01-Visualization in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installation\n",
    "\n",
    "Run the following commands:\n",
    "\n",
    "```bash\n",
    "conda create -n intro2ds -y python=3.6 jupyter  # create an environment\n",
    "source activate intro2ds                        # activate environment\n",
    "jupyter notebook                                # launch jupyter notebook\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "Installation:\n",
    "```bash\n",
    "conda install matplotlib\n",
    "```\n",
    "\n",
    "https://matplotlib.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x108e1f748>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XyMlLTXxp2Uzc3Vz0wgbSpwVyy+xwnjl4ns4eI0PJJTCb4YOfQ3gypAx3GemMELDsO9B+GU6/8unbV9p7eOVoOZsyp7n0BDVfL3c+f1M8+0vqyKto1luOy+GIX61pQPmAvyss712HEGI6EA+8P+BtbyFEjhDisBDiDgfocS6L/0WLNRz6zadv/eGDs4T5eXJPVqyOwkbHV29LpKG9h1ePXdJbigFA8TtQV6T96LrqasFK/DKIztS++2YzoM0d7+zt5/FlrpGFNxyfWzKDAG93fv2+sWq4Fkd88wabEjJUWe19wBtSyoG3p3GWGaQPAL8SQgzaSEgI8bjFgOTU1bnQ4JkpQTDvs1ped3Ml+ZXNfHimjsduisfbQ9+CntGwMD6EG2YE8+cPz9HTZ9ZbzuRGSvjoVxCSoFXZuzpCwNJvQEMZlOykq7ef5w5d4LakcJKinNzPaQwEeHvwyNJ49hXWcqbWqGsYiCMMQwUw8NY4Bhiqv/N9XONGklJWWZ7PAQeAeYPtKKX8s5QyS0qZFR4ebq9mx7L4yyDNcPRP/OHAWfy93Hlo8XS9VY2ar9yWSFVzl1ENrTflR7W6hRu/AibXv6kANHdXUBwc+jVvHK+gob2HLzm5SaQ9PHzjdLzcTTz78QW9pbgUjjAMx4BZQoh4IYQn2o//ddlFQogkIBj4ZMB7wUIIL8vrMGApoF7lSfAMSNmI+dizfJB/jgcXTyfA20NvVaPm1tnhJEf58+zHF4xhPnpy+PfgHag1y1MFN3dtoE/5ET45sIO5sUEsitevH5ithPp5sXneNLacqOCK0ZL+U+w2DFLKPuBrwB6gCHhNSlkghPihEGJgltH9wCvy6l+eFCBHCJEL7AeellKqZxgAlnwdU08L97p9yKNLZ+itxiaEEDyyZAZF1S0cPd+ot5zJSdMlbYzmgkdcp8p5tMx7kB7PQDa1v8GXl81EOHoG9Tjz6NJ4uvvMvGzE2T7FIdEtKeVOKeVsKWWClPIpy3v/JaXcNmCb/yulfOKa/Q5JKTOklHMtz884Qo8edERkcoIkvjJlL5F+6qwWrGzKnEbgFA+eO3RBbymTk6N/BgQsfFxvJbbj6cs2z3WscDvBymj1ZosnRfmzNDGUvx+6SG+/EWcDo/LZYWw9WcWfetYQ1lutpa8qxhRPN+5bGMvewloqm9S7uJWmuw2O/x1SN0FgjN5qbKa4poWf1S8FYcLtxPUFbyrw2NJ4alq62JU/tkFEEw3DMDgAKSXPH7pAZcRtyMAYOKbmwuezi6cjpTQK3pzNqZegu1mriVGQfxy+SLN7KH2z1sLJF6BXvRuL25IimBHqw9+MQT6AYRgcwpHzjZTUtvLZpTMRCx6B8x9AvXp9WGKCfchOjeTlo5eMltzOQkptlvK0BRB7g95qbKa1q5e3TlSyYe5UPG98XBtgVbBVb1k2YzIJHl0az6nyJk6VN428wwTHMAwO4PlDFwjy8WBT5jSY9zlt2tZxNZfUjyyJp6mjl22nhso4NnAolz7R2l9kfV5vJWPirZOVtPf089nF07UWGWGztVklCnLn/GlM8XDj5SNGENowDHZS3dzJ3sJaPpMVqxW0+UdC8no49eJ1PWRUYPHMEGZH+vHiEcOd5BRyngWvQDUK2q5BSskLn1xkbkwgc2ODtIK3G74AlTlQdVJveTbj7+3BxrlT2ZZbReskbyxpGAY7eT2ngn6z5MFFAwrash7TltSFb+snbIwIIbjvhjhyK5oprGrRW87EpqNR+47MuVebjqYYh881Unq57epizrn3gYevsquG+xfF0dnbz9uTfMVsGAY7MJslr+WUszQxlLjQARd2/DIITYScv+knzg42z5uGp5uJ13LKR97YYOzkvgz93ZD1qN5KxsRLRy8ROMWDDQM7CHsHaoYu7w3oVM9XPzcmkJToAF46cmlSF3sahsEODp1toOJKJ/de2yxPCFjwKJQfhtoCfcTZQbCvJ6vTo9hyosIIQo8XUsLx5yDmBohM01uNzTR19LCnoIbN86Zd3xNswSPQ1zWqudCuhhCCBxbGUljdQl7l5O26ahgGO3g1p5zAKR6sSou6/sPMB8DNC44/73xhDuC+G2Jp6epjt5HXPT5Yg84L1FwtbMutoqfPfP1NEUD0XG3I0Ml/OF+YA9g0bxreHiZePjp5g9CGYRgjV9p72JM/xB0TaBPektdB3uvQp14PlsUzQ5ke6sMrRpuA8eH4c8oGnQFeyyknfVoAqVMDrv9QCJj3kNYQcJiZ6K5KgLcHG+ZM5e1TVbR19+ktRxcMwzBGtp6qpKffzGduGGbmQuaD0NkIZ3Y7T5iDMJkE92bFcvhcI+fr2/WWM7HoaoHCbZBxt5JB54KqZvIrWwZfLVjJuBdMHlp2noLctzCOjp5+dpyenEFowzCMASklrx4r/zRQNSQJt4F/tFbZqiD3LIjBzSSMVYOjKdwKfZ2au1FBXs+pwNPdNPzYWt9QSFoDua8ouWKeHxfEzDBf3jwxOVvRG4ZhDORVNlNc08q9w60WQOupP+czULoXWmudI86BRAR4c1tSOFtPVtJvnrwZGg7n1MsQOkurdlaM7r5+tp6qZFVaFEE+nsNvPO+z0FEPpXucI86BCCG4a0EMR883Ut7Yobccp2MYhjGw5UQlnu4m1s8Z5o7JSuaDIPsh77XxFzYObJ4XQ21LN5+cbdBbysSg8TxcOgSZ92u+eMXYV1hLU0cv92aNotlfwnJtxaxoEPqOedMQAt48UaG3FKdjGAYb6e03805uFdkpkQROGUV77fDZWkriyRe1FEXFuD0lAn9vd7ZMwotjXMh9BRAw5z69lYyJN45XMDXQmyUJYSNv7OauFbyV7lNyxTwtaAo3zgxly4nKSVfTYBgGG/nwTB0N7T1snjdt9DtlPqANeFewTYC3hxvr50Szu6CG9kmaoeEwzGatqG3mLRBow/fHRahv6+ZgaT13zJuGm2mUq525D2grZgVrGgDumh/DpcYOci5e0VuKU3GIYRBCrBZClAghyoQQTwzy+SNCiDohxCnL4wsDPntYCFFqeTzsCD3jyZaTlYT4enJLkg1zp9Pu1Goacl8eeVsXZPO8GDp6+tlTYNQ02MWlT6DpovZjqSDbc6voN0vusOWmKHw2RGfC6VfHT9g4sjo9Ch9PN948PrlWzHYbBiGEG/A7YA2QCtwvhEgdZNNXpZSZlsdfLfuGAE8Ci4CFwJNCiGB7NY0XLV297CusZcOcaDzcbPinmxIESashfwv0q3fXnTU9mJjgKbx1cnJmaDiM3JfB0w9S1uutZEy8daqK1OgAZkf627bjnHuh+hTUnRkfYeOIr5c7a9Kj2XG6elJ1AXDEimEhUCalPCel7AFeATaNct9VwD4pZaOU8gqwD1jtAE3jwq68anr6zGyeP4YpWxn3ahka5w44XNd4YzIJ7pw3jY/L6qlpVq9jrEvQ26U1zEvZqN5MZ+B8fTu55U22uVCtpN8FwqRsAsZd86fR2t3HvkL14iRjxRGGYRowsNtaheW9a7lLCHFaCPGGEMKa5znafV2CLScqmRnmy9yYQNt3npWtVbrmve54YU5g8/wYzBLePmWsGsZE6V7oboE59+itZExsPVmJEFzdMG+0+EdB/C3ad1/BIO6imaFEBnixLXfyFLs5wjAMFoW69v/+O8AMKeUc4F3A2kBoNPtqGwrxuBAiRwiRU1dXN2axY6XiSgdHzjdaUtjGkGbo7gWpG7V50D3q5UXHh/mSGRtkuJPGSv4b4BsOM5bprcRmpJRsPVXJkoRQogK9x3aQOZ+BKxeg4phDtTkDN5Ngw5ypHCi5THPH5JjT4AjDUAEMrPSKAa4yrVLKBillt+XPvwALRrvvgGP8WUqZJaXMCg+3IfDrIN7JrQbgjkw7FjRz7oWeNjizy0GqnMumzKkU17RSdrlVbylq0dUCJbstSQjuequxmVPlTVxs6LDvu5+yHtynKBuE3pg5ld5+ye6Car2lOAVHGIZjwCwhRLwQwhO4D9g2cAMhRPSAPzcCRZbXe4CVQohgS9B5peU9l+Od3CoyY4OunrtgK9OXagU/eW84TpgTWZcRjRD/NJIGo6R4hzZ3IeNuvZWMia0nK/FyN7E6fZAuwqPFyx+S11oSMNS7686YFsiMUJ9J406y2zBIKfuAr6H9oBcBr0kpC4QQPxRCbLRs9g0hRIEQIhf4BvCIZd9G4EdoxuUY8EPLey7F2bo2CqtbxuZfHYjJTQvEle7TpncpRkSAN4vjQ3knt2rSFfzYRd7rEBSnFToqRl+/mR151axIicTfexQFncORca/WVPLs+44R50SEEGycO5VDZxu43DLxEzAcUscgpdwppZwtpUyQUj5lee+/pJTbLK+/L6VMk1LOlVLeJqUsHrDv36SUiZbHs47Q42i251YjhHbHbDcZ94C5V8mxn6AFH8/Vt1NgjP0cHW11WiZa+t1KtsA4cr6R+rYe1s9xwHc/Ybk24a3gLfuPpQMbM6ciJWw/PfFXzEbl8whIKdmWW8nCGSFjD7wNJHqu1kBN0UrQNelRuJsE70zSdsQ2U7hVq/xV1I20/XQVvp5u3JYcYf/B3D0heYPmWutV7647McKflOiASeFOMgzDCBTXtHK2rp319rqRrAihDWe5+DG0XXbMMZ1IsK8nN88KY3tuteFOGg15b0B4ipLjO3v7zezKr2FFauTgw6jGQvpmLW337HuOOZ6T2ZQ5lVPlTVxqUC+z0BYMwzAC209X4WYSrLEn8HYtaZtBmqFo28jbuiAb5k6lsqmTE5fUG/buVJortLnf6XfprWRMfFxWT1NH7+i6CI+W+FtgSoiy7iRrnHGir5gNwzAMUkreya1mSUIoYX5ejjtwRAqEJUHBVscd04lkp0bi5W7inUmwpLaLQovhV3R85/bT1fh7ubNs9ig6qY4WNw+tnqdkF/R2Ou64TmJa0BQyY4PYMcHjDIZhGIbTFc1cauywPxvpWqzupAsfKdmO2N/bg9uSItiRV43ZGOAzNIVbITIdwhL1VmIzPX1m9hTUkJ0WiZe7g9xIVtI2a/U8pXsde1wnsS4jmsLqFi5M4JG3hmEYhh151Xi4CValOtCNZCXtDkAq605aOyeautbuSdeOeNQ0V0L5EUi9Q28lY+JgaR2tXX1scKQbycr0m7Qq8Pwtjj+2E1iTof0e7MyfuKsGwzAMgZSSnXnVLE0MI9DHzvztwYhI0YKSivpalydH4OVuYmfexL047MJq8NPUNAzbT1cTOMWDpYkOdCNZcXOH1E1wZg/0qHfXHRPsQ2Zs0IT+7huGYQjyK1uouNLJ2nQH5G8PRdpmuHgIWtT7gvl5uXPL7HB25RvupEEpeMviRpqltxKb6e7r593CWlalReLpPk4/EWmboa8Tzuwen+OPM+syosmvbJmw2UmGYRiCnfnVuJkE2amR43cS1d1JGdHUtnRzstxwJ12F4m6kj8vqae3uY40jCjqHIu5G8I34Z4BeMazupB0TdNVgGIZBkFKyK0/LRgr29Ry/E4UnQUSasu6k21Mi8HQzseO0MdntKhR3I+3Mq8Hf252lo5nrPFZMblpjvdJ9SmYnxQT7MHcCu5MMwzAIRdWtXGjoYO143jFZSd0Ilw4rm520bHaY4U66loKtmsFX0I3U229mX2Et2Snj6EaykrIRetuhTM1it3UZUeRVNk9Id5JhGAZhV341JgErx9ONZCVlIyC1OQ0KsjYjmurmLnIrjGI3QIsXlR9WdrXwydkGmjt77eukOlpm3ARTgpXtG7bGEn+ciNlJhmG4BiklO/KqWTwzlFBHFrUNRUQKhCQoG2e4PSUSDzcxYZfUNmM18Ckbh9/ORdmVX4OvpxvLZjth5ombBySv0wLQfd0jb+9ixIb4MCcmkN35E8+VahiGazhT28a5uvbxDbwNRAjNnXT+oJKtuAOneHDzrHB25tUYvZMAit7RmiSGJ+mtxGb6zZK9BTXclhzhuN5II5GySeuddO4D55zPwaxKi+JUeRPVzerFSYbDMAzXsCtfa7G9Ks0JbiQrKRu1DpwlO513TgeyOi2KyqZOoxV3R6NWzZ66UckW20fPN9LQ3uOc2JqVmbeAV4Cy7iSry21vgXoxwuEwDMM17M6vIWt6MBEdSxQSAAAgAElEQVT+DmixPVqmzoPAOGVT91akRmISsKdg4i2pbaJkp2bgUzborWRM7MqvxtvDxK1JThyd6+4FSWugZIeSk90Swv2YFeHHrgkWZ3CIYRBCrBZClAghyoQQTwzy+b8JIQqFEKeFEO8JIaYP+KxfCHHK8tD1l/FiQzvFNa2sSnNC4G0gQmg/Juf2a/OBFSPE15NF8aET0tdqE0XvaAY+OlNvJTZjNkv2FNRwy+xwfDydPJc6ZSN0XoELB517XgexOj1KW221qRcnGQq7DYMQwg34HbAGSAXuF0KkXrPZSSBLSjkHeAP42YDPOqWUmZaHrhE76x2v0w0DaO6H/h5lG4utSouk9HIbZ+va9JaiD92t2sjKlA1KupFOVTRR29LtnGyka0m8HTx8oEjNzLzV6VGYJbxbNHHcSY5YMSwEyqSU56SUPcArwKaBG0gp90sprcm+h4EYB5zX4ewpqCU1OoDYEB/nnzxmIfhFKutrXWkxppPWnXRmj2bYFXUj7Smowd0kWJ7kxNiaFY8pmnEo3gFms/PPbyfab8aUCbVidoRhmAaUD/i7wvLeUHwe2DXgb28hRI4Q4rAQYsjkbyHE45btcurq6uxTPAiXW7o4cemKPndMACaTlrpX9q6SlaBTg6YwNyaQPRPo4rCJone0Fg+xC/VWYjNSSvYW1HJjQuj4NIwcDckboK0GKo/rc347EEKwOi2Kj8saaOlSL04yGI4wDIOtmwfNWxRCPARkAT8f8HaclDILeAD4lRAiYbB9pZR/llJmSSmzwsMdHxzbW1iLlDq5kawkr4feDm14vIKsSo8it6KZqib1DJtd9HZqrR2S12mtHhSj9HIb5+vbP1316cLslWByh+J39NNgB6vTo+jpN7O/WL1xvYPhCMNQAcQO+DsGuG60lxBiBfAfwEYp5adRGillleX5HHAAmOcATTazp6CG+DBfZkf66XF6jRk3g1egur7WNGvq3iRbNZw7oLV2SFmvt5IxYV3lOaXSfyimBGvf/6LtoGA9zLzYYCL8vSaMO8kRhuEYMEsIES+E8ATuA67KLhJCzAP+hGYULg94P1gI4WV5HQYsBQodoMkmmjt6+eRsAyvTIhF6Bg7dPbU7p5Kd0N+nn44xMtOSurdnguV0j0jxds2gz1imt5IxsaewhnlxQUQGODFFezBS1kPjWagr1lfHGDBZOjF/cKaOrt5+veXYjd2GQUrZB3wN2AMUAa9JKQuEED8UQlizjH4O+AGvX5OWmgLkCCFygf3A01JKpxuG90tq6TPLT+94dSV5PXQ2av12FGR1ehRHzjfQ2N6jtxTnYO7X5hfPXqkZdsUob+wgv7JFXxeqlaR12rOiK+aVaVF09PRz6Gy93lLsxiF1DFLKnVLK2VLKBCnlU5b3/ktKuc3yeoWUMvLatFQp5SEpZYaUcq7l+RlH6LGVvQW1RAZ4MTcmSI/TX03iCnDzUvfiSNVS996fIL7WESk/Ah0NWnxBQfYWaqs7lzAMAdEQc4OycYYbZ4bi7+U+IaqgJ33lc1dvPx+cqSM7NRKTyQXyz738IOE2LXVPQV9r+rQAogO9J0+coWg7uHlqBl1B9hTUMDvSj/gwX72laCSvh+pcaCofeVsXw9PdxK3JEbxbVEu/4m3oJ71h+Lisno6eflamusAdk5XkddB8CWpO663EZoQQrEyN5MPSOjp71Pe1Dou0tEufeSt4+eutxmYa23vIudDoGqsFK9Y6kOId+uoYIytTI6lv6+HEJbWnGk56w7C3oBZ/L3cWzwzVW8o/SVoLwqTuxZEWRVevmYOljq83cSlqC6DponaXqyDvFdVilrjWTVFoAoQnKzuf5NakcDzdTMqvmCe1Yeg3S94rruXW5Ijxn1ZlC75hELtY2TjDwvgQArzdP/VfT1iKdwBCawKnIHsLa4kO9CZ9WoDeUq4meR1cPKRkG3p/bw+WJIZa6qLUdSe50K+h8zl56Qr1bT365m8PRfI6uFwAVy7orcRmPNxM3J4SyXtFtfT1q9fiYNQUb4fYReAXobcSm+ns6edgqRZb0zVFezCS1mldahXtG7YyNYqLDR2cqVW3b9ikNgx7C2vxcBPObTM8WpLXas/Fas5oyE6N5EpHLzkX1fa1DkmTJQakaDbSwdI6unrNruVGsjJ1HvhHK+tKXZEagVC8Df2kNQxaf5galiSE4e+tU3+Y4QiZCeEpyl4cy2aH4+lumhCpe4NiNdiKGoZ9hbX4e7uzaGaI3lKux2TS3HNl70Fvl95qbCbC35t5sUHsU9iVOmkNQ9nlNi40dLDSmZPabCV5HVxS09fq5+XOTYlh7C2coCM/S3ZoQdLQQVt7uTRabO0yy5Mj8HBz0Z+A5HVam5Hzao78zE6NIq+yWdmRny76rRh/rIHRFSmubBjWgjRrLZ0VZGVqJBVXOimqbtVbimPpvAIXPtayxxTk+MUrNLb3uKYbycqMm8HTX9nspGxL3PJdRVcNk9cwFNQwN9YF+sMMR7TV16rmxXF7SiRCoPSSelBK92nBUUXTVPcW1ODpZuIWV4ytWXH3glnZWrsRs3r1MIkRfswM81U2M29SGobali5yK5pdMxtpICaTdld69n0lZzSE+3tpvtYidYNwg1K8HfyitCCpYkgp2VtYy5LEUPy8nDzC01aS10F7HVTk6K1kTGSnRnL4nJozGialYbDewbq8YQDNndTbAefU9bXmV7ZMnBkNfd1aUDRpjWa4FeNMbRuXGjs+dXW4NLOyweSh7Io5OzWS3n7JgRL1Cj3V+2Y7gH2FtUwP9SExQsfZC6PF6mstUTM76VNf60SZh3v+Q+hpUzgbSVu9uXRszYp3IMy4SWtDryDz4oIJ9fVU0pU66QxDW3cfn5xtIDvFBQt7BmOC+FpVvDgGpXgHePpBvJqzF/YV1rp+bG0gyeugoQzqzuitxGbcTILbUyI4UHyZnj61Cj0nnWH4oKSOnn6zGktpK4av1TUwmzUDnXi7ZrAVQ5nY2kCs7UaUXTFH0drdx5HzDXpLsQmHGAYhxGohRIkQokwI8cQgn3sJIV61fH5ECDFjwGfft7xfIoRY5Qg9w7GvsIZgHw8WTA8e71M5jsQV2jxcRZfUKvtar6LqpDawPklNN5LVnafUTVFgDETP1QyygtyUGIa3h0m5FbPdhkEI4Qb8DlgDpAL3CyFSr9ns88AVKWUi8Evgp5Z9U9FGgaYBq4HfW443LvT2m3m/+DLLkyNxd9XCnsGYEgTTlyprGFT2tV5FyQ4QbpprT0GssbVZKsTWBpK0FsqPQpt6w5+meLpx86xw3lWsqZ4jfh0XAmVSynNSyh7gFWDTNdtsAp63vH4DuF1oDv5NwCtSym4p5XmgzHK8ceHY+UZauvrUumOykrwO6s9AfZneSmxGZV/rVRTvhOlLwMcF20iMQFt3H4fKGlihSmxtIElrAQlnduutZExkp0ZS1dxFQVWL3lJGjSMMwzRg4LilCst7g25jmRHdDISOcl+HsbewFi93E8tmh43XKcYPw9eqL43noK5I2WykD88oGFuzEpUBgXHKNpS8PTkCk0CpYjdHGIbBbj+uXTMNtc1o9tUOIMTjQogcIUROXd3YfNW9/WZWpETi4+nihT2DERSnXSCKXhyq+lo/xfrvrmgbjH2FtQT5eJClUmzNirDMvDi3H3ra9VZjM6F+XiyYHqzUd98RhqECiB3wdwxQNdQ2Qgh3IBBoHOW+AEgp/yylzJJSZoWHj62U/6nNGfz2AfWqVT8laZ02fL5NvSCuqr7WTynZCZHpEDxdbyU20/dpbC1CrdjaQJLXQl8XnN2vt5IxkZ0aSVF1C+WNHXpLGRWO+JYcA2YJIeKFEJ5oweRt12yzDXjY8vpu4H2p/TpsA+6zZC3FA7OAow7QNCTK+VcHkqy4rzVFPV8rAO0NcOkTZVcLRy800tzZq1aa6rVMX6oVvCmagJFtaVj4niKFnnYbBkvM4GvAHqAIeE1KWSCE+KEQYqNls2eAUCFEGfBvwBOWfQuA14BCYDfwVSmlelVcziJqDgTGKpu6tzwlQs2meqV7tS63yWoahn2FtXi6m7h5lgs3zRsJNw+YtVK7KVKw0DM+zJfECD/2TRbDACCl3CmlnC2lTJBSPmV577+klNssr7uklPdIKROllAullOcG7PuUZb8kKaWav3jOwuprPfs+9KixJB1ImJ8XC+LU8rUCWsDffypEZ+qtxGaklOwrrOWmxDB8Xb1p3kgkrYWOBs2dqiDZqZEcOaet3lwdRR2Ok5iktdDXqQXiFCQ7NZLC6hYqrihi2Hq7oOx9zSAr6IYsrmml4kqnmtlI15K4wtJUT9XMvEj6zJIDJa5fj2EYBtWYcRN4BSqbnaTcAJPzH2iTxBR2IwkBt6dE6C3FfrwDtB5VJTtBwQSGzJggwv29lEhbNQyDarh5aJW3ivpaZ4b7kRDuq4yvVWua5691uVWQfYW1ZMYGEeGvSNO8kUhea6kpKdFbic2YTIIVKRF8UFJHd59rX7uGYVCR5LXQUa+1CVCQ7NQozdfa4eK+VmvTvFnZSjbNq27uJK+yeWK4kaxYM8MULfRcmRpFW3cfh8+59hx3wzCoSKL6A0z6zJIDZ1zc11qZA+2Xla12flelgVSjJWCqNjlPUVfqjQmh+Hi6sbfAtacaGoZBRRT3tc6LDSLMz4u9BS7uTirerhlgRZvm7S2sJT7Ml4RwxZrmjUTyOs1ot1TrrcRmvD3cuDUpnHeLajGbXffaNQyDqijua81OjeRAyWXX9rUW79SC/d6BeiuxmZauXg6fayA7VcGmeSNhbXt+Rs3s9uzUSGpbujld2ay3lCExDIOqKO9rjaS9p59DZ120qV7dGWgoVdaNdKCkjt5+ObHiC1YiUiB4hrJpq7clReBmEp+OWXVFDMOgKp/6WtW8OG5MCMXX08113UlWg2vtaqsYewtqCPPzZH6cgk3zRkIISF6vzd/uUqy9ChDk48mi+BDX/e5jGAa1SV4HlceV9bXe4sq+1uKdWqVzYIzeSmymu6+fAyV1rEiJxM00wdxIVpLXQX8PlL2rt5IxkZ0aSenlNs7Xu2a3WMMwqEzyeu1Z0cZiK1OjqGvt5lRFk95Srqa1FiqO/fPfVzE+OdtAW3cfK9MmoBvJSuwi8AlVdsVsdfG5qjvJMAwqE54MITOVTVu9LSkCd5NwvSV1yU5AKlvtvLewFh9PN5YkKDiQarSY3DQ3X+le6OvRW43NxAT7kBod4HrffQuGYVCZgb7WThe76x4FgT4eLJoZwl5Xu2sq3qEFNyOuHV3u+pjNWtO8W5PC8fYYt/HprkHyeuhugQsH9VYyJlamRXL80hXqWrv1lnIdhmFQneT1YO6D0n16KxkTK1OjOFfXTtnlNr2laHS1aP2Rktcr2TTvVEUTda3drLT0/5/QzLwVPHyUdSetSotCSnjXBdvDGIZBdWJuAN8IZd1JVl/rHlepBC3dqwU1UzborWRM7C2oxd0kuC1pAjTNGwmPKZB4u+b6M5v1VmMzyVH+xIZMcZ3v/gAMw6A6JpPmCy97V2sRrRhTg6YwJybQdVoEFG8H33DN4CrI3sIaFs8MJdDHQ28pziF5PbRWQ9VJvZXYjBCCValRHCproLXLtfqG2WUYhBAhQoh9QohSy/N1SdNCiEwhxCdCiAIhxGkhxGcGfPacEOK8EOKU5aHeJBRXIHkD9LRpLhAFWZUWRW5FM9XNnfoK6evWXHJJa7XgpmKUXW7lXF37xM5GupbZq0C4KbtiXpUeRU+/mf0lrjXH3d4VwxPAe1LKWcB7lr+vpQP4nJQyDVgN/EoIETTg8+9IKTMtj1N26pmcxN+stYZW9eJI0/zhumdonPtAM7CKupH2WP79JmS181BMCdbalhS9o7eSMTE/LpgwP0/XWTFbsNcwbAKet7x+Hrjj2g2klGeklKWW11XAZUDh4bMuiLsXzF6pFWUpOKMhMUKb0aC7r7X4Hc3Axi/TV8cY2Z1fQ2ZsENGBU/SW4lxSNmjtSxTsG+b2ad8w15rRYK9hiJRSVgNYnoeNeAkhFgKewNkBbz9lcTH9UgihXtN7VyF5vTaj4dJhvZWMidXpURw538iVdp1y0s39mmFVdPZCxZUO8iqbWZ0+CbKRrsVaiFi0TV8dY2Rlmjaj4VCZ6/QNG9EwCCHeFULkD/LYZMuJhBDRwAvAo1JKawrB94Fk4AYgBPjeMPs/LoTIEULk1NW5lj/OJZiVDW5eyi6pV6VF0W+W+qXulR/RDGuKmtXOVjeS1S03qQiIhpiFyn73lySE4uflrv+KeQAjGgYp5QopZfogj7eBWssPvvWHf9DJK0KIAGAH8J9SysMDjl0tNbqBZ4GFw+j4s5QyS0qZFR5ueKKuw8sfEpZrF4eCMxoypgUyNdD70x84p1P0Drh5akOQFGRPQQ3JUf7Eh/nqLUUfUjZAdS5cuai3EpvxctdmNOwrrKXfRfqG2etK2gY8bHn9MPD2tRsIITyBt4C/Sylfv+Yzq1ERaPGJfDv1TG5SN0JLBVSd0FuJzQghWJkWxcHSOjp6+px7cik1w5CwXBuCpBh1rd0cu9A4OVcLVqwrPUUTMFanR9HQ3sOxC64x8tNew/A0kC2EKAWyLX8jhMgSQvzVss29wDLgkUHSUl8UQuQBeUAY8GM79UxuZq/WUvcUXVKvSouiu8/MAWen7lWdgOZySNno3PM6iHeLapGSyRlfsBIyEyIzlP3u35YUgZe7id35ruFOssswSCkbpJS3SylnWZ4bLe/nSCm/YHn9Dymlx4CU1E/TUqWUy6WUGRbX1ENSShfpi6AoPiFa6mrhNiXdSTfMCCbU15Ndzr44CreByV3Z2Qu782uYHupDcpS/3lL0JWWDlnzR6notJkbC18udW2aHszu/xiXa0BuVzxONlI3QeBYuF+mtxGbc3UysTIvi/aJaunqdlLonpZbNMuNmzbAqRnNnL4fO1rM6LWrijfC0lZQNgFR2quGajChqWro4Wa5/Q0zDMEw0ktcDQtkl9Zr0KNp7+jlYWu+cE9YWaLOzU9V0I71fXEtvv2TlZI4vWIlIgZAEbQWoIMuTI/FwE+zO13/wlmEYJhr+kRC3WNmc7hsTQgmc4sGuPCddHEXbAKHsUJ6deTVEBXgzLzZo5I0nOkJoBv78h9DhGkFcWwic4sHSxDB25dcgdXYFG4ZhIpKyAWrzoeHsyNu6GB5uJlamRrKvqNY5laCF22D6EvBTrxtpa1cvH5ypY01GFKaJOsLTVlI3gexXNjtpbXo0FVc6KajSd5a1YRgmItbsmsKt+uoYI2syomjtckIlaH0p1BUpm430fvFlevrMrMuI1luK6xCdCUHTofC6zHklyE7V5nTv0tmdZBiGiUhQrNY2ukBNw7A0MQx/L3d2jrc7yfrjoWjTvJ151UQGeDE/7rqmxpMXISDtDjh3QEl3UrCvJ4tnhujuTjIMw0Ql9Q6oOa2kO8nL3Y0VqZHsLaylt38cB7AUbNWGygdOG79zjBPt3X0cKKljTXq04Ua6ltQ7tKmGJTv1VjIm1qRHc66unZLaVt00GIZhopJqaWVV8Ja+OsbI6vQomjt7+eTsOLmT6kuhNg/SNo/P8ceZ94ov091nZq3hRrqeqfMgKE7ZFfPq9ChMArbn6udOMgzDRMXqTlI0znDL7HB8Pd3YcXqcLo6CtwDxTwOqGDtPVxPh70XWdMONdB1CaKuGcweg84reamwmzM+LJQlhbD9dpZs7yTAME5m0zVCTB/VleiuxGW8PN7JTI9ldUENP3zi4kwregrgbIWCq4489zrR397G/5DJr0o1spCFJuwPMvVordQVZNyeaCw0dumUnGYZhImO9Gy5U0520fs5Umjt7+ajMwb2TLhfD5UJl3Uj7SzQ30hrDjTQ0U+dr7iRFV8yr06JwMwm2j9eKeQQMwzCRCYzR+tQXqJm6d/PsMAK83R3va1XcjfRObhUR/l7cMEO9Fh5Ow+pOOrtf2eykpYlh7MjTx51kGIaJTtpmLchaX6q3EpvxcndjVVoUewsd2DtJSs0wzLhJqxJXjJauXvaX1LF+zlTcDDfS8KTfpbmTFG0Ps35ONOWNnZyuaHb6uQ3DMNFJ3QQIyN+it5IxsWHuVNosqZkO4XIh1JdoPmgF2ZOvxVw2zDXcSCMSPRdCEyH/Db2VjIlVqVF4uAm2n65y+rkNwzDRCZwG05dC3utKtuJekhBKiK+n4y6O/C0gTJCiqBvpdDVxIT5kGr2RRkYISL8bzh+EVteYc2ALgT4e3DwrnB2nq53eitswDJOBjLuhoVQbfagY7m4mVqdH8V7RZfsnu0mp3T3G3wJ+6o2HbWjr5uOyejbMjTZabI+W9LsAqWw9z/o50VQ1d3HiknPTbu0yDEKIECHEPiFEqeV50KRqIUT/gOlt2wa8Hy+EOGLZ/1XLGFADR5O6CUwe2qpBQTbMmUpnbz/vFQ06Unz0VByDKxdgzr0O0eVsduZV02+WbJirXoqtboTPhqgMyFPTnbQyLQpvDxNbT1U69bz2rhieAN6TUs4C3rP8PRidA6a3DexY9lPgl5b9rwCft1OPwWD4hEDiCs2NYh7HFhPjxML4ECIDvHj7lJ3upNOvgbu3si2238mtZnakH8lR6s2l1pX0u6EyBxrP663EZvy83MlOjWLH6erxbQ9zDfYahk3A85bXzwOjjugJbS28HLCacpv2N7CRjLuhtQouHdJbic24mQSbMqdxoOQyje09YztIfy8UbNHmYnur98Na1dTJ0QuNbDRWC7aTfpf2nP+mvjrGyB2ZU7nS0cuHZ5w3C91ewxAppawGsDwP1dTeWwiRI4Q4LISw/viHAk1SSqvjuAJQr5uZKiStAQ9fZd1Jd2ROo88s2THWIPTZ/dDRoKwb6Z1c7b97/RzDMNhMUCzELlbWMCybHU6wjwdb7V0x28CIhkEI8a4QIn+Qhy1pHXFSyizgAeBXQogEYLDo2ZChdyHE4xbjklNX5zzLOWHw9IXktVpjsb4x3nXrSEq0P0mR/rx1coy+1rzXwDsIErMdK8wJSCnZcqKSeXFBzAjz1VuOmsy5R0tVrsnTW4nNeLiZWDcnmn2FNbR125mAMUpGNAxSyhVSyvRBHm8DtUKIaADL86DRQSllleX5HHAAmAfUA0FCCHfLZjHAkCZRSvlnKWWWlDIrPFy9jBKXIOMe6GqCsnf1VmIzQgg2z5/GiUtNXGxot23n7jYo3qHVLrirl99QWN1CSW0rd86P0VuKuqTdqSVgnHpZbyVj4o7MaXT1mtlb4Jy0W3tdSduAhy2vHwau670ghAgWQnhZXocBS4FCqdV57wfuHm5/AweSsBx8wiBXzYtj49ypCAFbT9q4pC7ZBb0dkKGmG2nLiUo83AQb5hhFbWPGJwSSVmsrx37n3HU7kgXTg4kJnuI0d5K9huFpIFsIUQpkW/5GCJElhPirZZsUIEcIkYtmCJ6WUhZaPvse8G9CiDK0mMMzduoxGA43D83HXrJLyf4xU4OmsDg+lK2nKm3rH3P6FQiI0bqpKkZfv5m3T1Vye3IkQT7qrXZcirn3Q3sdnH1fbyU2I4RgU+ZUPiqto661e9zPZ5dhkFI2SClvl1LOsjw3Wt7PkVJ+wfL6kJQyQ0o51/L8zID9z0kpF0opE6WU90gpx/+/eLKT+YDWP0bRQNzm+dM4X99O7mj7x7RUaT8Ec+8Dk3r1nAdL66lv6+HO+UZeht0kZsOUEMh9SW8lY2LzvGncNT+G7j4H9Q0bBvWuFAP7iMrQHqde1FvJmFiTHoWXu4k3j1eMbofcl0GaNYOoIG+eqCDYx4Nbk4ZK+DMYNe6eWpyteCd0NumtxmYSI/z5+T1ziQn2GfdzGYZhMpL5IFSdhNrCkbd1Mfy9PVidHsXbpypH7rgqJZx8EeKWQGiCcwQ6kObOXvYW1rJx7lQ83Y1L1SHMvQ/6u5Wd0+AsjG/bZCTjHjC5K7uk/kxWLC1dfewZKUOj/Ag0noV5DzpHmIPZmVdNT5/ZyEZyJFPnQViSstlJzsIwDJMR3zCYtUprEaFghsbimaHEhkzh1WPlw2948h9aUV+qmgX1rx4rJzHCjzkxgXpLmTgIAZn3Q/lhJUfeOgvDMExWMh+Atlo4+57eSmzGZBLcuyCWQ2cbuNTQMfhGPe1aR820O8DLz7kCHUBxTQunypu4f2Gc0UnV0cx9QFsxn3h+5G0nKYZhmKzMWgm+4XBczYvj7qwYTAJePz7EqqFwG/S0afEUBXnlaDmebibunGdkIzkc/0itZ9apl5TsAuAMDMMwWXH31H40z+zWUjoVIzpwCstmh/PG8Qr6BxticvIfEBwP05c4X5yddPX2s+VEBavTowj2NWoXxoUFj0JHPZTs0FuJS2IYhsnMgodB9sOJF/RWMibuzYqlurmLg6XX9M66XAwXP9L++xR0w+zMq6alq4/7FsbqLWXiknAbBMbC8ef0VuKSGIZhMhMyU2uTceJ5JYPQK1IiCfH15OWjl67+IOdv4OYJ8z6rjzA7eeVoOTNCfbhxZqjeUiYuJjeY/zk4d0DJOQ3jjWEYJjsLHoWWSijbp7cSm/F0N3FvViz7CmupaurU3uxp14raUjdp2VeKUXa5jaMXGrnPCDqPP5kPavO/T/xdbyUuh2EYJjtJa8AvEnKe1VvJmHhwURwALx2xrBry3oDuFshScxjgK0cv4W4S3GXULow/gdO0tO1TL2qDnAw+xTAMkx03D83lUrYPmkaoC3BBYkN8WJ4cyctHL9Hd2wfH/goRqRC3WG9pNtPe3cerOeWsTo8i3N9LbzmTg6xHtbTtom0jbzuJMAyDgSUILeG4mquGz904nYb2Hg5/uBdqTsMNn1cy6LzlZCWtXX08ujRebymTh8RsLdZ2+A96K3EpDMNgAEFxkLxOcyf1DFEw5sLclBjGzDBf+o89A55+MOczekuyGbNZ8tzH5z9SptoAABE5SURBVJkTE8j8uCC95UweTCZY+CWoOAYVx/VW4zIYhsFAY/FXoLNRm12gGCaT4IvzfFnaeYCGhM3g5a+3JJs5WFbP2bp2Hl06wwg6O5t5D4JXABwxVg1WDMNgoDF9CURnwie/B7NZbzU2s7lvJx7085fe1XpLGRPPfXyecH8v1mVM1VvK5MPLH+Y9pLVQaanWW41LYJdhEEKECCH2CSFKLc/Bg2xzmxDi1IBHlxDiDstnzwkhzg/4LNMePQZ2IATc+FVoKFVvJnRPO94n/0Zx0M08U2SiprlLb0U2ca6ujf0ldTy4KM5or60XCx8Hcz/kGEMkwf4VwxPAe1LKWcB7lr+vQkq5X0qZKaXMBJYDHcDeAZt8x/q5lPKUnXoM7CH1DvCfCp/8Vm8ltnHyRehqInTltzFLeOajc3orsonnDl3A083Eg4um6y1l8hISr6Vu5/wNejv1VqM79hqGTYC1C9vzwEj9je8Gdkkp1YtwTgbcPWHhF+H8B1CTr7ea0WHu1wxZzEIi025h/ZxoXjpyieYONfLS61q7efVYOZsypxopqnqz+CvQ0aDsdENHYq9hiJRSVgNYnkeaP3gfcO2EjKeEEKeFEL8UQgx5ZQghHhdC5Aghcurq6obazMBeFjwCHj5w6Nd6KxkdRe9A00VY8nUAvrQsgfaefl44fEFfXaPkmY/O09tv5l9uVW/C3IRjxk0Quwg++tWk77o6omEQQrwrhMgf5LHJlhMJIaKBDGDPgLe/DyQDNwAhwPeG2l9K+WcpZZaUMis8PNyWUxvYgk8IZD0Gea+7/iATKTUDFhyvpdsCqVMDuDUpnGc/vjDy6E+daero4YVPLrB+zlRmhqs3M2LCIQQs+w40l8PpV/VWoysjGgYp5QopZfogj7eBWssPvvWH//Iwh7oXeEtK+ekaX0pZLTW6gWeBhfb95xg4hKXfBDcvOPi/eisZntJ9UHlc02ty+/TtL9+SQEN7D68fr9BR3Mg8+/EF2nv6+eptiXpLMbCSuAKi58LBXyjZWNJR2OtK2gY8bHn9MPD2MNvezzVupAFGRaDFJxRxbE9w/CK06uHTr0LDWb3VDI6UsP/HEDRdSzUcwKL4EObFBfHHA2fp7nPNVUNrVy/PHbrAytRIkqLUq7uYsFhXDVfOQ8EWvdXohr2G4WkgWwhRCmRb/kYIkSWE+Kt1IyHEDCAW+OCa/V8UQuQBeUAY8GM79Rg4iiXf0FYNH7roqqF4O1Tnwi3f0/o9DUAIwbeyk6hs6uTFw5eGOIC+/OPwJZo7e/nacmO14HIkrdP6bX34v0rW9DgCuwyDlLJBSnm7lHKW5bnR8n6OlPILA7a7IKWcJqU0X7P/cillhsU19ZCUss0ePQYOxD9SizW44qrBbIb9/wOhiUO2v7hpVhhLEkL53f4y2rpdyyXQ3NnLnz48yy2zw5kTY7S/cDlMJrj5W1BfAvlv6q1GF4xqGoOhWfpN7W78g5/qreRqCt+Cy4VwyxPg5j7kZt9ZlURDew9/+8i1BrH8fn8ZzZ29fG91st5SDIYi7U6IyoD3fgi9ahVMOgLDMBgMjX8kLP4XbdVQkaO3Go3+Xtj/EwhPhvQ7h910Xlwwq9Ii+cuH52hsd430w/LGDp79+AJ3zY8hdWqA3nIMhsJkgpVPQfMlOPJHvdX8EznIfPNxwDAMBsNz87e0QT67n3Dal3JYjv5Za9tx+5NXZSINxbdXJtHe08cfDrhG6u3/7i3BZIJvrZyttxSDkZh5izbI5+AvoL1ebzVa0elfVzjFtWsYBoPh8fLXfoQrjmm1DXrSWqutFhKztfYFo2BWpD93zY/huUMXKLvcOs4Chye3vIm3T1XxhZtmEh04RVctBqMk+4fauFi93almM+z4Ny1basp1LekcjmEYDEZm7v0wdR7se1K7SPTi3SehvxvW/NSmQTzfW5OMj6c7//FWPlKnVY/ZLPnxjkLC/Dz5slHlrA4Rydogq2PPQF2JfjpOvgDlRyD7R1oR6jhjGAaDkTGZYPXT0FoFB/+fPhouHYbcl+HGr0GobT+sYX5ePLEmmSPnG3nzROU4CRyeF49e4tiFK3x3VTJ+XkMHzA1ckFu/D15+sO0bWm8uZ9Ner90UTV8KmQ845ZSGYTAYHXGLtdTQj38FVU5ugtvfCzu/DQHTYNm3x3SIz2TFsmB6MP+zs4grTg5EV1zp4OmdRdw8K4x7smKcem4DB+AXAat/CuWHtRiXs9n3X9DdCut+4bSRtYZhMBg9q58G33B460vOTeE78DTU5Gnn9/Qd0yFMJsFTm9Np6ezlf3YWOVjg0Egp+f6WPAB+cmeGMZ1NVebeB7NWwrv/DY1ObOt+/qDW7XXJ1yEixWmnNQyDwejxCYFNv4W6Ynj/R84554WPtKyQzIcgdaNdh0qOCuCLy/7/9u49OKr6CuD49yQhgEQMiJGSB88gRqw8IiXacUQewhQJFVCiUAatthYfqB1Fa6eM1qpTpsAoY0uFikJjBVRAUQQFY60yEKIoz6RQIBIkUTEgyCM5/eO31oRKSLJ77w275zOTye7dm3vPnezuub93FxYWlvLaRn9W6lpYWMq7xRVMGdaDtDZn+XJO4wERGD7DjetZcoc/I6K/roCXboW2Xdw0HT6yxGAaptsgNyL6/VnuS9tLR7787oMxLDK9Qu4e1J3eGcncv3gjOyu8bUgv2X+IR5Ztpl/ntrYITzQ4JxWufhR2/RPef9Lbc1VXu5L54c9hzLONLik3liUG03CDH4E2nWDxLVC515tzqMKyyXDoMxj1V9f4FwGJCXE8dUMfEuKFSQs2eDY191dHjnPLc+tp3iyO6df3Ii7OqpCiQu/xkJULq6a62X298t4Mt8Tu0MfcbK8+s8RgGq55Elz/PBythAXXuYaxSFvzGGx+Ba56CFL7RvTQqcktmX5dLzaXVTJ16aaId2GtqlbuzC+i9MvDPD2uL6nJNmYhaojAyKch5SJYdBOUb4/8OXa+C2//3k3LkX1T5I9fD5YYTOO0vxjGzHNzFi2cGNm569fOdgOKeo+DyydH7rg1DOiRwqQBXXlh3R6mvbktosnhiTe28s72ch7O7cmlnbzvc258ltgK8v4O8YmQP9ZVeUbKnnXumOd2hWtm+tYL6WSWGEzjZQ5yXehKVsKrkyPTx/vjRfD6fW7q4+HefjDuHXwBef3SmbX638xYVRz28VSVx5ZvYXbBDsb370hev4wIRGmapOQMuH4+HNgN80bAobrWKKunvUUwf5Tr+fezJdAiuLm0LDGY8GRPdD0mip6H/LzGVyupwvq58PIvoeNlMHpOnTOnRkJcnPDoyIsZ0zeNmW8VM3NVcaNLDieqqrl/8Ub+UrCDcf0zmDrioghHa5qcjjmQlw+fl8CcIeF1Y91bBM+NhBbnwIRl0LpD5OJshLASg4iMEZFNIlItItl17DdURLaJSImITKmxvbOIrBWRYhH5h4gkhhOPCchVD4VKDqtgztXuLqohjh2GV34Fr97tJi7Ly4dm/tTLx8UJj4/6Idf2SWX6qu3c8lwhFYeONugYX3x9jNsWbODF9aXcOTCTR3J7Em+NzbEhc7D7Iv/mK/fe3722YX9fXe16+M0ZAolJMGEpJKd7E2sDhFti+AS4Fig41Q4iEg/MAoYBWUCeiGSFXn4CmK6qmcCXwM1hxmOCcunP4caFbiH1P//YTZ1xunmVVGHHGveh+Cjfra9ww0J31+Sj+Dhh2uhL+O3wLAqKyxk6o4CVmz87bemhqlqZ/8EuBkxbw+qt+5l6TRb3DO5ug9hiTVo23LQCmrWAuUPg5dvchI+nc2APLBgFKx503cB/8Q607ex9vPUgkWh0E5E1wK9V9f8m7ReRHGCqql4dev5A6KXHgXKgvaqeOHm/umRnZ+v69U1kfQBTW0Wxe6MXvwmtUiBnkpvjpX1PVwqoOgGVn7oJwf71JOzb6Kb1HvEUdB8SdPRs23eQu14oYuu+g3RLSWJ03zRG9krl/NbNERFUlc1llRRsr2DZR3vZXFZJTpdzeTj3IjLPt7WbY9rRQ1DwR1cCSGgBfcZDlwGuarR5krsROn7Y3QwVznNtc/HNYegfoO9EXxqaRaRQVU9Zu/O//XxIDKOBod8u9Ski44EfAVOBD1S1W2h7OvC6qvY83fksMZwBdr3vRkfves89l3g4u70bl1Ad6sHUrrsb6n/xde5uq4k4eqKKlzd8ysLCUgp3uR4nItAqMQEROPiNi79H+7O57cqujLikg5USzHcqStykd8Ur3WzAcQnQItlVN1Ufd/sktYfeN0KfCdDGv8GP9U0Mp23dE5FVQPvveek3qrqkPrF8zzatY/up4rgVuBUgI8N6ezR5HXNg4nJXXC77EMo+co/PSYXkjtAuE9L7u5lbm5jmCfGM7ZfB2H4Z7Cg/xNtb91N55DiHjlZxrKqKXultuCKzHSmtm04yM01Iu24wdgEcP+JmBd75Dhw5AC2TXYJIuRC6DvS8c0U4ThuZqg4K8xylQM3WlDRgL1ABJItIgqqeqLH9VHHMBmaDKzGEGZPxS3K6+7nwmqAjaZQu5yXR5bzIjLo2MaZZS+g6wP2cYfy4XVsHZIZ6ICUCY4Gl6uqwVgOjQ/tNAOpTAjHGGOOhcLur/lRESoEc4DURWRHa3kFElgOESgO3AyuALcCLqropdIj7gXtEpAQ4F5gTTjzGGGPCF5HGZ79Z47MxxjRcfRufm17LnzHGmEBZYjDGGFOLJQZjjDG1WGIwxhhTiyUGY4wxtZyRvZJEpBzY1cg/b4cbXBdL7Jpjg11z9Av3ejuq6nmn2+mMTAzhEJH19emuFU3smmODXXP08+t6rSrJGGNMLZYYjDHG1BKLiWF20AEEwK45Ntg1Rz9frjfm2hiMMcbULRZLDMYYY+oQU4lBRIaKyDYRKRGRKUHH4yURSReR1SKyRUQ2ichdQcfkFxGJF5EiEXk16Fj8ICLJIrJIRLaG/t85QcfkNRG5O/S+/kRE8kUk6lZNEpG5IrJfRD6psa2tiKwUkeLQ7zZenDtmEoOIxAOzgGFAFpAnIlnBRuWpE8C9qnoh0B+YFOXXW9NduCneY8VM4A1V7QFcQpRfu4ikAncC2aGlgONx67xEm2eBoSdtmwK8paqZwFuh5xEXM4kB6AeUqOoOVT0GvADkBhyTZ1S1TFU3hB4fxH1ZpAYblfdEJA34CfBM0LH4QURaA1cQWstEVY+p6oFgo/JFAtBSRBKAs6hj9cczlaoWAF+ctDkXmBd6PA8Y6cW5YykxpAJ7ajwvJQa+KAFEpBPQG1gbbCS+mAHcB1QHHYhPugDlwN9C1WfPiEiroIPykqp+CkwDdgNlwFeq+mawUfnmfFUtA3fzB6R4cZJYSgzyPduivkuWiCQBi4HJqloZdDxeEpHhwH5VLQw6Fh8lAH2Ap1W1N/A1HlUvNBWhevVcoDPQAWglIuOCjSq6xFJiKAXSazxPIwqLnzWJSDNcUligqi8FHY8PLgdGiMh/cFWFV4nI/GBD8lwpUKqq35YGF+ESRTQbBOxU1XJVPQ68BFwWcEx++UxEfgAQ+r3fi5PEUmJYB2SKSGcRScQ1Vi0NOCbPiIjg6p23qOqfgo7HD6r6gKqmqWon3P/3bVWN6jtJVd0H7BGRC0KbBgKbAwzJD7uB/iJyVuh9PpAob3CvYSkwIfR4ArDEi5MkeHHQpkhVT4jI7cAKXC+Guaq6KeCwvHQ5MB74WEQ+DG17UFWXBxiT8cYdwILQDc8OYGLA8XhKVdeKyCJgA673XRFROAJaRPKBK4F2IlIK/A54HHhRRG7GJcgxnpzbRj4bY4ypKZaqkowxxtSDJQZjjDG1WGIwxhhTiyUGY4wxtVhiMMYYU4slBmOMMbVYYjDGGFOLJQZjjDG1/BfimP+w9xv1qgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e1f828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "Installation:\n",
    "```bash\n",
    "conda install pandas\n",
    "```\n",
    "\n",
    "http://pandas.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1156856d8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107aace80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('iris.csv')\n",
    "data.plot.scatter('petal_length', 'petal_width')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn \n",
    "Installation:\n",
    "```bash\n",
    "conda install seaborn\n",
    "```\n",
    "\n",
    "https://seaborn.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x115695f98>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e1fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "seaborn.pairplot(data, hue='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bokeh\n",
    "```\n",
    "conda install bokeh\n",
    "```\n",
    "\n",
    "https://bokeh.pydata.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"7f0914f3-b295-40fd-8337-6cc4c386d664\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"7f0914f3-b295-40fd-8337-6cc4c386d664\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"7f0914f3-b295-40fd-8337-6cc4c386d664\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '7f0914f3-b295-40fd-8337-6cc4c386d664' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"7f0914f3-b295-40fd-8337-6cc4c386d664\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"7f0914f3-b295-40fd-8337-6cc4c386d664\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"7f0914f3-b295-40fd-8337-6cc4c386d664\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '7f0914f3-b295-40fd-8337-6cc4c386d664' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"7f0914f3-b295-40fd-8337-6cc4c386d664\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"f16c6bc4-d6b4-48c0-9319-c4c0ffd77f01\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"3922fa6a-dba1-458c-b8c4-b8bf65927109\",\"elementid\":\"f16c6bc4-d6b4-48c0-9319-c4c0ffd77f01\",\"modelid\":\"3e04fb38-9631-4023-9e29-3ca11699ab20\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "3e04fb38-9631-4023-9e29-3ca11699ab20"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import  output_notebook\n",
    "\n",
    "output_notebook()\n",
    "p = figure()\n",
    "p.circle(data.petal_length, data.petal_width)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotly\n",
    "```\n",
    "conda install plotly\n",
    "```\n",
    "\n",
    "https://plot.ly/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.graph_objs import Scatter\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "p = Scatter(x=data.petal_length, y=data.petal_width, mode='markers')\n",
    "iplot([p])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Altair\n",
    "Installation:\n",
    "```bash\n",
    "conda install altair --channel conda-forge\n",
    "```\n",
    "\n",
    "https://altair-viz.github.io/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "\n",
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       ".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"
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      "jupyter-vega": "#38d88d25-0213-4f39-8687-197d61a81c3d"
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3672, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 429.0, \"Horsepower\": 208.0, \"Miles_per_Gallon\": 11.0, \"Name\": \"mercury marquis\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4633, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"buick lesabre custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4502, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 160.0, \"Miles_per_Gallon\": 12.0, \"Name\": \"oldsmobile delta 88 royale\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4456, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chrysler newport royal\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4422, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"mazda rx2 coupe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2330, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"amc matador (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3892, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 307.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevrolet chevelle concours (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4098, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford gran torino (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4294, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 14.0, 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\"Year\": \"1972-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"ford pinto (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2395, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"datsun 510 (sw)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2288, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"toyouta corona mark ii (sw)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2506, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"dodge colt (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2164, \"Year\": \"1972-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 97.0, 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\"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 14.0, \"Name\": \"plymouth fury gran sedan\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4237, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 440.0, \"Horsepower\": 215.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chrysler new yorker brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4735, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 455.0, \"Horsepower\": 225.0, \"Miles_per_Gallon\": 12.0, \"Name\": \"buick electra 225 custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4951, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 175.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"amc ambassador brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3821, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"plymouth valiant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3121, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"chevrolet nova custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3278, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"amc hornet\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2945, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"ford maverick\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3021, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 198.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"plymouth duster\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2904, \"Year\": \"1973-01-01\"}, {\"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, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 12.0, \"Name\": \"oldsmobile vista cruiser\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4499, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"amc gremlin\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2789, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"toyota carina\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2279, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 19.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 21.0, \"Name\": \"chevrolet vega\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2401, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 94.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"datsun 610\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2379, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"maxda rx3\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2124, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2310, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 6, \"Displacement\": 155.0, \"Horsepower\": 107.0, \"Miles_per_Gallon\": 21.0, \"Name\": \"mercury capri v6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2472, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"fiat 124 sport coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2265, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 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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\", 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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\", 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\"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, 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\"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\": 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\"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 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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 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{\"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, 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\"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 = \"#38d88d25-0213-4f39-8687-197d61a81c3d\";\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": "#38d88d25-0213-4f39-8687-197d61a81c3d"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#38d88d25-0213-4f39-8687-197d61a81c3d"
     },
     "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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}