02-Introduction to Altair.ipynb

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   "source": [
    "# Altair: Statistical Visualization for Python"
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    "[Altair](http://github.com/altair-viz/altair/) provides a declarative Python API for statistical visualization, built on top of [Vega-Lite](http://vega.github.io/vega-lite/).\n",
    "\n",
    "The notebooks listed here provide a set of tutorials and examples of Altair's use. For more complete documentation, see [Altair's Documentation](http://altair-viz.github.io/)."
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   "source": [
    "## Quick Altair example"
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   "source": [
    "Here is a quick example of the Altair API in action:"
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\"Year\": \"1973-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 46.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"volkswagen super beetle\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1950, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 11.0, \"Name\": \"chevrolet impala\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4997, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 167.0, \"Miles_per_Gallon\": 12.0, \"Name\": \"ford country\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4906, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 170.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"plymouth custom suburb\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4654, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 350.0, 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350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"chevrolet monte carlo s\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4082, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 9.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 230.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4278, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 19.5, \"Cylinders\": 4, \"Displacement\": 68.0, \"Horsepower\": 49.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"fiat 128\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1867, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"opel manta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2158, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 114.0, \"Horsepower\": 91.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"audi 100ls\", \"Origin\": \"Europe\", 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\"Displacement\": 350.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 11.0, \"Name\": \"oldsmobile omega\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3664, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 6, \"Displacement\": 198.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"plymouth duster\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3102, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": null, \"Miles_per_Gallon\": 21.0, \"Name\": \"ford maverick\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2875, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"amc hornet\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2901, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"chevrolet nova\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3336, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"datsun b210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1950, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2451, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 4, \"Displacement\": 71.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"toyota corolla 1200\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1836, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"chevrolet vega\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2542, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"chevrolet chevelle malibu classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3781, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3632, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"plymouth satellite sebring\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3613, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"ford gran torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4141, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"buick century luxus (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4699, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"dodge coronet custom (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4457, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"ford gran torino (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4638, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"amc matador (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4257, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"audi fox\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2219, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1963, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"opel manta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2300, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 76.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1649, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 83.0, \"Horsepower\": 61.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"datsun 710\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2003, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, 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{\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"fiat x1.9\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2000, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth valiant custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3264, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"chevrolet nova\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3459, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"mercury monarch\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3432, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 19.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, 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\"Year\": \"1975-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"buick century\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3907, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 18.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"chevroelt chevelle malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3897, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3730, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"plymouth fury\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3785, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 6, \"Displacement\": 231.0, 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119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"datsun 710\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2545, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 171.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2984, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"volkswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1937, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"amc pacer\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3211, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 115.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"audi 100ls\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2694, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2957, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 98.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"volvo 244dl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2945, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"saab 99le\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2671, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"honda civic cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1795, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"fiat 131\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2464, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 81.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"opel 1900\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2220, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"capri ii\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2572, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 79.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2255, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 101.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"renault 12tl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2202, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet chevelle malibu classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"dodge coronet brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4190, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.9, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3962, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 152.0, \"Miles_per_Gallon\": 14.5, \"Name\": \"ford gran torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"plymouth valiant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3233, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"chevrolet nova\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3353, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.6, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 81.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"ford maverick\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3012, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.6, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 22.5, \"Name\": \"amc hornet\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3085, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 22.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2035, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 22.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"chevrolet woody\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2164, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1937, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1795, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"dodge aspen se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3651, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"ford granada ghia\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3574, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"pontiac ventura sj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3645, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.8, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"amc pacer d/l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3193, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.2, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 29.5, \"Name\": \"volkswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1825, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"datsun b-210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1990, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2155, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 26.5, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2565, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 130.0, \"Horsepower\": 102.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"volvo 245\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3150, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"plymouth volare premier v8\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 21.9, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3270, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 108.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"toyota mark ii\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"mercedes-benz 280s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3820, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.1, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"cadillac seville\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4380, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevy c10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4055, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford f108\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3870, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"dodge d100\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3755, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"honda Accelerationord cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2045, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 111.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"buick opel isuzu deluxe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"renault 5 gtl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1825, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"plymouth arrow gs\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2300, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"datsun f-10 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1945, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3880, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"oldsmobile cutlass supreme\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4060, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 13.7, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"dodge monaco brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4140, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"mercury cougar brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4295, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet concours\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3520, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.9, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"buick skylark\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3425, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 17.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth volare custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3630, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 98.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"ford granada\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3525, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 11.1, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4220, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 11.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 170.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4165, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.2, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chrysler cordoba\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4325, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 149.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"ford thunderbird\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4335, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"volkswagen rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1940, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"pontiac sunbird coupe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2740, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"toyota corolla liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 89.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"ford mustang ii 2+2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2755, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2051, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.9, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"dodge colt m/m\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2075, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.1, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"datsun 810\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2815, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"bmw 320i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2600, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 80.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"mazda rx-4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2720, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 21.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.1, \"Name\": \"volkswagen rabbit custom diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 66.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"ford fiesta\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 78.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 32.8, \"Name\": \"mazda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 39.4, \"Name\": \"datsun b210 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2070, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"honda civic cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 19.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3365, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"dodge diplomat\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3735, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"mercury monarch ghia\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3570, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"pontiac phoenix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3535, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"chevrolet malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3155, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford fairmont (auto)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2965, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 25.1, \"Name\": \"ford fairmont (man)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"plymouth volare\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3430, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3210, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"buick century special\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3380, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.8, \"Name\": \"mercury zephyr\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3070, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 18.6, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3620, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.1, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"amc concord d/l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3425, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 165.0, \"Miles_per_Gallon\": 17.7, \"Name\": \"buick regal sport coupe (turbo)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3445, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 11.2, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"ford futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3205, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.7, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"dodge magnum xe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4080, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 27.5, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2560, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"datsun 510\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2300, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 30.9, \"Name\": \"dodge omni\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2230, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 21.1, \"Name\": \"toyota celica gt liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2515, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 23.2, \"Name\": \"plymouth sapporo\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2745, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 23.8, \"Name\": \"oldsmobile starfire sx\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2855, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"datsun 200-sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2405, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.9, \"Cylinders\": 5, \"Displacement\": 131.0, \"Horsepower\": 103.0, \"Miles_per_Gallon\": 20.3, \"Name\": \"audi 5000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2830, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.6, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"volvo 264gl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3140, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.6, \"Name\": \"saab 99gle\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2795, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 133.0, \"Miles_per_Gallon\": 16.2, \"Name\": \"peugeot 604sl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"volkswagen scirocco\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1990, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 29.5, \"Name\": \"honda Accelerationord lx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2135, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"pontiac lemans v6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3245, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 19.8, \"Name\": \"mercury zephyr 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2990, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 22.3, \"Name\": \"ford fairmont 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2890, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"amc concord dl 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3265, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"dodge aspen 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3360, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3840, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.4, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Miles_per_Gallon\": 17.6, \"Name\": \"ford ltd landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3725, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 138.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"mercury grand marquis\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3955, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 135.0, \"Miles_per_Gallon\": 18.2, \"Name\": \"dodge st. regis\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3830, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Miles_per_Gallon\": 16.9, \"Name\": \"buick estate wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4360, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.3, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 142.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"ford country squire (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4054, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 267.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet malibu classic (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3605, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"chrysler lebaron town @ country (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.9, \"Name\": \"vw rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1925, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 34.1, \"Name\": \"maxda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 35.7, \"Name\": \"dodge colt hatchback custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1915, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 27.4, \"Name\": \"amc spirit dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2670, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 20.1, \"Cylinders\": 5, \"Displacement\": 183.0, \"Horsepower\": 77.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"mercedes benz 300d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3530, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 17.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"cadillac eldorado\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3900, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 24.8, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3190, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 22.2, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3420, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.2, \"Name\": \"plymouth horizon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2200, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.5, \"Name\": \"plymouth horizon tc3\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2150, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 31.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2020, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 69.0, \"Miles_per_Gallon\": 37.3, \"Name\": \"fiat strada custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.4, \"Name\": \"buick skylark limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2670, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 11.3, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 28.8, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2595, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 12.9, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 26.8, \"Name\": \"oldsmobile omega brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2700, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2556, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 41.5, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2144, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.8, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 38.1, \"Name\": \"toyota corolla tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1968, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 32.1, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2120, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.2, \"Name\": \"datsun 310\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2019, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2678, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.1, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 26.4, \"Name\": \"ford fairmont\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2870, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 20.1, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 24.3, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3003, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.1, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3381, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 34.3, \"Name\": \"audi 4000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2188, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"toyota corona liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2711, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 31.3, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2542, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 510 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2434, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.2, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.9, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 46.6, \"Name\": \"mazda glc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 27.9, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2800, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 40.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 21.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 44.3, \"Name\": \"vw rabbit c (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2085, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 23.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.4, \"Name\": \"vw dasher (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2335, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 19.9, \"Cylinders\": 5, \"Displacement\": 121.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 36.4, \"Name\": \"audi 5000s (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2950, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 21.8, \"Cylinders\": 4, \"Displacement\": 146.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"mercedes-benz 240d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3250, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 13.8, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 44.6, \"Name\": \"honda civic 1500 gl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1850, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": null, \"Miles_per_Gallon\": 40.9, \"Name\": \"renault lecar deluxe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1835, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 33.8, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2145, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"vokswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1845, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 11.4, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 132.0, \"Miles_per_Gallon\": 32.7, \"Name\": \"datsun 280-zx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2910, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 23.7, \"Name\": \"mazda rx-7 gs\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2420, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.1, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 35.0, \"Name\": \"triumph tr7 coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2500, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 14.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.6, \"Name\": \"ford mustang cobra\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2905, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2290, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2490, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"buick skylark\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 25.8, \"Name\": \"dodge aries wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2620, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 23.5, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2725, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.9, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2385, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 39.1, \"Name\": \"toyota starlet\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1755, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 64.0, \"Miles_per_Gallon\": 39.0, \"Name\": \"plymouth champ\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1875, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.1, \"Cylinders\": 4, \"Displacement\": 81.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 35.1, \"Name\": \"honda civic 1300\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1760, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.3, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2065, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 37.7, \"Name\": \"toyota tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2050, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 34.1, \"Name\": \"mazda glc 4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 34.7, \"Name\": \"plymouth horizon 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2215, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 34.4, \"Name\": \"ford escort 4w\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2045, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 29.9, \"Name\": \"ford escort 2h\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2380, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"volkswagen jetta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 100.0, \"Horsepower\": null, \"Miles_per_Gallon\": 34.5, \"Name\": \"renault 18i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2320, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 33.7, \"Name\": \"honda prelude\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2210, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2350, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 32.9, \"Name\": \"datsun 200sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2615, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.3, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 31.6, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.4, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 28.1, \"Name\": \"peugeot 505s turbo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3230, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": null, \"Name\": \"saab 900s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2800, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.6, \"Cylinders\": 6, \"Displacement\": 145.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 30.7, \"Name\": \"volvo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3160, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 116.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"toyota cressida\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2900, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.8, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 24.2, \"Name\": \"datsun 810 maxima\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 22.4, \"Name\": \"buick century\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3415, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"oldsmobile cutlass ls\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3725, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.1, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford granada gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3060, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 17.6, \"Name\": \"chrysler lebaron salon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3465, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet cavalier\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2605, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet cavalier wagon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2640, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"chevrolet cavalier 2-door\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2395, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"pontiac j2000 se hatchback\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2575, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"dodge aries se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2525, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2735, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"ford fairmont futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2865, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.0, \"Name\": \"amc concord dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3035, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"volkswagen rabbit l\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1980, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"mazda glc custom l\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2025, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"mazda glc custom\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1970, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"plymouth horizon miser\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"mercury lynx l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"nissan stanza xe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2160, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2205, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2245, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"honda civic (auto)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"datsun 310 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1995, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 6, \"Displacement\": 181.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"buick century limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2945, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 262.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"oldsmobile cutlass ciera (diesel)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3015, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"chrysler lebaron medallion\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2585, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 112.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"ford granada l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2835, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.9, \"Cylinders\": 4, \"Displacement\": 144.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"toyota celica gt\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2665, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"dodge charger 2.2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2370, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet camaro\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2950, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"ford mustang gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2790, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 24.6, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 44.0, \"Name\": \"vw pickup\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 11.6, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"dodge rampage\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2295, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 79.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"ford ranger\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2625, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 82.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"chevy s-10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1982-01-01\"}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#e62e0ab1-485e-48fe-baea-810b8ec0e99e\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#e62e0ab1-485e-48fe-baea-810b8ec0e99e"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#e62e0ab1-485e-48fe-baea-810b8ec0e99e"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import altair as alt\n",
    "\n",
    "data = alt.load_dataset('cars')\n",
    "\n",
    "chart = alt.Chart(data).mark_circle().encode(\n",
    "    x='Horsepower',\n",
    "    y='Miles_per_Gallon',\n",
    "    color='Origin',\n",
    ")\n",
    "\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In most cases, the data passed to the ``Chart`` will be a standard Pandas dataframe, as we can see here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Miles_per_Gallon</th>\n",
       "      <th>Name</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Weight_in_lbs</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>USA</td>\n",
       "      <td>3504</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.5</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>USA</td>\n",
       "      <td>3693</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>USA</td>\n",
       "      <td>3436</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>USA</td>\n",
       "      <td>3433</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.5</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>ford torino</td>\n",
       "      <td>USA</td>\n",
       "      <td>3449</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Acceleration  Cylinders  Displacement  Horsepower  Miles_per_Gallon  \\\n",
       "0          12.0          8         307.0       130.0              18.0   \n",
       "1          11.5          8         350.0       165.0              15.0   \n",
       "2          11.0          8         318.0       150.0              18.0   \n",
       "3          12.0          8         304.0       150.0              16.0   \n",
       "4          10.5          8         302.0       140.0              17.0   \n",
       "\n",
       "                        Name Origin  Weight_in_lbs        Year  \n",
       "0  chevrolet chevelle malibu    USA           3504  1970-01-01  \n",
       "1          buick skylark 320    USA           3693  1970-01-01  \n",
       "2         plymouth satellite    USA           3436  1970-01-01  \n",
       "3              amc rebel sst    USA           3433  1970-01-01  \n",
       "4                ford torino    USA           3449  1970-01-01  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, Altair interprets the input and constructs a Python dictionary of the vega spec:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$schema': 'https://vega.github.io/schema/vega-lite/v1.2.1.json',\n",
       " 'encoding': {'color': {'field': 'Origin', 'type': 'nominal'},\n",
       "  'x': {'field': 'Horsepower', 'type': 'quantitative'},\n",
       "  'y': {'field': 'Miles_per_Gallon', 'type': 'quantitative'}},\n",
       " 'mark': 'circle'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart.to_dict(data=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When converted to JSON, this spec is exactly in the form expected by Vega-Lite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison to Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To clarify the declarative nature of Altair, we perform the same visualization with [Matplotlib](http://matplotlib.org/). In this case, the user has to manually perform a group-by operation and label the axes and legend. With Altairs declarative approach, the user only has to specify *what* should happen, not *how*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107266d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for (origin), group in data.groupby('Origin'):\n",
    "    plt.plot(group['Horsepower'], group['Miles_per_Gallon'],'o', label=origin)\n",
    "plt.legend(title='Origin')\n",
    "plt.xlabel('Horsepower')\n",
    "plt.ylabel('Miles_per_Gallon');\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Documentation\n",
    "\n",
    "You can learn more about Altair at its documentation site:\n",
    "\n",
    "- [Altair Documentation](http://altair-viz.github.io)\n",
    "\n",
    "Additionally, it can be useful when using Altair to understand the Vega-Lite library that underlies it. Vega-Lite's documentation can be found here:\n",
    "\n",
    "- [Vega-Lite Documentation](https://vega.github.io/vega-lite/docs/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: Introduction to Altair"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will guide you through the process of creating visualizations in Altair. For details on installing Altair or its underlying philosophy, please see the [Altair Documentation](http://altair-viz.github.io/)\n",
    "\n",
    "Outline:\n",
    "\n",
    "- [The data](#The-data)\n",
    "- [The `Chart` object](#The-Chart-object)\n",
    "- [Data encodings and marks](#Data-encodings-and-marks)\n",
    "- [Data transformation: Aggregation](#Data-transformation:-Aggregation)\n",
    "- [Customizing your visualization](#Customizing-your-visualization)\n",
    "- [Publishing a visualization online](#Publishing-a-visualization-online)\n",
    "\n",
    "This tutorial is written in the form of a Jupyter Notebook; we suggest downloading the notebook and following along, executing the code yourself as we go. For creating Altair visualizations in the notebook, all that is required is to [install the package and its dependencies](https://altair-viz.github.io/installation.html) and import the Altair namespace:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment/run this line to enable Altair in JupyterLab/nteract:\n",
    "# alt.enable_mime_rendering()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data in Altair is built around the [Pandas Dataframe](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html).\n",
    "For the purposes of this tutorial, we'll start by importing Pandas and creating a simple `DataFrame` to visualize, with a categorical variable in column `a` and a numerical variable in column `b`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>D</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>E</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  C  2\n",
       "1  C  7\n",
       "2  C  4\n",
       "3  D  1\n",
       "4  D  2\n",
       "5  D  6\n",
       "6  E  8\n",
       "7  E  4\n",
       "8  E  7"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame({'a': list('CCCDDDEEE'),\n",
    "                     'b': [2, 7, 4, 1, 2, 6, 8, 4, 7]})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Altair, every dataset should be provided as a `Dataframe`, or as a URL referencing an appropriate dataset (see [Defining Data](https://altair-viz.github.io/documentation/data.html))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `Chart` object\n",
    "\n",
    "The fundamental object in Altair is the ``Chart``. It takes the dataframe as a single argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart = alt.Chart(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fundamentally, a ``Chart`` is an object which knows how to emit a JSON dictionary representing the data and visualization encodings (see below), which can be sent to the notebook and rendered by the Vega-Lite JavaScript library.\n",
    "\n",
    "Here is what that JSON looks like for the current chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$schema': 'https://vega.github.io/schema/vega-lite/v1.2.1.json',\n",
       " 'data': {'values': [{'a': 'C', 'b': 2},\n",
       "   {'a': 'C', 'b': 7},\n",
       "   {'a': 'C', 'b': 4},\n",
       "   {'a': 'D', 'b': 1},\n",
       "   {'a': 'D', 'b': 2},\n",
       "   {'a': 'D', 'b': 6},\n",
       "   {'a': 'E', 'b': 8},\n",
       "   {'a': 'E', 'b': 4},\n",
       "   {'a': 'E', 'b': 7}]},\n",
       " 'mark': 'point'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point the specification contains only the data, and no visualization specification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chart Marks\n",
    "\n",
    "Next we can decide what sort of *mark* we would like to use to represent our data.\n",
    "For example, we can choose the ``point`` mark to represent each data as a point on the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"eefad5c8-0f16-47a1-8a8d-624d0f5e554b\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#eefad5c8-0f16-47a1-8a8d-624d0f5e554b"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"mark\": \"point\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#eefad5c8-0f16-47a1-8a8d-624d0f5e554b\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#eefad5c8-0f16-47a1-8a8d-624d0f5e554b"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABoAAAAaCAYAAACpSkzOAAAA9UlEQVRIS+2USwrCMBRF70sbnbsR96BOFBF07MhlCNq6I0FxVJ24AzfiSDCWvkiESP22ES0O2mFb3sm5uQmhoIcK4qAEfZz0/0TXmCxWwqPmRYVErCSG23Fn7qr21qgVLpUQqKSHMgis1W4TDOousJcgY+JLapthSQJNhFhrSM8DGZjyqe9i9hJkbQxkPesKu/pmsOQLjHkfhb1aXqtMEDNOUdit2oF2Affvs4DZIEBH0x8apfeIAQ1GDAEpANIaOErxnT0yUTxrnYEkOH2vdTbzm3MEPqiKP3Jpm53zPzdDVpvyfi+N8ib18F8ZXRndNYHCynAGBGhlG7epNNIAAAAASUVORK5CYII="
     },
     "metadata": {
      "jupyter-vega": "#eefad5c8-0f16-47a1-8a8d-624d0f5e554b"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = alt.Chart(data).mark_point()\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a visualization with one point per row in the data, though it is not a particularly interesting: all the points are stacked right on top of each other!\n",
    "To see how this affects the specification, we can once again examine the JSON:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$schema': 'https://vega.github.io/schema/vega-lite/v1.2.1.json',\n",
       " 'data': {'values': [{'a': 'C', 'b': 2},\n",
       "   {'a': 'C', 'b': 7},\n",
       "   {'a': 'C', 'b': 4},\n",
       "   {'a': 'D', 'b': 1},\n",
       "   {'a': 'D', 'b': 2},\n",
       "   {'a': 'D', 'b': 6},\n",
       "   {'a': 'E', 'b': 8},\n",
       "   {'a': 'E', 'b': 4},\n",
       "   {'a': 'E', 'b': 7}]},\n",
       " 'mark': 'point'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that now in addition to the data, the specification includes information about the mark type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data encodings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to add *visual encodings* (or *encodings* for short) to the chart. A visual encoding specifies how a given data column should be mapped onto the visual properties of the visualization.\n",
    "Some of the more frequenty used visual encodings are listed here:\n",
    "\n",
    "* X: x-axis value\n",
    "* Y: y-axis value\n",
    "* Color: color of the mark\n",
    "* Opacity: transparency/opacity of the mark\n",
    "* Shape: shape of the mark\n",
    "* Size: size of the mark\n",
    "* Row: row within a grid of facet plots\n",
    "* Column: column within a grid of facet plots\n",
    "\n",
    "For a complete list of these encodings, see the [Encodings](https://altair-viz.github.io/documentation/encoding.html) section of the documentation.\n",
    "\n",
    "Visual encodings can be created with the `encode()` method of the `Chart` object. For example, we can start by mapping the `y` axis of the chart to column `a`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"c33c1088-c0cc-4aa4-bf0f-fde0bda4c45b\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#c33c1088-c0cc-4aa4-bf0f-fde0bda4c45b"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"encoding\": {\"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"point\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#c33c1088-c0cc-4aa4-bf0f-fde0bda4c45b\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#c33c1088-c0cc-4aa4-bf0f-fde0bda4c45b"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "jupyter-vega": "#c33c1088-c0cc-4aa4-bf0f-fde0bda4c45b"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = alt.Chart(data).mark_point().encode(y='a')\n",
    "c # save the Chart to a variable and then display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a one-dimensional visualization representing the values taken on by `a`.\n",
    "As above, we can view the JSON data generated for this visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$schema': 'https://vega.github.io/schema/vega-lite/v1.2.1.json',\n",
       " 'data': {'values': [{'a': 'C', 'b': 2},\n",
       "   {'a': 'C', 'b': 7},\n",
       "   {'a': 'C', 'b': 4},\n",
       "   {'a': 'D', 'b': 1},\n",
       "   {'a': 'D', 'b': 2},\n",
       "   {'a': 'D', 'b': 6},\n",
       "   {'a': 'E', 'b': 8},\n",
       "   {'a': 'E', 'b': 4},\n",
       "   {'a': 'E', 'b': 7}]},\n",
       " 'encoding': {'y': {'field': 'a', 'type': 'nominal'}},\n",
       " 'mark': 'point'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is the same as above with the addition of the `'encoding'` key, which specifies the visualization channel (`y`), the name of the field (`a`), and the type of the variable (`nominal`).\n",
    "\n",
    "Altair is able to automatically determine the type of the variable using built-in heuristics. Altair and Vega-Lite support four primitive data types:\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>Data Type</th>\n",
    "    <th>Code</th>\n",
    "    <th>Description</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>quantitative</td>\n",
    "    <td>Q</td>\n",
    "    <td>Number (real-valued)</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>nominal</td>\n",
    "    <td>N</td>\n",
    "    <td>Unordered categorical</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>ordinal</td>\n",
    "    <td>O</td>\n",
    "    <td>Ordered categorial</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>temporal</td>\n",
    "    <td>T</td>\n",
    "    <td>Date/time</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "You can set the data type of a column explicitly using a one letter code as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"1f2e0ef7-fa3c-4cc2-aca2-d15e0de3f6b2\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#1f2e0ef7-fa3c-4cc2-aca2-d15e0de3f6b2"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"encoding\": {\"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"point\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#1f2e0ef7-fa3c-4cc2-aca2-d15e0de3f6b2\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#1f2e0ef7-fa3c-4cc2-aca2-d15e0de3f6b2"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "jupyter-vega": "#1f2e0ef7-fa3c-4cc2-aca2-d15e0de3f6b2"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(data).mark_point().encode(y='a:N')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualization can be made more interesting by adding another channel to the encoding: let's encode column `b` as the `x` position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"0e0d6c02-eb75-4565-8699-c8a3d47ec948\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#0e0d6c02-eb75-4565-8699-c8a3d47ec948"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"encoding\": {\"x\": {\"field\": \"b\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"point\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#0e0d6c02-eb75-4565-8699-c8a3d47ec948\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#0e0d6c02-eb75-4565-8699-c8a3d47ec948"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "jupyter-vega": "#0e0d6c02-eb75-4565-8699-c8a3d47ec948"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(data).mark_point().encode(\n",
    "    y='a',\n",
    "    x='b'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With two visual channels encoded, we can see the raw data points in the `DataFrame`. A different mark type can be chosen using a different `mark_*()` method, such as `mark_bar()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"6487f1f5-6cda-4ff6-97e6-f8fb56da98db\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#6487f1f5-6cda-4ff6-97e6-f8fb56da98db"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"encoding\": {\"x\": {\"field\": \"b\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"bar\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#6487f1f5-6cda-4ff6-97e6-f8fb56da98db\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#6487f1f5-6cda-4ff6-97e6-f8fb56da98db"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RBfoVsKD0m70zV6AUgcklnvXAvcDJwJnAgcDWUgbpOBRQYLECFpTFens0BRTYUWD716CcmC73WFBcLQp0LGBB6Th8p65AIQKTSzyfAa4DLkjjioLyeCFjdBgKKLBgAQvKgsE9nAIK7CAwKSiTGx4EjgBu1koBBfoVsKD0m70zV6BEgb2AeKvxthIH55gUUGBxAhaUxVl7JAUUUEABBRTIFLCgZEK5mQIKKKCAAgosTsCCsjhrj6SAAgoooIACmQIWlEwoN1NAAQUUUECBxQlYUBZn7ZEUUEABBRRQIFPAgpIJ5WYKKKCAAgoosDgBC8rirD2SAgoooIACCmQK/BfVseGkKgm1nQAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "jupyter-vega": "#6487f1f5-6cda-4ff6-97e6-f8fb56da98db"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(data).mark_bar().encode(\n",
    "    alt.Y('a'),\n",
    "    alt.X('b')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice, we have used a slightly different syntax for specifying the channels using classes (`X` and `Y`) passed as positional arguments. These classes allow additional arguments to be passed to each channel.\n",
    "\n",
    "Here are some of the more commonly used `mark_*()` methods supported in Altair and Vega-Lite; for more detail see [Markings](https://altair-viz.github.io/documentation/marks.html) in the Altair documentation:\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>Method</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_area()</code></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_bar()</code></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_circle()</code></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_line()</code></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_point()</code></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_rule()</code></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_square()</code></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_text()</code></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><code>mark_tick()</code></td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data transformation: Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Altair and Vega-Lite also support a variety of built-in data transformations, such as aggregation. The easiest way to specify such aggregations is through a string-function syntax in the argument to the column name. For example, here we will plot not all the values, but a single point representing the average of the x-values for a given y-value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"362f44b8-8f93-4d63-b76c-e307086fa481\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#362f44b8-8f93-4d63-b76c-e307086fa481"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"encoding\": {\"x\": {\"aggregate\": \"average\", \"field\": \"b\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"point\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#362f44b8-8f93-4d63-b76c-e307086fa481\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#362f44b8-8f93-4d63-b76c-e307086fa481"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#362f44b8-8f93-4d63-b76c-e307086fa481"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(data).mark_point().encode(\n",
    "    y='a',\n",
    "    x='average(b)'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conceptually, this is equivalent to the following groupby operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b\n",
       "a          \n",
       "C  4.333333\n",
       "D  3.000000\n",
       "E  6.333333"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('a').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More typically, aggregated values are displayed using bar charts.\n",
    "Making this change is as simple as replacing `mark_point()` with `mark_bar()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"a2f82f22-c8ac-489b-9f82-bca65f1b46ad\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#a2f82f22-c8ac-489b-9f82-bca65f1b46ad"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"encoding\": {\"x\": {\"aggregate\": \"average\", \"field\": \"b\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"bar\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#a2f82f22-c8ac-489b-9f82-bca65f1b46ad\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#a2f82f22-c8ac-489b-9f82-bca65f1b46ad"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "jupyter-vega": "#a2f82f22-c8ac-489b-9f82-bca65f1b46ad"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = alt.Chart(data).mark_bar().encode(\n",
    "    y='a',\n",
    "    x='average(b)'\n",
    ")\n",
    "c # save the Chart to a variable and then display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As above, Altair's role in this visualization is converting the resulting object into an appropriate JSON dict.\n",
    "Here it is, leaving out the data for clarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$schema': 'https://vega.github.io/schema/vega-lite/v1.2.1.json',\n",
       " 'encoding': {'x': {'aggregate': 'average',\n",
       "   'field': 'b',\n",
       "   'type': 'quantitative'},\n",
       "  'y': {'field': 'a', 'type': 'nominal'}},\n",
       " 'mark': 'bar'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.to_dict(data=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that Altair has taken the string `'average(b)'` and converted it to a mapping that includes `field`, `type`, and `aggregate`. The full shorthand syntax for the column names in Altair also includes the explicit type code separated by a column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aggregate': 'average', 'field': 'b', 'type': 'quantitative'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = alt.X('average(b):Q')\n",
    "x.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shorthand is equivalent to spelling-out these properties by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aggregate': 'average', 'field': 'b', 'type': 'quantitative'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = alt.X('b', aggregate='average', type='quantitative')\n",
    "x.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is one benefit of using the Altair API over writing the Vega-Lite spec from scratch: valid Vega-Lite specifications can be created very succinctly, with less boilerplate code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing your visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To speed the process of data exploration, Altair (via Vega-Lite) makes some choices about default properties of the visualization.\n",
    "Altair also provides an API to customize the look of the visualization. For example, we can use the `X` object we saw above to override the default x-axis title:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"381def63-efea-41a6-abc8-73da1cd40cdd\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#381def63-efea-41a6-abc8-73da1cd40cdd"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"encoding\": {\"x\": {\"aggregate\": \"average\", \"axis\": {\"title\": \"Average of b\"}, \"field\": \"b\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"bar\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#381def63-efea-41a6-abc8-73da1cd40cdd\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#381def63-efea-41a6-abc8-73da1cd40cdd"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiUAAACFCAYAAACE7ogsAAAUDklEQVR4Xu3dDbBtZV3H8S9KdcOXxl4sy6loFKUhKhvCyjQUrmLXCKysxDING4x8qZs1TSJeIy01SsWifMHKmjTHhrlgXcXQFFHBN8QMxhynFxFNAuNFQ2h+8uzYHs45e/332fuctfb67pk7F+5ZzzprfZ7/3uu3n2ft/RyEDwUUUEABBRRQoAcCB/XgGDwEBRRQQAEFFFAAQ4lFoIACCiiggAK9EDCU9KIbPAgFFFBAAQUUMJRYAwoooIACCijQCwFDSS+6wYNQQAEFFFBAgT6Gkts26ZY+Hm+fq+j+wFV9PsAeHptm9U7RTLO6QL2FdTYCsyFd5BNWhnS89fJZfAufxHVTzTSrC9RbWGea1QXqLQZXZ0O6yBtKRlCQ9VNceIvBPYkXLlDfoWaa1QXqLayzEZgZSuqdPKQWPonrvaWZZnWBegvrTLO6QL3F4OpsMKFk9779m91rUu8qWyiggAIrLHDg9D2DeX3v2A2Du8B2PK9lbjY4s8EUraFkmXXrvhVQYNUEDCWr1qNznY+hZC62Do0MJR2Q3EQBBRRoAoYSSwEwlCyrDAwly5J1vwoosIoChpJV7NXyORlKymQdGxhKOkK5mQIKKAAYSiwDR0q61cC92veNXA/c0q0JGEq6SrmdAgooYCixBr4k4EjJJoWQMPJa4PipbfYA53cpHkNJFyW3UUABBW4XcKTESjCUbFwDBwMXA5cDe4GMkjwMuBB4AHDlrPIxlMwS8ucKKKDAHQKGEqvBULJxDSSAvAo4ArhparOnAu9tfzatIEOJTzAFFFCgu4ChpLvVCm/p9M0Gnfv4NjLylHk731Ayr5ztFFBgjAKGkjH2+p3O2VBSDCV3bTe9Tt/wuu43t+7et98KU0ABBRToKGAo6Qi12psZSjbo32OBc4DDgS9MbfM3wAXAa2bVhSMls4T8uQIKKHCHgKHEavCeko1rYBdwGZDhjucCNwInAW8Avhn45KzyMZTMEvLnCiiggKHEGvgyAUdKNimI+7aP/x7ZtrkGeCTwgS5FZCjpouQ2CiigwO0CjpRYCY6UdKuBfF/JXYDPAp1X/jWUdMN1KwUUUMBQYg00AUdKllUKhpJlybpfBRRYRQFHSlaxV8vnZCgpk3VsYCjpCOVmCiiggNM31sDtAoaSZVWCoWRZsu5XAQVWUcCRklXs1fI5GUrKZB0bGEo6QrmZAgoo4EiJNeBIyXJrwFCyXF/3roACqyXgSMlq9eecZ+NIyZxwXZrlkzoHddnQbf5fYHAF2YO+06zeCZppVheot7DORmA2pIu8oWQEBVk/xYW38IWvTqqZZnWBegvrbARmhpJ6Jw+phU/iem9pplldoN7COtOsLlBvMbg6M5TUO3lILQZXkD3A1azeCZppVheot7DORmBmKKl38pBa+CSu95ZmmtUF6i2sM83qAvUWg6szQ0m9k4fUYnAF2QNczeqdoJlmdYF6C+tsBGaGknonD6mFT+J6b2mmWV2g3sI606wuUG8xuDozlNQ7eUgtBleQPcDVrN4JmmlWF6i3sM5GYDaYUOKXp9Wr0RYKbIfAgdP3HAZctR2/a4V+hxfYemdqNgIzQ0m9k22hgAJTAoaSucrBC2ydTbMRmBlK6p1sCwUUMJRstQa8wNYFNRuBmaGk3sm2UEABQ8lWa8ALbF1QsxGYGUrqnWwLBRQwlGy1BrzA1gU1G4GZoaTeybZQQAFDyVZrwAtsXVCzEZhtVyjZBbwbOHLK9CXAs4Hruzj76ZsuSm6jwPYLeKPrXOZeYOtsmo3ArGsoORTYC9wC3NpcHg08GLi2g1NCyRXAicCngLsDL21/P7ztd9PdGEo6KLuJAjsgYCiZC90LbJ1NsxGYdQkl2eaNwAlrPK4B7gd8roNTQskHgaOmRkbu2b7bYHf7maGkA6SbKNA3AUPJXD3iBbbOptkIzLqEkrsBHwJOBo4DPglcCLwZOAK4qYPTJJSsHVl5BXAJkL8NJbMQ/LkCPRQwlMzVKV5g62yajcCsEkqeD/wv8CjgWcBFwOOASzs4bRZKzgPyZ/K4bb397d63v8OvcRMFFNhuAUPJXOJeYOtsmo3ArEsoCcMZwKlttORAc8m0Tb5e+uoOTutN30zuM+kUbLynpIOymyiwAwKGkrnQvcDW2TQbgVnXUBKKo9uoyEOBBwHvAi7uaDQJJXuAzwCHAM8Bvgk4yRtdOyq6mQI9FDCUzNUpXmDrbJqNwKwSSuocd7RY7yPB5wKnATd02bEjJV2U3EaB7RcwlMxl7gW2zqbZCMy2K5TUKde0MJRsmdAdKLAUAUPJXKxeYOtsmo3AzFBS72RbKKDAlIChZK5y8AJbZ9NsBGaGknon20IBBQwlW60BL7B1Qc1GYGYoqXeyLRRQwFCy1RrwAlsX1GwEZoaSeifbQgEFDCVbrQEvsHVBzUZgZiipd7ItFFDAULLVGvACWxfUbARmgwklQL7pdUjHWy+fxbfwSVw31UyzukC9hXWmWV2g3mJwdTaki7yhZAQFWT/FhbcY3JN44QL1HWqmWV2g3sI6G4GZoaTeyUNq4ZO43luaaVYXqLewzjSrC9RbDK7ODCX1Th5Si8EVZA9wNat3gmaa1QXqLayzEZgZSuqdPKQWPonrvaWZZnWBegvrTLO6QL3F4OrMUFLv5CG1GFxB9gBXs3onaKZZXaDewjobgZmhpN7JQ2rhk7jeW5ppVheot7DONKsL1FsMrs4MJfVOHlKLwRVkD3A1q3eCZprVBeotrLMRmA0mlLhKcL0abaGAAgoooMAyBQ6cvmehOWKhO1vmiRtKlqnrvhVQQAEFFKgLGErqZrZQQAEFFFBAgSUIGEqWgOouFVBAAQUUUKAuYCipm9lCAQUUUEABBZYgYChZAqq7VEABBRRQQIG6wJBDST7OdeU6p3wacPYsCm90nSXkzxVQQAEFFNhegaGHkr8HjgPyqZ/JJ3+uBf5rFqOhZJaQP1dAAQUUUGB7BYYcSh4I/DFwzDxkhpJ51GyjgAIKKKDA8gSGHEoyfXMZcCbwxUa0CzgLuGEWmaFklpA/V0ABBRRQYHsFhh5K3gGcAtza2A4G3gR8forxtvVId+/bv73S/jYFFFBAAQUU2FRgyKHE6RuLWwEFFFBAgRUSGHIoyfRNhjt+eOom13TNzcB1s/rI6ZtZQv5cAQUUUECB7RUYeihZ7yPBe4EXz2I0lMwS8ucKKKCAAgpsr8CQQ8mWpAwlW+KzsQIKKKCAAgsXMJQsnNQdKqCAAgoooMA8AoaSedRso4ACCiiggAILFzCULJzUHSqggAIKKKDAPAKGknnUbKOAAgoooIACCxcwlCyc1B0qoIACCiigwDwCow0lQL7pdbKI3zx2Y2yT74a5aownvoVz1qyOp5lmdYF6C+tsBGZDusgbSkZQkPVTXHgLX/jqpJppVheot7DORmBmKKl38pBa+CSu95ZmmtUF6i2sM83qAvUWg6szQ0m9k4fUYnAF2QNczeqdoJlmdYF6C+tsBGZ9DCXrrhJc7wtbKKCAAgoooEBPBdbNH30MJRv5eU9JvbI006wuUG9hnWlWF6i3sM5Wx2zDvjSU1Dt5SC18Etd7SzPN6gL1FtaZZnWBeou+1pmhpN6XK9GirwXZZ1zN6r2jmWZ1gXoL62x1zAwl9b5ciRY+ievdqJlmdYF6C+tMs7pAvUVf68xQUu/LlWjR14LsM65m9d7RTLO6QL2FdbY6ZoaSel/aQgEFFFBAAQWWILASoWQJLu5SAQUUUEABBfoiMKRP3/TFzONQQAEFFFBAgSUIGEqWgOouFVBAAQUUUKAuYCipm9lCAQUUUEABBZYgMIRQcjBwMnAc8AHgLOCWJVis4i4fAewCzl/Fk1vwOaXOTgWeCLwD+APgEwv+Hau2u0OA3waOBN4O/Alw/aqd5JLO56uAPwOeB1y1pN+xKrv9FuAJU6/7nwb+HHBJks17+KT2enY18HvAx4ZQEEMIJU8Hngb8NPAc4OPt/y3IjSvsXu1C8XIgf84eQjHu8DGe0V74HgucAPwicBhw0w4fV19/fV47Xg98DngZsBe4tb2B8Lk5u9dSb3k9Owq4dPbmo97iZ4Bnttexr2zB93WGkk1r4tHAq4HHAEcDzwCOGMLrWd9DSd7lX9FGSf4VyMU2716/Dbh21E/TzU8+o0p5Eh8PnGYomVkpqbPU1fe3v+8CXA78VKu/mTsY4QZ3Az7aXuiua8/JC1oY/uIIPSqnfCzwu0BGS55sKJlJlzel7wEumbmlG0TgK1pN/Wx7/coocELKW4Ab+07U91CSEJIXvixZnWHhSUj5EeDf+o7bg+M7B/gw8NIeHEvfD+EbgAwL5/EQIBdYw+/mvXZX4J5ALrKntIvG6X3v6B0+vvsA/wLcr03dvAJ47w4fU99//V+3kfIcZ0bmMi2RC6yP9QVynbys1dmJzSxvUN85BLC+h5KEkczvP7CNjExCyY/5DrZTeeUF7/2OlHSyykYJJme2C2yC79s6txzvhnnjkOH1nwQSUnYDN4+XY9MzzzvWi4AEt7cCudjm3iVDyeYFk9GkvDm9GDimuU2uCZbanQUmoeRV7Q3pg4B3Ad86hDfzfQ8lwf1gm3edjJTkprDcWOf0zeyno6FkttFki8y7Znj4l4G4faF701FumQCXC0Tm9vPIkHEuHJnD/sgoRWaf9OHNJu/27zG1+fcB75vdfJRbJOh+NfA/7ewnU6sJwdbZ+iWRqdXc7vBdwDVAzPLmNPfJ9T4A9z2U5J1F0vGftgvFj7e7iL/bd2OdXqAMJZ2YvnRBzSe7cuPh/vYimOdGgrCf9Frf8OvbdFeeix9qbxQyqumU18Y1lwvs3duPc9/NucArgTd1K9NRbpXpwdyzNKmzvHk4r01/Jdz5uLNAXrsyEpfrZkbjMuOQ6ZxBPDf7HkrCfWh7B3vvNjf2PS0FWoyzBV7SbtjMRw99bCyQd/0ZJfmONZuk1jJS52N9gXxU/y+mfpS5/jeK1VkgF4x8VDOB2MfGAhkVmYzIZauHA/8o2KYC3w5cOPWaNpjn5hBCSeTzMbDMXScxO1/ts1GB/ghkGiLD6xlV8rnZn35ZtSPJJ5UyLXED8PlVO7klnc/kRvRMRcdtEI+hhJJBYHqQCiiggAIKKDC/gKFkfjtbKqCAAgoooMACBQwlC8R0VwoooIACCigwv4ChZH47WyqggAIKKKDAAgUMJQvEdFcKKKCAAgooML+AoWR+O1sqoIACCiigwAIFDCULxHRXCijQW4F8pNSPkva2ezwwBW4XMJRYCQoosFYg3wl0aVsLKF+BnqUdhvrIuby2rZj9A2tWms2XC+aL8x4P3DrUE/S4FVglAUPJKvWm56LAYgSyzPn5bVe/Cpy1mN3uyF7yzZYfB54LJIR8duoozga+FsgS77ftyNH5SxVQ4MsEDCUWhAIKTAvkNeH1wGPbP2ZhryOAb2wX9aw8+ndt7ZGscPuytpTB84Gfb+u4/HpbxftY4BfaYmDfC/wc8KS2JkfWLXkR8Ift22Af1/b/T8A/t3U6sjhivo0y+3teW2Pnt6YC0/Rx56vHs5xClgr4B+AZ7Rug/wrIis9ZX+bpa0Z9ElLyezOS8swWXH4fuNGSUECBnREwlOyMu79Vgb4K3Lctb74X+I+2oNdRLSi8B/gYkIUxnwLkAp5l0d/Q7tc4s02FPKwFmfydgJPHGW0doayNcxrw38Bftv/+MHBRCxNvbmElq5tmefrfAJ4KnNoWFsuiiT/YlmKfGOb4cmw5jqzFk7CUhe8eDPxEO859wDnAf07BJ5T8CvDCtpLqrwG/Azy7r53jcSmw6gKGklXvYc9PgZrAE4FXAw8FPtOWh89F+1ltpCEjFbnP5DVtWfSMYFzRlkRPSPlRIPvI/RsJOBlZyQqvmULJ/R0JNFmVOf+WsJGAchOQkZaMyHy6BZCEmASeA0BWJM40UlY5TfBJYHrx1Gn9EpDQMWmf0PJOIGEl55Dfvd7iigkl+fdj2j0lCUxZ5v1EIKv4+lBAgW0WMJRsM7i/ToEeCyQsZPXVH1pzjP8OHAl8TbvAv7wFike0kYdMtyR8ZJQji2fmT/47oSD3bWTE49o2apGRk6yMOxnZSCjJzaYZCZlsl1CTbSahJIuJZZrn4LbvjwAXTx1jpl5+s42kZGHABI33t5GS3ENyZTuW3Lw7/UgoyT0nJ7R/zDl8naGkxxXqoa28gKFk5bvYE1Sgs0BGGi5v0x25zyKP44EXtL9zr0ZGE3IRz70mGe1IkPkocEGbonlMm/7ISElCxXQoSQhI4Mi0yqHAfuCUFnTe0oLN24Fz2yjM4W3657D2+xMgXgk8ubWdnNhkZCSjNn/bjvchwHe2FYw3CyWZvsl9JVlRNfefTEaFOqO5oQIKLE7AULI4S/ekwNAFMtqQaZQHtNGFnM992mhIwkDuI3lUu9E091/k3o08jgbOA+7d/v8J7X6R3M8xHUom4SGbvbVNt7wOeFoLBn8EfKLdjJqbZDNysqtNFR3X9p3QkHs+1n7nSG6KzU23k8cj29TP/du5ZMrpfWs6KNNNuYl28rikTS99augd6fErMFQBQ8lQe87jVqBfAhlpyPROwkKmWzZ6HNJGV66b2iBhJSMrbwNy02vuGzl5ajomm+Z+lHyXyHS7tb/jHm1kJFM4Nxd4cuy5MXazfRd256YKKDCvgKFkXjnbKaDAogQm3yUyvb98fDifpPGhgAIjEjCUjKizPVUFeiyQEZTcZ5K/r24fS+7x4XpoCiiwDAFDyTJU3acCCiiggAIKlAUMJWUyGyiggAIKKKDAMgQMJctQdZ8KKKCAAgooUBYwlJTJbKCAAgoooIACyxAwlCxD1X0qoIACCiigQFng/wCx1yTC+V1kNQAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "jupyter-vega": "#381def63-efea-41a6-abc8-73da1cd40cdd"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(data).mark_bar().encode(\n",
    "    y='a',\n",
    "    x=alt.X('average(b)', axis=alt.Axis(title='Average of b'))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The properties of marks can be configured by passing keyword arguments to the `mark_*()` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"99c110f4-c06a-4ff8-a66b-1a7d38df4618\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#99c110f4-c06a-4ff8-a66b-1a7d38df4618"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}, \"mark\": {\"color\": \"#03A9F4\"}}, \"encoding\": {\"x\": {\"aggregate\": \"average\", \"axis\": {\"title\": \"Average of b\"}, \"field\": \"b\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"bar\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#99c110f4-c06a-4ff8-a66b-1a7d38df4618\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#99c110f4-c06a-4ff8-a66b-1a7d38df4618"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "jupyter-vega": "#99c110f4-c06a-4ff8-a66b-1a7d38df4618"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(data).mark_bar(color='#03A9F4').encode(\n",
    "    y='a',\n",
    "    x=alt.X('average(b)', axis=alt.Axis(title='Average of b'))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can use various `configure_*()` methods to adjust global properties of the chart, such as the chart width or height:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"93bae98e-84c9-4d46-8b39-cbed3f354080\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#93bae98e-84c9-4d46-8b39-cbed3f354080"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"cell\": {\"width\": 300.0, \"height\": 350}, \"scale\": {\"bandSize\": 50.0}}, \"encoding\": {\"x\": {\"aggregate\": \"average\", \"axis\": {\"title\": \"Average of b\"}, \"field\": \"b\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"a\", \"type\": \"nominal\"}}, \"mark\": \"bar\", \"data\": {\"values\": [{\"a\": \"C\", \"b\": 2}, {\"a\": \"C\", \"b\": 7}, {\"a\": \"C\", \"b\": 4}, {\"a\": \"D\", \"b\": 1}, {\"a\": \"D\", \"b\": 2}, {\"a\": \"D\", \"b\": 6}, {\"a\": \"E\", \"b\": 8}, {\"a\": \"E\", \"b\": 4}, {\"a\": \"E\", \"b\": 7}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#93bae98e-84c9-4d46-8b39-cbed3f354080\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#93bae98e-84c9-4d46-8b39-cbed3f354080"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#93bae98e-84c9-4d46-8b39-cbed3f354080"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = alt.Chart(data).mark_bar().encode(\n",
    "    y='a',\n",
    "    x=alt.X('average(b)', axis=alt.Axis(title='Average of b'))\n",
    ").configure_scale(\n",
    "    bandSize=50  # scale.bandSize adjusts thickness (in pixels) of bars\n",
    ").configure_cell(\n",
    "    width=300  # cell.width adjusts the width (in pixels) of the chart\n",
    ")\n",
    "\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As above, we can inspect how these configuration options affect the resulting Vega-lite specification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$schema': 'https://vega.github.io/schema/vega-lite/v1.2.1.json',\n",
       " 'config': {'cell': {'width': 300.0}, 'scale': {'bandSize': 50.0}},\n",
       " 'encoding': {'x': {'aggregate': 'average',\n",
       "   'axis': {'title': 'Average of b'},\n",
       "   'field': 'b',\n",
       "   'type': 'quantitative'},\n",
       "  'y': {'field': 'a', 'type': 'nominal'}},\n",
       " 'mark': 'bar'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.to_dict(data=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To learn more about the various configuration options, one useful place to start is Altair's ``Config`` object, along with IPython's documentation capabilities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "alt.Axis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also read more in Altair's [Configuration](https://altair-viz.github.io/documentation/config.html) documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Publishing a visualization online"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because Altair produces Vega-Lite specifications, it is relatively straightforward to export charts and publish them on the web as Vega-Lite plots.\n",
    "All that is required is to load the Vega-Lite javascript library, and pass it the JSON plot specification output by Altair.\n",
    "For convenience Altair contains a built-in bare-bones HTML template that can be used to display any plot.\n",
    "Here's the template with an empty chart for clarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "<!DOCTYPE html>\n",
      "<head>\n",
      "  <title>Vega-Lite Chart</title>\n",
      "  <meta charset=\"utf-8\">\n",
      "\n",
      "  <script src=\"https://d3js.org/d3.v3.min.js\"></script>\n",
      "  <script src=\"https://cdnjs.cloudflare.com/ajax/libs/vega/2.6.5/vega.min.js\"></script>\n",
      "  <script src=\"https://cdnjs.cloudflare.com/ajax/libs/vega-lite/1.2.1/vega-lite.min.js\"></script>\n",
      "  <script src=\"https://cdnjs.cloudflare.com/ajax/libs/vega-embed/2.2.0/vega-embed.min.js\" charset=\"utf-8\"></script>\n",
      "\n",
      "  <style media=\"screen\">\n",
      "    /* Add space between vega-embed links  */\n",
      "    .vega-actions a {\n",
      "      margin-right: 5px;\n",
      "    }\n",
      "  </style>\n",
      "</head>\n",
      "<body>\n",
      "  <!-- Container for the visualization -->\n",
      "  <div id=\"vis\"></div>\n",
      "\n",
      "  <script>\n",
      "  var vlSpec = {\n",
      "    \"mark\": \"point\",\n",
      "    \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"\n",
      "}\n",
      "\n",
      "  var embedSpec = {\n",
      "    mode: \"vega-lite\",  // Instruct Vega-Embed to use the Vega-Lite compiler\n",
      "    spec: vlSpec\n",
      "  };\n",
      "\n",
      "  // Embed the visualization in the container with id `vis`\n",
      "  vg.embed(\"#vis\", embedSpec, function(error, result) {\n",
      "    // Callback receiving the View instance and parsed Vega spec\n",
      "    // result.view is the View, which resides under the '#vis' element\n",
      "  });\n",
      "  </script>\n",
      "</body>\n",
      "</html>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(alt.Chart().to_html())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All that must be changed is the ``vlSpec`` variable, which should contain the Vega-Lite spec output by ``chart.to_dict()``.\n",
    "This is done automatically using the ``Chart.savechart`` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart = alt.Chart(data).mark_bar().encode(\n",
    "    y='a',\n",
    "    x='average(b)'\n",
    ")\n",
    "\n",
    "chart.savechart('example.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the output in an iframe within the notebook (note that some online notebook viewers will not show iframes for security reasons):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"200\"\n",
       "            src=\"example.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x10cf78b38>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display IFrame in IPython\n",
    "from IPython.display import IFrame\n",
    "IFrame('example.html', width=400, height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can use your web browser to open the file manually to confirm that it works: [example.html](example.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning More\n",
    "\n",
    "For more information on Altair, please refer to Altair's online documentation: http://altair-viz.github.io/\n",
    "\n",
    "You can also see some of the example plots listed in the [accompanying notebooks](01-Index.ipynb)."
   ]
  }
 ],
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