \"Acceleration\": 15.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"subaru\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 93.0, \"Weight_in_lbs\": 2391, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat x1.9\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2000, \"Acceleration\": 16.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth valiant custom\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3264, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3459, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury monarch\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 3432, \"Acceleration\": 21.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 3158, \"Acceleration\": 19.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac catalina\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 4668, \"Acceleration\": 11.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet bel air\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4440, \"Acceleration\": 14.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth grand fury\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4498, \"Acceleration\": 14.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ltd\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 148.0, \"Weight_in_lbs\": 4657, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick century\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3907, \"Acceleration\": 21.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevroelt chevelle malibu\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3897, \"Acceleration\": 18.5, \"Year\": \"1975-01-01T00:00:00\", 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13.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Weight_in_lbs\": 3169, \"Acceleration\": 12.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2171, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2639, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc gremlin\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2914, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac astro\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2592, \"Acceleration\": 18.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2702, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen dasher\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 2223, \"Acceleration\": 16.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 710\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2545, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 171.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2984, \"Acceleration\": 14.5, \"Year\": 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\"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 98.0, \"Weight_in_lbs\": 2945, \"Acceleration\": 14.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 99le\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2671, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic cvcc\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Weight_in_lbs\": 1795, \"Acceleration\": 17.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat 131\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2464, \"Acceleration\": 15.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"opel 1900\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 81.0, \"Weight_in_lbs\": 2220, \"Acceleration\": 16.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"capri ii\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2572, \"Acceleration\": 14.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 79.0, \"Weight_in_lbs\": 2255, \"Acceleration\": 17.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"renault 12tl\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 101.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2202, \"Acceleration\": 15.3, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet chevelle malibu classic\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4215, \"Acceleration\": 13.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge coronet brougham\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4190, \"Acceleration\": 13.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3962, \"Acceleration\": 13.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino\", \"Miles_per_Gallon\": 14.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 152.0, \"Weight_in_lbs\": 4215, \"Acceleration\": 12.8, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth valiant\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3233, \"Acceleration\": 15.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3353, \"Acceleration\": 14.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 81.0, \"Weight_in_lbs\": 3012, \"Acceleration\": 17.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 22.5, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3085, \"Acceleration\": 17.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 2035, \"Acceleration\": 22.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet woody\", \"Miles_per_Gallon\": 24.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 2164, \"Acceleration\": 22.1, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1937, \"Acceleration\": 14.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Weight_in_lbs\": 1795, \"Acceleration\": 17.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge aspen se\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3651, \"Acceleration\": 17.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada ghia\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 3574, \"Acceleration\": 21.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac ventura sj\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3645, \"Acceleration\": 16.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc pacer d/l\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3193, \"Acceleration\": 17.8, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit\", \"Miles_per_Gallon\": 29.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1825, \"Acceleration\": 12.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun b-210\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1990, \"Acceleration\": 17.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 16.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 26.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 2565, \"Acceleration\": 13.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volvo 245\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 4, \"Displacement\": 130.0, \"Horsepower\": 102.0, \"Weight_in_lbs\": 3150, \"Acceleration\": 15.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth volare premier v8\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3940, \"Acceleration\": 13.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3270, \"Acceleration\": 21.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota mark ii\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 108.0, \"Weight_in_lbs\": 2930, \"Acceleration\": 15.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mercedes-benz 280s\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3820, \"Acceleration\": 16.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"cadillac seville\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 4380, \"Acceleration\": 12.1, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevy c10\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4055, \"Acceleration\": 12.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford f108\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 3870, \"Acceleration\": 15.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge d100\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3755, \"Acceleration\": 14.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda Accelerationord cvcc\", \"Miles_per_Gallon\": 31.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2045, \"Acceleration\": 18.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick opel isuzu deluxe\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 111.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 14.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"renault 5 gtl\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Weight_in_lbs\": 1825, \"Acceleration\": 18.6, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth arrow gs\", \"Miles_per_Gallon\": 25.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 15.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun f-10 hatchback\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1945, \"Acceleration\": 16.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet caprice classic\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 3880, \"Acceleration\": 12.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass supreme\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 4060, \"Acceleration\": 19.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge monaco brougham\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4140, \"Acceleration\": 13.7, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury cougar brougham\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 4295, \"Acceleration\": 14.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet concours\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3520, \"Acceleration\": 16.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skylark\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3425, \"Acceleration\": 16.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth volare custom\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3630, \"Acceleration\": 17.7, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 98.0, \"Weight_in_lbs\": 3525, \"Acceleration\": 19.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac grand prix lj\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 4220, \"Acceleration\": 11.1, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet monte carlo landau\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 4165, \"Acceleration\": 11.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler cordoba\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Weight_in_lbs\": 4325, \"Acceleration\": 12.2, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford thunderbird\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 149.0, \"Weight_in_lbs\": 4335, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit custom\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 1940, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"pontiac sunbird coupe\", \"Miles_per_Gallon\": 24.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2740, \"Acceleration\": 16.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla liftback\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 18.2, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford mustang ii 2+2\", \"Miles_per_Gallon\": 25.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 89.0, \"Weight_in_lbs\": 2755, \"Acceleration\": 15.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 30.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2051, \"Acceleration\": 17.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge colt m/m\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2075, \"Acceleration\": 15.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"subaru dl\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 16.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen dasher\", \"Miles_per_Gallon\": 30.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2190, \"Acceleration\": 14.1, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 810\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2815, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"bmw 320i\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2600, \"Acceleration\": 12.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mazda rx-4\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 3, \"Displacement\": 80.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 13.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen rabbit custom diesel\", \"Miles_per_Gallon\": 43.1, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 21.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"ford fiesta\", \"Miles_per_Gallon\": 36.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 66.0, \"Weight_in_lbs\": 1800, \"Acceleration\": 14.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mazda glc deluxe\", \"Miles_per_Gallon\": 32.8, \"Cylinders\": 4, \"Displacement\": 78.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 19.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun b210 gx\", \"Miles_per_Gallon\": 39.4, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2070, \"Acceleration\": 18.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic cvcc\", \"Miles_per_Gallon\": 36.1, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1800, \"Acceleration\": 16.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"oldsmobile cutlass salon brougham\", \"Miles_per_Gallon\": 19.9, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3365, \"Acceleration\": 15.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge diplomat\", \"Miles_per_Gallon\": 19.4, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 3735, \"Acceleration\": 13.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury monarch ghia\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Weight_in_lbs\": 3570, \"Acceleration\": 12.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix lj\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3535, \"Acceleration\": 19.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet malibu\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3155, \"Acceleration\": 18.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont (auto)\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2965, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont (man)\", \"Miles_per_Gallon\": 25.1, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 15.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth volare\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3430, \"Acceleration\": 17.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord\", \"Miles_per_Gallon\": 19.4, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3210, \"Acceleration\": 17.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick century special\", \"Miles_per_Gallon\": 20.6, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3380, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury zephyr\", \"Miles_per_Gallon\": 20.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3070, \"Acceleration\": 16.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen\", \"Miles_per_Gallon\": 18.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3620, \"Acceleration\": 18.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord d/l\", \"Miles_per_Gallon\": 18.1, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3410, \"Acceleration\": 15.1, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet monte carlo landau\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 3425, \"Acceleration\": 13.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick regal sport coupe (turbo)\", \"Miles_per_Gallon\": 17.7, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 165.0, \"Weight_in_lbs\": 3445, \"Acceleration\": 13.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford futura\", \"Miles_per_Gallon\": 18.1, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Weight_in_lbs\": 3205, \"Acceleration\": 11.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge magnum xe\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4080, \"Acceleration\": 13.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 16.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 27.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2560, \"Acceleration\": 14.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 510\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 14.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge omni\", \"Miles_per_Gallon\": 30.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2230, \"Acceleration\": 14.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota celica gt liftback\", \"Miles_per_Gallon\": 21.1, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2515, \"Acceleration\": 14.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth sapporo\", \"Miles_per_Gallon\": 23.2, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 2745, \"Acceleration\": 16.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile starfire sx\", \"Miles_per_Gallon\": 23.8, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2855, \"Acceleration\": 17.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 200-sx\", \"Miles_per_Gallon\": 23.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2405, \"Acceleration\": 14.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"audi 5000\", \"Miles_per_Gallon\": 20.3, \"Cylinders\": 5, \"Displacement\": 131.0, \"Horsepower\": 103.0, \"Weight_in_lbs\": 2830, \"Acceleration\": 15.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo 264gl\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3140, \"Acceleration\": 13.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 99gle\", \"Miles_per_Gallon\": 21.6, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2795, \"Acceleration\": 15.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 604sl\", \"Miles_per_Gallon\": 16.2, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 133.0, \"Weight_in_lbs\": 3410, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volkswagen scirocco\", \"Miles_per_Gallon\": 31.5, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1990, \"Acceleration\": 14.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda Accelerationord lx\", \"Miles_per_Gallon\": 29.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2135, \"Acceleration\": 16.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"pontiac lemans v6\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 3245, \"Acceleration\": 15.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury zephyr 6\", \"Miles_per_Gallon\": 19.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2990, \"Acceleration\": 18.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont 4\", \"Miles_per_Gallon\": 22.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2890, \"Acceleration\": 17.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord dl 6\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3265, \"Acceleration\": 18.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen 6\", \"Miles_per_Gallon\": 20.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3360, \"Acceleration\": 16.6, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet caprice classic\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 3840, \"Acceleration\": 15.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ltd landau\", \"Miles_per_Gallon\": 17.6, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Weight_in_lbs\": 3725, \"Acceleration\": 13.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury grand marquis\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 138.0, \"Weight_in_lbs\": 3955, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge st. regis\", \"Miles_per_Gallon\": 18.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 135.0, \"Weight_in_lbs\": 3830, \"Acceleration\": 15.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick estate wagon (sw)\", \"Miles_per_Gallon\": 16.9, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Weight_in_lbs\": 4360, \"Acceleration\": 14.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford country squire (sw)\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 142.0, \"Weight_in_lbs\": 4054, \"Acceleration\": 14.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet malibu classic (sw)\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 8, \"Displacement\": 267.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3605, \"Acceleration\": 15.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron town @ country (sw)\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3940, \"Acceleration\": 13.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit custom\", \"Miles_per_Gallon\": 31.9, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1925, \"Acceleration\": 14.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"maxda glc deluxe\", \"Miles_per_Gallon\": 34.1, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1975, \"Acceleration\": 15.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt hatchback custom\", \"Miles_per_Gallon\": 35.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 1915, \"Acceleration\": 14.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc spirit dl\", \"Miles_per_Gallon\": 27.4, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2670, \"Acceleration\": 15.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercedes benz 300d\", \"Miles_per_Gallon\": 25.4, \"Cylinders\": 5, \"Displacement\": 183.0, \"Horsepower\": 77.0, \"Weight_in_lbs\": 3530, \"Acceleration\": 20.1, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"cadillac eldorado\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3900, \"Acceleration\": 17.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 3190, \"Acceleration\": 24.8, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"oldsmobile cutlass salon brougham\", \"Miles_per_Gallon\": 23.9, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3420, \"Acceleration\": 22.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth horizon\", \"Miles_per_Gallon\": 34.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2200, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth horizon tc3\", \"Miles_per_Gallon\": 34.5, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2150, \"Acceleration\": 14.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 31.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2020, \"Acceleration\": 19.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat strada custom\", \"Miles_per_Gallon\": 37.3, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 69.0, \"Weight_in_lbs\": 2130, \"Acceleration\": 14.7, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"buick skylark limited\", \"Miles_per_Gallon\": 28.4, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2670, \"Acceleration\": 16.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 28.8, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2595, \"Acceleration\": 11.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile omega brougham\", \"Miles_per_Gallon\": 26.8, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2700, \"Acceleration\": 12.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2556, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit\", \"Miles_per_Gallon\": 41.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 2144, \"Acceleration\": 14.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corolla tercel\", \"Miles_per_Gallon\": 38.1, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1968, \"Acceleration\": 18.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 32.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2120, \"Acceleration\": 15.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 310\", \"Miles_per_Gallon\": 37.2, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2019, \"Acceleration\": 16.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2678, \"Acceleration\": 16.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont\", \"Miles_per_Gallon\": 26.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2870, \"Acceleration\": 18.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord\", \"Miles_per_Gallon\": 24.3, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3003, \"Acceleration\": 20.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen\", \"Miles_per_Gallon\": 19.1, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3381, \"Acceleration\": 18.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"audi 4000\", \"Miles_per_Gallon\": 34.3, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2188, \"Acceleration\": 15.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corona liftback\", \"Miles_per_Gallon\": 29.8, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2711, \"Acceleration\": 15.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda 626\", \"Miles_per_Gallon\": 31.3, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2542, \"Acceleration\": 17.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 510 hatchback\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2434, \"Acceleration\": 15.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 32.2, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 15.2, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc\", \"Miles_per_Gallon\": 46.6, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2110, \"Acceleration\": 17.9, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 27.9, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 2800, \"Acceleration\": 14.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 40.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2110, \"Acceleration\": 19.2, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"vw rabbit c (diesel)\", \"Miles_per_Gallon\": 44.3, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 2085, \"Acceleration\": 21.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"vw dasher (diesel)\", \"Miles_per_Gallon\": 43.4, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 2335, \"Acceleration\": 23.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"audi 5000s (diesel)\", \"Miles_per_Gallon\": 36.4, \"Cylinders\": 5, \"Displacement\": 121.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2950, \"Acceleration\": 19.9, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mercedes-benz 240d\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 146.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 3250, \"Acceleration\": 21.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic 1500 gl\", \"Miles_per_Gallon\": 44.6, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1850, \"Acceleration\": 13.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"renault lecar deluxe\", \"Miles_per_Gallon\": 40.9, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": null, \"Weight_in_lbs\": 1835, \"Acceleration\": 17.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"subaru dl\", \"Miles_per_Gallon\": 33.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2145, \"Acceleration\": 18.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"vokswagen rabbit\", \"Miles_per_Gallon\": 29.8, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Weight_in_lbs\": 1845, \"Acceleration\": 15.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 280-zx\", \"Miles_per_Gallon\": 32.7, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 132.0, \"Weight_in_lbs\": 2910, \"Acceleration\": 11.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda rx-7 gs\", \"Miles_per_Gallon\": 23.7, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2420, \"Acceleration\": 12.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"triumph tr7 coupe\", \"Miles_per_Gallon\": 35.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2500, \"Acceleration\": 15.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"ford mustang cobra\", \"Miles_per_Gallon\": 23.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": null, \"Weight_in_lbs\": 2905, \"Acceleration\": 14.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda Accelerationord\", \"Miles_per_Gallon\": 32.4, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 2290, \"Acceleration\": 17.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth reliant\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2490, \"Acceleration\": 15.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skylark\", \"Miles_per_Gallon\": 26.6, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2635, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aries wagon (sw)\", \"Miles_per_Gallon\": 25.8, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2620, \"Acceleration\": 14.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 23.5, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2725, \"Acceleration\": 12.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth reliant\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2385, \"Acceleration\": 12.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota starlet\", \"Miles_per_Gallon\": 39.1, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Weight_in_lbs\": 1755, \"Acceleration\": 16.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth champ\", \"Miles_per_Gallon\": 39.0, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 64.0, \"Weight_in_lbs\": 1875, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda civic 1300\", \"Miles_per_Gallon\": 35.1, \"Cylinders\": 4, \"Displacement\": 81.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1760, \"Acceleration\": 16.1, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"subaru\", \"Miles_per_Gallon\": 32.3, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2065, \"Acceleration\": 17.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1975, \"Acceleration\": 19.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota tercel\", \"Miles_per_Gallon\": 37.7, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Weight_in_lbs\": 2050, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc 4\", \"Miles_per_Gallon\": 34.1, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 16.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth horizon 4\", \"Miles_per_Gallon\": 34.7, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2215, \"Acceleration\": 14.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford escort 4w\", \"Miles_per_Gallon\": 34.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2045, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford escort 2h\", \"Miles_per_Gallon\": 29.9, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2380, \"Acceleration\": 20.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen jetta\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 2190, \"Acceleration\": 14.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"renault 18i\", \"Miles_per_Gallon\": 34.5, \"Cylinders\": 4, \"Displacement\": 100.0, \"Horsepower\": null, \"Weight_in_lbs\": 2320, \"Acceleration\": 15.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda prelude\", \"Miles_per_Gallon\": 33.7, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2210, \"Acceleration\": 14.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 32.4, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2350, \"Acceleration\": 16.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 200sx\", \"Miles_per_Gallon\": 32.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2615, \"Acceleration\": 14.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda 626\", \"Miles_per_Gallon\": 31.6, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 2635, \"Acceleration\": 18.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"peugeot 505s turbo diesel\", \"Miles_per_Gallon\": 28.1, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 3230, \"Acceleration\": 20.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 900s\", \"Miles_per_Gallon\": null, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2800, \"Acceleration\": 15.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo diesel\", \"Miles_per_Gallon\": 30.7, \"Cylinders\": 6, \"Displacement\": 145.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 3160, \"Acceleration\": 19.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota cressida\", \"Miles_per_Gallon\": 25.4, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 116.0, \"Weight_in_lbs\": 2900, \"Acceleration\": 12.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 810 maxima\", \"Miles_per_Gallon\": 24.2, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 2930, \"Acceleration\": 13.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick century\", \"Miles_per_Gallon\": 22.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3415, \"Acceleration\": 15.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass ls\", \"Miles_per_Gallon\": 26.6, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3725, \"Acceleration\": 19.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada gl\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3060, \"Acceleration\": 17.1, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron salon\", \"Miles_per_Gallon\": 17.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3465, \"Acceleration\": 16.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2605, \"Acceleration\": 19.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier wagon\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2640, \"Acceleration\": 18.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier 2-door\", \"Miles_per_Gallon\": 34.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2395, \"Acceleration\": 18.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac j2000 se hatchback\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2575, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aries se\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2525, \"Acceleration\": 16.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2735, \"Acceleration\": 18.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont futura\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2865, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord dl\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": null, \"Weight_in_lbs\": 3035, \"Acceleration\": 20.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit l\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 1980, \"Acceleration\": 15.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mazda glc custom l\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2025, \"Acceleration\": 18.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc custom\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 1970, \"Acceleration\": 17.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth horizon miser\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 14.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury lynx l\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"nissan stanza xe\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2160, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda Accelerationord\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2205, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 34.0, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2245, \"Acceleration\": 16.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1965, \"Acceleration\": 15.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic (auto)\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1965, \"Acceleration\": 15.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 310 gx\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1995, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick century limited\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 6, \"Displacement\": 181.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2945, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass ciera (diesel)\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 6, \"Displacement\": 262.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3015, \"Acceleration\": 17.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron medallion\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2585, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada l\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 112.0, \"Weight_in_lbs\": 2835, \"Acceleration\": 14.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota celica gt\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 144.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2665, \"Acceleration\": 13.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge charger 2.2\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2370, \"Acceleration\": 13.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet camaro\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2950, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford mustang gl\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2790, \"Acceleration\": 15.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw pickup\", \"Miles_per_Gallon\": 44.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 2130, \"Acceleration\": 24.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"dodge rampage\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2295, \"Acceleration\": 11.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ranger\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 79.0, \"Weight_in_lbs\": 2625, \"Acceleration\": 18.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevy s-10\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 82.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 19.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "source = data.cars()\n",
    "\n",
    "# Brush for selection\n",
    "brush = alt.selection(type='interval')\n",
    "\n",
    "# Scatter Plot\n",
    "points = alt.Chart(source).mark_point().encode(\n",
    "    x='Horsepower:Q',\n",
    "    y='Miles_per_Gallon:Q',\n",
    "    color=alt.condition(brush, alt.value('steelblue'), alt.value('grey'))\n",
    ").add_selection(brush)\n",
    "\n",
    "# Base chart for data tables\n",
    "ranked_text = alt.Chart(source).mark_text(align='right').encode(\n",
    "    y=alt.Y('row_number:O',axis=None)\n",
    ").transform_window(\n",
    "    row_number='row_number()'\n",
    ").transform_filter(\n",
    "    brush\n",
    ").transform_window(\n",
    "    rank='rank(row_number)'\n",
    ").transform_filter(\n",
    "    alt.datum.rank<16\n",
    ")\n",
    "\n",
    "# Data Tables\n",
    "horsepower = ranked_text.encode(text='Horsepower:N').properties(title=alt.TitleParams(text='Horsepower', align='right'))\n",
    "mpg = ranked_text.encode(text='Miles_per_Gallon:N').properties(title=alt.TitleParams(text='MPG', align='right'))\n",
    "origin = ranked_text.encode(text='Origin:N').properties(title=alt.TitleParams(text='Origin', align='right'))\n",
    "text = alt.hconcat(horsepower, mpg, origin) # Combine data tables\n",
    "\n",
    "# Build chart\n",
    "alt.hconcat(\n",
    "    points,\n",
    "    text\n",
    ").resolve_legend(\n",
    "    color=\"independent\"\n",
    ").configure_view(strokeWidth=0)"
   ]
  },
  {
   "attachments": {
    "85476463-916b-4da4-9fbd-c2b97dc9befa.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dash data tables in R\n",
    "\n",
    "In R, we use `purrr` similar to how we did in a dropdown for creating the columns\n",
    "and the function `df_to_list` from `dashTable` to convert the dataframe to a dash table friendly format.\n",
    "\n",
    "```R\n",
    "library(dash)\n",
    "library(dashTable)\n",
    "library(dplyr)\n",
    "\n",
    "msleep <- ggplot2::msleep\n",
    "\n",
    "app <- Dash$new()\n",
    "\n",
    "app$layout(\n",
    "    dashDataTable(\n",
    "        id = \"table\",\n",
    "        columns = msleep %>%\n",
    "            colnames %>%\n",
    "            purrr::map(function(col) list(name = col, id = col)),\n",
    "        data = df_to_list(msleep),\n",
    "        page_size = 5\n",
    "    )\n",
    ")\n",
    "```\n",
    "\n",
    "![image.png](attachment:85476463-916b-4da4-9fbd-c2b97dc9befa.png)\n",
    "![](images/7/7.09.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Table callbacks in R\n",
    "\n",
    "The most unusual aspect of this callback is the use of `unlist()`\n",
    "to convert the list of column names into a vector of column names\n",
    "that is valid for indexing the dataframe.\n",
    "\n",
    "```\n",
    "library(dash)\n",
    "library(dashTable)\n",
    "library(dplyr)\n",
    "library(dashCoreComponents)\n",
    "library(dashBootstrapComponents)\n",
    "\n",
    "msleep <- ggplot2::msleep\n",
    "\n",
    "app <- Dash$new(external_stylesheets = dbcThemes$BOOTSTRAP)\n",
    "\n",
    "app$layout(\n",
    "    dbcContainer(\n",
    "        dbcCol(list(\n",
    "            dashDataTable(\n",
    "                id = \"table\",\n",
    "                page_size = 5),\n",
    "            dccDropdown(\n",
    "                id = 'dropdown',\n",
    "                    options = msleep %>% \n",
    "                    colnames %>% \n",
    "                    purrr::map(function(col) list(label = col, value = col)),\n",
    "                multi = T,\n",
    "                value = list('name'))\n",
    "            )\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "app$callback(\n",
    "    list(output('table', 'data'),\n",
    "         output('table', 'columns')),\n",
    "    list(input('dropdown', 'value')),\n",
    "    function(cols) {\n",
    "        columns <- cols %>%\n",
    "            purrr::map(function(col) list(name = col, id = col))\n",
    "        data <- df_to_list(msleep %>% select(unlist(cols)))\n",
    "        print(columns)\n",
    "        list(data, columns)\n",
    "    }\n",
    ")\n",
    "\n",
    "app$run_server(debug = T)\n",
    "```\n",
    "\n",
    "![](images/7/7.10.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Styling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting plot titles via cards\n",
    "\n",
    "You can also put local widgets inside these cards to clearly separate them from the global widgets\n",
    "and to indicate which plot they control.\n",
    "\n",
    "```python\n",
    "import dash\n",
    "import dash_table\n",
    "import dash_bootstrap_components as dbc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Output, Input\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "\n",
    "cars = data.cars()\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "app.layout = dbc.Container(\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            dash_table.DataTable(\n",
    "                id='table',\n",
    "                columns=[{\"name\": col, \"id\": col, 'selectable': True if col != 'Name' else False} for col in cars.columns[:5]], \n",
    "                data=cars.to_dict('records'),\n",
    "                style_cell={'padding': '5px'},\n",
    "                sort_action=\"native\",\n",
    "                page_action='native',\n",
    "                column_selectable=\"single\",\n",
    "                selected_columns=['Miles_per_Gallon'], \n",
    "                page_size= 10,\n",
    "                filter_action='native',\n",
    "                style_data_conditional=[{\n",
    "                    'if': {'row_index': 'odd'},\n",
    "                    'backgroundColor': 'rgb(248, 248, 248)'}],\n",
    "                 style_header={\n",
    "                    'backgroundColor': 'rgb(230, 230, 230)',\n",
    "                    'fontWeight': 'bold'}),\n",
    "            html.Br(),\n",
    "            html.Br(),\n",
    "            html.Br(),\n",
    "            dbc.Row([\n",
    "                dbc.Col([\n",
    "                    dbc.Card([\n",
    "                        dbc.CardHeader('Variable distrbution'),\n",
    "                        dbc.CardBody(\n",
    "                            html.Iframe(\n",
    "                                id='histogram',\n",
    "                                style={'border-width': '0', 'width': '100%', 'height': '400px'}))])]),\n",
    "                dbc.Col([\n",
    "                    dbc.Card([\n",
    "                        dbc.CardHeader('Relation to Horsepower'),\n",
    "                        dbc.CardBody(\n",
    "                            html.Iframe(\n",
    "                                id='scatter',\n",
    "                                style={'border-width': '0', 'width': '100%', 'height': '400px'}))])])\n",
    "            ])\n",
    "        ])\n",
    "    ])\n",
    ")\n",
    "\n",
    "@app.callback(\n",
    "    Output('histogram', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_histogram(rows, selected_column):\n",
    "    chart = alt.Chart(pd.DataFrame(rows)).mark_bar().encode(\n",
    "        alt.X(selected_column[0], bin=True),\n",
    "        alt.Y('count()'))\n",
    "    return chart.properties(width=320, height=320).to_html()\n",
    "\n",
    "@app.callback(\n",
    "    Output('scatter', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_scatter(rows, selected_column):\n",
    "    chart2 = alt.Chart(pd.DataFrame(rows)).mark_point().encode(\n",
    "        alt.X(selected_column[0]),\n",
    "        alt.Y('Horsepower'),\n",
    "        tooltip='Name')\n",
    "    return chart2.properties(width=320, height=320).to_html()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "![](images/7/7.11.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an appealing dashboard title\n",
    "\n",
    "```python\n",
    "import dash\n",
    "import dash_table\n",
    "import dash_bootstrap_components as dbc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Output, Input\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "\n",
    "cars = data.cars()\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "app.layout = dbc.Container(\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            # dbc.Jumbotron(dbc.Container(html.H1('Layout demo', className='display-3'), fluid=True), fluid=True),\n",
    "            html.H1('My splashboard demo',\n",
    "                style={\n",
    "                    'backgroundColor': 'steelblue',\n",
    "                    'padding': 20,\n",
    "                    'color': 'white',\n",
    "                    'margin-top': 20,\n",
    "                    'margin-bottom': 20,\n",
    "                    'text-align': 'center',\n",
    "                    'font-size': '48px',\n",
    "                    'border-radius': 3}), #, 'width': 300}),\n",
    "            # jumbotron,\n",
    "            dash_table.DataTable(\n",
    "                id='table',\n",
    "                columns=[{\"name\": col, \"id\": col, 'selectable': True if col != 'Name' else False} for col in cars.columns[:5]], \n",
    "                data=cars.to_dict('records'),\n",
    "                style_cell={'padding': '5px'},\n",
    "                sort_action=\"native\",\n",
    "                page_action='native',\n",
    "                column_selectable=\"single\",\n",
    "                selected_columns=['Miles_per_Gallon'], \n",
    "                page_size= 10,\n",
    "                filter_action='native',\n",
    "                style_data_conditional=[{\n",
    "                    'if': {'row_index': 'odd'},\n",
    "                    'backgroundColor': 'rgb(248, 248, 248)'}],\n",
    "                 style_header={\n",
    "                    'backgroundColor': 'rgb(230, 230, 230)',\n",
    "                    'fontWeight': 'bold'}),\n",
    "            html.Br(),\n",
    "            html.Br(),\n",
    "            html.Br(),\n",
    "            dbc.Row([\n",
    "                dbc.Col([\n",
    "                    dbc.Card([\n",
    "                        dbc.CardHeader('Variable distrbution', style={'fontWeight': 'bold'}),\n",
    "                        dbc.CardBody(\n",
    "                            html.Iframe(\n",
    "                                id='histogram',\n",
    "                                style={'border-width': '0', 'width': '100%', 'height': '400px'}))])]),\n",
    "                dbc.Col([\n",
    "                    dbc.Card([\n",
    "                        dbc.CardHeader('Relation to Horsepower', style={'fontWeight': 'bold'}),\n",
    "                        dbc.CardBody(\n",
    "                            html.Iframe(\n",
    "                                id='scatter',\n",
    "                                style={'border-width': '0', 'width': '100%', 'height': '400px'}))])])\n",
    "            ])\n",
    "        ])\n",
    "    ])\n",
    ")\n",
    "\n",
    "@app.callback(\n",
    "    Output('histogram', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_histogram(rows, selected_column):\n",
    "    chart = alt.Chart(pd.DataFrame(rows)).mark_bar().encode(\n",
    "        alt.X(selected_column[0], bin=True),\n",
    "        alt.Y('count()'))\n",
    "    return chart.properties(width=320, height=320).to_html()\n",
    "\n",
    "@app.callback(\n",
    "    Output('scatter', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_scatter(rows, selected_column):\n",
    "    chart2 = alt.Chart(pd.DataFrame(rows)).mark_point().encode(\n",
    "        alt.X(selected_column[0]),\n",
    "        alt.Y('Horsepower'),\n",
    "        tooltip='Name')\n",
    "    return chart2.properties(width=320, height=320).to_html()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "![](images/7/7.12.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a sidebar for global widgets\n",
    "\n",
    "```python\n",
    "import dash\n",
    "import dash_table\n",
    "import dash_bootstrap_components as dbc\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Output, Input\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "\n",
    "cars = data.cars()\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "app.layout = dbc.Container([\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            html.H1('My splashboard demo',\n",
    "                style={\n",
    "                    'color': 'white',\n",
    "                    'text-align': 'left',\n",
    "                    'font-size': '48px',\n",
    "                    }) #, 'width': 300}),\n",
    "\n",
    "        ], style={'backgroundColor': 'steelblue',\n",
    "                    'border-radius': 3,\n",
    "                    'padding': 15,\n",
    "                    'margin-top': 20,\n",
    "                    'margin-bottom': 20,\n",
    "                    'margin-right': 15\n",
    "        })\n",
    "                    \n",
    "    ]),\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            html.H5('Global controls'),\n",
    "            html.Br(),\n",
    "            dcc.Dropdown(),\n",
    "            html.Br(),\n",
    "            dcc.Dropdown(),\n",
    "            html.Br(),\n",
    "            dcc.Dropdown(),\n",
    "            html.Br(),\n",
    "            dcc.Dropdown(),\n",
    "            ],\n",
    "            md=2,\n",
    "            style={\n",
    "                'background-color': '#e6e6e6',\n",
    "                'padding': 15,\n",
    "                'border-radius': 3}), \n",
    "        dbc.Col([\n",
    "            dash_table.DataTable(\n",
    "                id='table',\n",
    "                columns=[{\"name\": col, \"id\": col, 'selectable': True if col != 'Name' else False} for col in cars.columns[:5]], \n",
    "                data=cars.to_dict('records'),\n",
    "                style_cell={'padding': '5px'},\n",
    "                sort_action=\"native\",\n",
    "                page_action='native',\n",
    "                column_selectable=\"single\",\n",
    "                selected_columns=['Miles_per_Gallon'], \n",
    "                page_size= 10,\n",
    "                filter_action='native',\n",
    "                style_data_conditional=[{\n",
    "                    'if': {'row_index': 'odd'},\n",
    "                    'backgroundColor': 'rgb(248, 248, 248)'}],\n",
    "                 style_header={\n",
    "                    'backgroundColor': 'rgb(230, 230, 230)',\n",
    "                    'fontWeight': 'bold'}),\n",
    "            html.Br(),\n",
    "            html.Br(),\n",
    "            html.Br(),\n",
    "            dbc.Row([\n",
    "                dbc.Col([\n",
    "                    dbc.Card([\n",
    "                        dbc.CardHeader('Variable distrbution', style={'fontWeight': 'bold'}),\n",
    "                        dbc.CardBody(\n",
    "                            html.Iframe(\n",
    "                                id='histogram',\n",
    "                                style={'border-width': '0', 'width': '100%', 'height': '400px'}))])]),\n",
    "                dbc.Col([\n",
    "                    dbc.Card([\n",
    "                        dbc.CardHeader('Relation to Horsepower', style={'fontWeight': 'bold'}),\n",
    "                        dbc.CardBody(\n",
    "                            html.Iframe(\n",
    "                                id='scatter',\n",
    "                                style={'border-width': '0', 'width': '100%', 'height': '400px'}))])])\n",
    "            ])\n",
    "        ])\n",
    "    ]),\n",
    "    html.Hr(),\n",
    "    html.P('This dashboard was made by Joel, link to GitHub source. The data is from here and there (include links if appropriate), some copyright/license info')\n",
    "])\n",
    "\n",
    "@app.callback(\n",
    "    Output('histogram', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_histogram(rows, selected_column):\n",
    "    chart = alt.Chart(pd.DataFrame(rows)).mark_bar().encode(\n",
    "        alt.X(selected_column[0], bin=True),\n",
    "        alt.Y('count()'))\n",
    "    return chart.properties(width=320, height=320).to_html()\n",
    "\n",
    "@app.callback(\n",
    "    Output('scatter', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_scatter(rows, selected_column):\n",
    "    chart2 = alt.Chart(pd.DataFrame(rows)).mark_point().encode(\n",
    "        alt.X(selected_column[0]),\n",
    "        alt.Y('Horsepower'),\n",
    "        tooltip='Name')\n",
    "    return chart2.properties(width=320, height=320).to_html()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "![](images/7/7.13.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding introductory text and references to code and data\n",
    "\n",
    "```python\n",
    "import dash\n",
    "import dash_table\n",
    "import dash_bootstrap_components as dbc\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Output, Input, State\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "\n",
    "cars = data.cars()\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "collapse = html.Div(\n",
    "    [\n",
    "        dbc.Button(\n",
    "            \"Learn more\",\n",
    "            id=\"collapse-button\",\n",
    "            className=\"mb-3\",\n",
    "            outline=False,\n",
    "            style={'margin-top': '10px',\n",
    "                'width': '150px',\n",
    "                'background-color': 'white',\n",
    "                'color': 'steelblue'}\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"collapse\", \"is_open\"),\n",
    "    [Input(\"collapse-button\", \"n_clicks\")],\n",
    "    [State(\"collapse\", \"is_open\")],\n",
    ")\n",
    "def toggle_collapse(n, is_open):\n",
    "    if n:\n",
    "        return not is_open\n",
    "    return is_open\n",
    "\n",
    "app.layout = dbc.Container([\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            dbc.Row([\n",
    "            dbc.Col([\n",
    "                html.H1('My splashboard demo',\n",
    "                    style={\n",
    "                        'color': 'white',\n",
    "                        'text-align': 'left',\n",
    "                        'font-size': '48px',  #, 'width': 300}),\n",
    "                        }),\n",
    "               dbc.Collapse(\n",
    "                    html.P(\"\"\"\n",
    "                        This dashboard is helping you understand x, y, and z, \n",
    "                        which are really important because a, b, c.\n",
    "                        Start using the dashboard by clicking on 1, 2, 3\n",
    "                        and pulling i, ii, and iii.\"\"\",\n",
    "                        style={'color': 'white', 'width': '50%'}),\n",
    "                    id=\"collapse\",\n",
    "        ),\n",
    "\n",
    "                    ],\n",
    "                    md=10),\n",
    "                dbc.Col([collapse])\n",
    "\n",
    "                \n",
    "            ])\n",
    "        ], style={'backgroundColor': 'steelblue',\n",
    "                    'border-radius': 3,\n",
    "                    'padding': 15,\n",
    "                    'margin-top': 20,\n",
    "                    'margin-bottom': 20,\n",
    "                    'margin-right': 15\n",
    "        })\n",
    "                    \n",
    "    ]),\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            html.H5('Global controls'),\n",
    "            html.Br(),\n",
    "            dcc.Dropdown(),\n",
    "            html.Br(),\n",
    "            dcc.Dropdown(),\n",
    "            html.Br(),\n",
    "            dcc.Dropdown(),\n",
    "            html.Br(),\n",
    "            dcc.Dropdown(),\n",
    "            ],\n",
    "            md=2,\n",
    "            style={\n",
    "                'background-color': '#e6e6e6',\n",
    "                'padding': 15,\n",
    "                'border-radius': 3}), \n",
    "        dbc.Col([\n",
    "            dash_table.DataTable(\n",
    "                id='table',\n",
    "                columns=[{\"name\": col, \"id\": col, 'selectable': True if col != 'Name' else False} for col in cars.columns[:5]], \n",
    "                data=cars.to_dict('records'),\n",
    "                style_cell={'padding': '5px'},\n",
    "                sort_action=\"native\",\n",
    "                page_action='native',\n",
    "                column_selectable=\"single\",\n",
    "                selected_columns=['Miles_per_Gallon'], \n",
    "                page_size= 10,\n",
    "                filter_action='native',\n",
    "                style_data_conditional=[{\n",
    "                    'if': {'row_index': 'odd'},\n",
    "                    'backgroundColor': 'rgb(248, 248, 248)'}],\n",
    "                 style_header={\n",
    "                    'backgroundColor': 'rgb(230, 230, 230)',\n",
    "                    'fontWeight': 'bold'}),\n",
    "            html.Br(),\n",
    "            html.Br(),\n",
    "            html.Br(),\n",
    "            dbc.Row([\n",
    "                dbc.Col([\n",
    "                    dbc.Card([\n",
    "                        dbc.CardHeader('Variable distrbution', style={'fontWeight': 'bold'}),\n",
    "                        dbc.CardBody(\n",
    "                            html.Iframe(\n",
    "                                id='histogram',\n",
    "                                style={'border-width': '0', 'width': '100%', 'height': '400px'}))])]),\n",
    "                dbc.Col([\n",
    "                    dbc.Card([\n",
    "                        dbc.CardHeader('Relation to Horsepower', style={'fontWeight': 'bold'}),\n",
    "                        dbc.CardBody(\n",
    "                            html.Iframe(\n",
    "                                id='scatter',\n",
    "                                style={'border-width': '0', 'width': '100%', 'height': '400px'}))])])\n",
    "            ])\n",
    "        ])\n",
    "    ]),\n",
    "    html.Hr(),\n",
    "    html.P(f'''\n",
    "    This dashboard was made by Joel, link to GitHub source.\n",
    "    The data is from here and there (include links if appropriate), some copyright/license info\n",
    "    Mention when the dashboard was latest updated (and data if appropriate).\n",
    "    This will show the date when you last resarted the server: {datetime.now().date()}\n",
    "    ''')\n",
    "])\n",
    "@app.callback(\n",
    "    Output('histogram', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_histogram(rows, selected_column):\n",
    "    chart = alt.Chart(pd.DataFrame(rows)).mark_bar().encode(\n",
    "        alt.X(selected_column[0], bin=True),\n",
    "        alt.Y('count()'))\n",
    "    return chart.properties(width=300, height=300).to_html()\n",
    "\n",
    "@app.callback(\n",
    "    Output('scatter', \"srcDoc\"),\n",
    "    Input('table', \"derived_virtual_data\"),\n",
    "    Input('table', \"selected_columns\"))\n",
    "def update_scatter(rows, selected_column):\n",
    "    chart2 = alt.Chart(pd.DataFrame(rows)).mark_point().encode(\n",
    "        alt.X(selected_column[0]),\n",
    "        alt.Y('Horsepower'),\n",
    "        tooltip='Name')\n",
    "    return chart2.properties(width=300, height=300).to_html()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "![](images/7/7.14.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data from binary formats\n",
    "\n",
    "If you initial load times are slow,\n",
    "consider storing the processed data (still keep a copy of the raw)\n",
    "as a binary file format.\n",
    "[According to the pandas docs,\n",
    "feather and hdf5 are the fastest](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#performance-considerations).\n",
    "If you have complex data structures like nested lists,\n",
    "these might not work,\n",
    "but saving to a `pickle` (Python's native binary format)\n",
    "still should and is also very fast.\n",
    "\n",
    "When working with `altair` and large data sets,\n",
    "you can enable the altair data server like we did in 531\n",
    "in order to avoid writing all the data to each plot's json \n",
    "which can take a long time.\n",
    "[Uploading it to your server as a compatible json format (`data.to_json(path, orient='records'`)\n",
    "and reading directly from that path/URL is also a good approach](https://altair-viz.github.io/user_guide/faq.html?highlight=url#why-does-altair-lead-to-such-extremely-large-notebooks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caching/memoization\n",
    "\n",
    "If we have a time consuming computation step that generates an output,\n",
    "we can save that output to the file system (or a database)\n",
    "so that next time we don't have to compute it again but just read it in.\n",
    "Trading space for time like this\n",
    "is called chaching or memoization.\n",
    "\n",
    "First let's create an app that simulates a timeconsuming step,\n",
    "by sleeping the process for a few seconds between each call.\n",
    "Every time we select from the dropdown menu,\n",
    "this app will do the time consuming process of reading in the data again,\n",
    "so we have to wait a couple of seconds for the output to refresh.\n",
    "If we look at the timestamp in the `time` column,\n",
    "we can see that it is always the same\n",
    "as the current time\n",
    "(because it is rerun each time the dropdown changes).\n",
    "\n",
    "```python\n",
    "import time\n",
    "from datetime import datetime\n",
    "\n",
    "import dash\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dash.dependencies import Input, Output\n",
    "\n",
    "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n",
    "\n",
    "def query_data():\n",
    "    # Simulate a time consuming step with sleep\n",
    "    time.sleep(2)\n",
    "    df = pd.DataFrame(\n",
    "        np.random.randint(0, 100, size=(100, 4)),\n",
    "        columns=list('ABCD'))\n",
    "    df['time'] = datetime.now().strftime('%H:%M:%S')\n",
    "    return df.to_json(date_format='iso', orient='split')\n",
    "\n",
    "\n",
    "def dataframe():\n",
    "    return pd.read_json(query_data(), orient='split')\n",
    "\n",
    "app.layout = html.Div([\n",
    "    html.H6(id='current-time'),\n",
    "    dcc.Dropdown(\n",
    "        id='live-dropdown',\n",
    "        value='A',\n",
    "        options=[{'label': i, 'value': i} for i in dataframe().columns]),\n",
    "    html.Iframe(\n",
    "        id='output-area',\n",
    "        style={'border-width': '0', 'width': '100%', 'height': '400px'})])\n",
    "\n",
    "\n",
    "@app.callback(Output('output-area', 'srcDoc'),\n",
    "              Output('current-time', 'children'),\n",
    "              Input('live-dropdown', 'value'))\n",
    "def update_live_graph(col):\n",
    "    print(col)\n",
    "    df = dataframe()\n",
    "    current_time = f\"The current time is {datetime.now().strftime('%H:%M:%S')}\"\n",
    "    return df[[col]].to_html(index=False), current_time\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "![](images/7/7.15.png)\n",
    "\n",
    "Ideally,\n",
    "we would do most of our timeconsuming wrangling in the beginning when the page load\n",
    "or even better,\n",
    "beforehand and save a processed dataset,\n",
    "but there might be some wrangling or other time consuming process that can't be avoided.\n",
    "Often we don't need these process to update each time a selection is made,\n",
    "and we would be fine reusing the data from the selection made a few seconds ago.\n",
    "We can set this up using the [flask-caching](https://flask-caching.readthedocs.io/en/latest/) package\n",
    "(`conda install flask-caching`) like this:\n",
    "\n",
    "```python\n",
    "from flask_caching import Cache\n",
    "\n",
    "\n",
    "cache = Cache(app.server, config={\n",
    "    'CACHE_TYPE': 'filesystem',\n",
    "    'CACHE_DIR': 'cache-directory'})\n",
    "\n",
    "@cache.memoize()\n",
    "def query_date():\n",
    "    ...\n",
    "```\n",
    "\n",
    "The decorator indicates which function to be cached,\n",
    "and the return value from this function will be save on the local filesystem\n",
    "in a directory name `cache-directory`.\n",
    "Our entire app now looks like this and you can see when you select a new dropdown value\n",
    "that the time column will remain the same because it is not being rerun each time,\n",
    "instead it is reusing data from an earlier run.\n",
    "\n",
    "```python\n",
    "import time\n",
    "from datetime import datetime\n",
    "\n",
    "import dash\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dash.dependencies import Input, Output\n",
    "from flask_caching import Cache\n",
    "\n",
    "\n",
    "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n",
    "\n",
    "cache = Cache(app.server, config={\n",
    "    'CACHE_TYPE': 'filesystem',\n",
    "    'CACHE_DIR': 'cache-directory'})\n",
    "\n",
    "@cache.memoize()\n",
    "def query_data():\n",
    "    # Simulate a time consuming step with sleep\n",
    "    time.sleep(2)\n",
    "    df = pd.DataFrame(\n",
    "        np.random.randint(0, 100, size=(100, 4)),\n",
    "        columns=list('ABCD'))\n",
    "    df['time'] = datetime.now().strftime('%H:%M:%S')\n",
    "    return df.to_json(date_format='iso', orient='split')\n",
    "\n",
    "\n",
    "def dataframe():\n",
    "    return pd.read_json(query_data(), orient='split')\n",
    "\n",
    "app.layout = html.Div([\n",
    "    html.H6(id='current-time'),\n",
    "    dcc.Dropdown(\n",
    "        id='live-dropdown',\n",
    "        value='A',\n",
    "        options=[{'label': i, 'value': i} for i in dataframe().columns]),\n",
    "    html.Iframe(\n",
    "        id='output-area',\n",
    "        style={'border-width': '0', 'width': '100%', 'height': '400px'})])\n",
    "\n",
    "@app.callback(Output('output-area', 'srcDoc'),\n",
    "              Output('current-time', 'children'),\n",
    "              Input('live-dropdown', 'value'))\n",
    "def update_live_graph(col):\n",
    "    print(col)\n",
    "    df = dataframe()\n",
    "    current_time = f\"The current time is {datetime.now().strftime('%H:%M:%S')}\"\n",
    "    return df[[col]].to_html(index=False), current_time\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "\n",
    "![](images/7/7.16.png)\n",
    "\n",
    "We could also add a timeout (in seconds) for how long the cache should be saved.\n",
    "For example, if the dropdown is used after 20 seconds, \n",
    "the function will run again.\n",
    "\n",
    "```\n",
    "@cache.memoize(timeout=20)\n",
    "```\n",
    "\n",
    "There are [more details on caching in the dash docs](https://dash.plotly.com/performance)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entertaining the user\n",
    "\n",
    "Another approach to dealing with slow callbacks is to clearly show the user that the page is loading.\n",
    "[Dash has built in support for different loading animations](https://dash.plotly.com/dash-core-components/loading)\n",
    "that can be triggered by wrapping an output component in `dcc.Loading`.\n",
    "\n",
    "```python\n",
    "import time\n",
    "from datetime import datetime\n",
    "\n",
    "import dash\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dash.dependencies import Input, Output\n",
    "\n",
    "\n",
    "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n",
    "\n",
    "def query_data():\n",
    "    # Simulate a time consuming step with sleep\n",
    "    time.sleep(2)\n",
    "    df = pd.DataFrame(\n",
    "        np.random.randint(0, 100, size=(100, 4)),\n",
    "        columns=list('ABCD'))\n",
    "    df['time'] = datetime.now().strftime('%H:%M:%S')\n",
    "    return df.to_json(date_format='iso', orient='split')\n",
    "\n",
    "\n",
    "def dataframe():\n",
    "    return pd.read_json(query_data(), orient='split')\n",
    "\n",
    "app.layout = html.Div([\n",
    "    html.H6(id='current-time'),\n",
    "    dcc.Dropdown(\n",
    "        id='live-dropdown',\n",
    "        value='A',\n",
    "        options=[{'label': i, 'value': i} for i in dataframe().columns]),\n",
    "    dcc.Loading(\n",
    "        children=html.Iframe(\n",
    "            id='output-area',\n",
    "            style={'border-width': '0', 'width': '100%', 'height': '400px'}))])\n",
    "\n",
    "@app.callback(Output('output-area', 'srcDoc'),\n",
    "              Output('current-time', 'children'),\n",
    "              Input('live-dropdown', 'value'))\n",
    "def update_live_graph(col):\n",
    "    print(col)\n",
    "    df = dataframe()\n",
    "    current_time = f\"The current time is {datetime.now().strftime('%H:%M:%S')}\"\n",
    "    return df[[col]].to_html(index=False), current_time\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n",
    "```\n",
    "\n",
    "You can change the type to any of `'graph'`, `'cube'`, `'circle'`, or `'dot'`.\n",
    "But don't overdo it!\n",
    "You don't want to give an not so serious imporession and undermine your carefully crafted visualizations,\n",
    "by having too many toy-like animations in your dashboards.\n",
    "\n",
    "![](images/7/7.17.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running on multiple workers\n",
    "\n",
    "You can run your server on multiple threads locally via setting `app.run_server(processes=4)` or similar.\n",
    "This might not work,\n",
    "and for running it in a production environment\n",
    "you would use this instead in your `Procfile`: `gunicorn --workers 6 --threads 2 src/app:server --pythonpath=src`.\n",
    "I don't think there is an equivalent for dash R.\n",
    "You can see [an example on this page for more details](https://dash.plotly.com/sharing-data-between-callbacks).\n",
    "\n",
    "Note that this is one of the reasons it is a bad idea to modify global variables inside a functions.\n",
    "The dash docs explain why this can lead to unintended consequences and miscomputations in your app:\n",
    "\n",
    "> When Dash apps run across multiple workers, their memory is not shared. This means that if you modify a global variable in one callback, that modification will not be applied to the rest of the workers.\n",
    ">\n",
    "> https://dash.plotly.com/sharing-data-between-callbacks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Better plot performance for many data points\n",
    "\n",
    "\n",
    "These options are only available via Plotly, not Altair.\n",
    "For Altair, [work is under way](https://github.com/vega/scalable-vega) to make it handle larger datasets,\n",
    "but still not ready to use easily."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WebGL for millions of points\n",
    "\n",
    "WebGL is a Javascript library that allows for GPU rendered graphics inside browsers.\n",
    "Leveraging this, it is possible to render millions of points without stuttering.\n",
    "WebGL backends are available for Holoviews, Bokeh, and Plotly\n",
    "([WebGL for Python](https://plotly.com/r/webgl-vs-svg/) and [WebGL for R](https://plotly.com/r/webgl-vs-svg/))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datashader for billion of points\n",
    "\n",
    "The Python package [Datashader](https://datashader.org/) works by rendering plots to images of lower resolution\n",
    "when you are looking at them from far away\n",
    "(since you cannot distinguish billions of points anyways),\n",
    "and then increases the resolution of the image as you zoom in\n",
    "on an area (but then only renders the data set within the zoomed in region).\n",
    "\n",
    "Datashader is developed by the Anaconda and Holoviews team,\n",
    "and is available to use directly with Holoviews, Bokeh and [Plotly](https://plotly.com/python/datashader/) (in Python only).\n",
    "Since it creates an image it can also be used with any other library\n",
    "that can show images (such as Altair),\n",
    "but it is much easier and feature filled when used with a library that directly integrates it,\n",
    "like the three mentioned above.\n",
    "Datashader can create beautiful heatmap images,\n",
    "and you [view some datashader examples in this gallery](https://plotly.com/python/datashader/),\n",
    "e.g. [this one about ship traffic along the US coast](https://examples.pyviz.org/ship_traffic/ship_traffic.html#ship-traffic-gallery-ship-traffic).\n",
    "\n",
    "![](images/7/7.18.png)\n",
    "\n",
    "#### rasterly for R\n",
    "\n",
    "Plotly has developed the [rasterly library to bring some of the functionality of datashader to R](https://z267xu.github.io/rasterly/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attribution\n",
    "\n",
    "These lecture notes were prepared by Dr. Joel Ostblom, a post-doctoral teaching fellow in the UBC Vancouver MDS program."
   ]
  }
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