exploring_visuals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring with Visuals\n",
    "Use `clean_08.csv` and `clean_18.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load datasets\n",
    "\n",
    "df_08 = pd.read_csv('clean_08.csv')\n",
    "df_18 = pd.read_csv('clean_18.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6584fbb160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584f13160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584f4c160>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6584f07240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584ec0240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584ec0278>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6584e1dda0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584dd9da0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584d92da0>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yzVPuw29mZkCJoOE+/GZmVuHHiJiZWWkOGmZmVpqDhpmZleYHFppZXxuodz/WzP3blJP+\n4JqGmZmV5qBhZmalOWiYmVlpvqbRB+q12YLbbc2sOVzTMDOz0lzTMLOWKFMDtt7joGGjmrtjmjXG\nQcM6wmeh1i18TbAxvqZhZmalOWiYmVlpLQkakvaRdI+kOZJmtCIN6yyXcf9zGVstTQ8aktYEvgbs\nC2wPvF/S9s1OxzrHZdz/XMY2lFZcCN8FmBMR9wFIugQ4EPhjC9Kyzhg1ZdyMC/Y9ehF11JRxGe5l\nt0IrgsYk4KHC8Dxg1+qZJB0LHJsHl0i6pzB5AvD4cInojBHmsoQWp1F3HZupxrpsNYLFNaOM2+KE\nNm/nWkruR63IZ1+XcTeUbcVqHCualfeRlPFqaUXQqPVq2FhlRMTZwNk1FyDdHBE7Nztj3aTH13HE\nZdwuvbKduzCfXV/GXbjNSuvlvLfiQvg8YHJheAvg4RakY53jMu5/LmOrqRVB43fAtpK2lvQS4H3A\nVS1Ip2tJmippXqfz0UJ9U8aSzpN0uqQ9m9G0ImmupLc1I28d1pYy7qPtNWo0vXkqIpZJOh74GbAm\ncG5E3NngYjrapNEmPbuOTSrjdim1nSPi18CrWpyX4XTV/tAjZdxV26xBPZt3RazSTGkjJGkqcGFE\nbNHpvNjwJJ0HzIuIf2/S8uYCH4iIXzRjeZ0maUxELGvh8ufSR9urF4y0TH1HeAmSJku6QtJjkp6Q\ndJakJyVNKcyziaS/SHpZJ/Nqw5P0Bkm3SFos6VJgnTx+pSZFSadImp/nu0fS3nn8aZIul3RpnnaL\npNd3aHVWm6QdJf0+r8P38vqcXtkOef0fAb6d5z9A0q2Snpb0v5JeV1jW5pK+n38f90s6oTDtNEmX\nSbogp3WnpOoLwDtIuk3SopyPdQrf/2C+ufBJSVdJ2jyPH5AUksYU5h2U9IH8eRtJv8rLfDyXdWW+\nV0u6Ni/zHknvLbG9Npb0I0nPSPpd3lY3FKafKemhPH22pD0L03aRdHOe9qikL9RJax1JF+ZjzdM5\nvYl52nhJ35b0sKSnJP2w3rbK00LScZLuBe5d3e0AQET4b5g/UtX8D8AXgbGkg8wewNeBMwrznQj8\nKH+eSjp77Xj+/bdSWb4EeAD4Z2At4GDgb8DpxTIjNVM9BGyehweAV+TPp+XvHJyX8S/A/cBaefpc\n4G2dXteS2+HEvA4HAc8XtsMy4AxgbWBdYEdgIanL7ZrAUXk91yadeM4GPpGX+3LgPuDvC9vrOWC/\n/N3PAjcW8jIX+C2wOTAeuAv4cJ72VlK31B1zWl8Bri+USQBjCssaJNVaAC4G/i3nbx1gjzx+bC7b\no0nN8zvmNF5TZ5tdkv/WI93s+BBwQ2H64cDGeZnTgUeAdfK03wBH5M/jgN3qpPUh4Ec5rTWBnYCX\n5mlXA5cCG+Wy26vetsrTA7g2b+N1V3c7RET3BQ1gQ+By4O68A72pw/l5E/BYcefM43fNG32NPHwz\n8N78eSpDBA3SAetO4I68Y6/T6W3eD3+knj7X5X3mTuDEPH58/rHcS7q4u4DcLJun/y+rBo1tSAfJ\nt5GDQWH+01j5oLdGXuaeeXguJYJGPhj8HvhxHt4auCnn81LgJS3cVm8G5ldthxsK2+H54n4JnAV8\numoZ9wB75d/Bg1XTPg58u7C9flGYtj3wl8LwXODwwvB/A9/In8/Jw5WyvTsf/D7FiqDxi7zNrs3r\nUAkaF5CuG2xRlbdDgV9Xjft/wCfrlNXfgFcVxp1OIWjU+M5TwOvz5+uB/wRuK1PewD/m/fJ1Vcvc\nDHgR2KhGeucA/10YHpfzPJCHA3jrSLZD5a8bm6fOBH4aEa8GXk86CHTSZOCBqGoDjIibgKXAXpJe\nTTrQDNu7RNIk4ARg54h4LWlnfF9Lcj36LAOmR8R2wG7AcUqPvZgBzIqIbcnV8si/kOyB6gVFxBzg\nJNIBb6GkS4pVfQo3vUXEi6TuqZvTmBNZed8+A/hizudTwDENLq8RmwPzq7ZD8Ua+xyLiucLwVsD0\n3FTytKSnSb+LzfO0zaumnQpMLHz/kcLnZ4F1is1KNaaPK+TzAVaU7atJwfxo0u8N4Jd5m80Ctiws\n52TSvSa/zU1i/1hYl12r8nsYsGmtDZW9jHQ2XtxGxc9Imi7prtwc9jSwAekGPkhleQDwSmAPSQcw\nfHl/h9QB4ZLcDPXfktYibfMnI+KpGnmsbCsAImIJ8ATpJs1aeV6d7QB02TUNSS8lnQWdAxARz0fE\n053NFQ8BW1bt5BXnk6qlRwCXV/3QhjIGWDcvbz3c970pImJBRNySPy8mHZAnkR59cX6e7XvAyyQV\nb1zbkhoi4rsRsQfpxxWkH3nF8vsXJK1Bg/cwSNoC2B/4Vh4WqXnh8jzL+cC7yy5vNSwAJlVth+I9\nGdW9Yx4CPhMRGxb+1ouIi/O0+6umrR8R+zUhnw8DW1XKVtJYUs3xblKLBKQyhbTNlgeqiHgkIj4Y\nEZuTmnu+LmmbnN9fVeV3XER8ZJh8PEYKXMWOLcV9YE/gFOC9pFrAhsAiVtwg+RdSYNgfmEMq5yHL\nOyL+FhGfiojtgb8jBZwjc97HS6qs+yrbqpCnsaTmsvmFeapPEhrdDkCXBQ1Se+hjwLeVLtJ9K698\nJ/2W9CObKWlsvki1e572HeA9pMBxQb0FRcR84HPAg3mZiyLi563J9uglaQB4A6n6PzEiFuRJV5P2\n+RMkjZF0EOkZS9Xff5Wkt0pam9Qe/xfghcIsO0k6KAf+k4C/Ajc2kMUvkc6EX8zDGwNPF2qz81j5\nDLHZfkNan+PzdjiQGtuh4JvAhyXtqmSspP0lrU/6fTyjdOF8XUlrSnqtpDc2IZ/fBY6WtEMui/8C\nbgVeDfycdBB8u9LDFfclXVMBQNIhOThDOmBHXucfA6+UdISktfLfGyVtN1QmIuIF4ArgNEnr5ZaF\nIwuzrE8KKo8BYyR9AnhpYfoVOe8v5Pkg/fZrlrekt0iaktfrGVIz0wt5P/4JKQBulPP+5mG21U0R\nMXeI1Wp4O1R0W9CoXJA5KyLeQGr+6egjmfMO805SdfhBUgEfmqfNA24h7ZC/rrcsSRuRzny3JlUn\nx0o6vDU5H50kjQO+D5wUEc8Up0XE88ASYBrpQHIo6QddbW1gJunC4CPAJqQml4or83efItUyD4qI\nv5XM3wHAwoiYXRxdY9aW9YXP2+EgUpPI06STnh+Tgl+t+W8GPgh8lbTOc0jbsPj72IHUIeBxUg1q\ngybkcxbwH6TyXEBq3lmbFWX7LPCvpGaY17DigAzwRuAmSUtIzcYnRsT9uRb6DlKz8MOk8q1c9B/O\n8XmdHiGdLF7Miu31M9LB/E+kJqLnyE1Bubw3yPP/JOfzWFYt3+LwpqRayDOkGvOvgAvztCNIQeRu\nUlPdSUNsq1cwTNP3CLZDd10IzxtrbmF4T+DqTuerTp7PBU4vOe8hwDmF4SOBr3d6Hfrlj9Sb5GfA\nxwrj7gE2y583A+4ZYRqnke7BWd3vf5Z04jE3/1CfBS4iHWzH5HneBPyszdvuJuDoTpdhJ8u2wfyc\nAZzfq+U9kr+uqmlExCPAQ5Iqd+buTRc/ijk3gxxEvgZTwoPAbrmKK9L6dfpCf1/I2/Mc4K6IKPaD\nv4rURZT8/8p2560oIj4eEVtExADpLO+XEXEYqXfQwXm2ludT0l6SNs3NU0cBrwN+2so0V1c3lG2+\np+F1uXluF1It7Qf1vtct5d1UnY5aNSLzDqTuq7cBP6RG97Ju+AM+TWrq+LcGv/cpUtXyDlI1d+1O\nr0s//JHunYm839ya//YjXS+YReo5NQsYP8J0TmMENY2qZU1lRRfMl5OuD8whXdxt6X5BaiJ5lNQE\nfBuwf6fLsAvK9s78m67+O4zU3DWHVFOYS+pWrAaXXyzvk0jXOF4kNatV0rqz09u73p8fI2JmZqV1\nVfOUmZl1t1a8hKlhEyZMiIGBgaYvd+nSpYwd25keu51Ku2y6s2fPfjwi2vacrGaVcbdv125Kt9Nl\n3MnfXyN6JZ+wal7bXcZAd1zT2GmnnaIVrrvuupYst5vTLpsucHP0YBl3+3btpnQ7Xcad/P01olfy\nGbFqXttdxhFd1nvKzMy6W1c0T7XCwIyrmT5lGdNmXD3kPHNn7t/GHFmjBmqUXbFMXX7WLrX2xaLR\ntC+6pmFmZqU5aJiZWWkOGmZmVlrpaxr5iYs3k57Df4CkrUlvshpPemjfERHxfH7C4gWkt009ARwa\nQz9p0ayl3BZt1lyN1DTKvjTmGOCpiNiG9IrUMzAzs75QKmg0+NKY4ktvLgf2rnrZi5mZ9aiyzVOV\nl8asn4eHe2nMJPKz5CNimaRFef7HiwuUdCzpoWlMnDiRwcHB1VyF2qZPWcbEddP/oTQ7zaIlS5a0\ndPndlq6ZjQ51g0bxpTGSplZG15g1SkxbMSLibNKL39l5551j6tSp1bOMyLR8n8bnbx96Fece1tw0\niwYHB2n2OnVzumY2OpSpaewOvEvSfsA6pNcYfgnYUNKYXNsoviN5Hun9ufPy6zA3AJ5ses7NzKzt\n6l7TiMZfIlJ8McrBeX4/f93MrA+M5D6NU4CPSZpDumZReXvdOcDGefzH6PA7vq0+SZMlXSfpLkl3\nSjoxjx8v6VpJ9+b/G+XxkvRlSXMk3SZpx86ugZm1S0PPnoqIQWAwf74P2KXGPM+R3oVtvWMZMD0i\nbpG0PjBb0rXANGBWRMyUNIN0AnAKsC+wbf7bFTgr/zezPuc7wo2IWBARt+TPi0n340xi5e7T1d2q\nL8hPZ76RdH1rszZn2xrg2qQ1i4OGrUTSAPAG4CZgYkQsgBRYgE3ybMu7VWfFLtfWnSq1ye2A3YDj\nJG1Pqj3OyjfpzmJFc3KxNnksqTZp1r+PRrfGSRoHfB84KSKeGeaezFLdqkd6L06te2yK996UWd5w\n9+mUXQb0/n03OehXTgAWSyrWJqfm2c4nNT+fQqE2CdwoaUNJm1VOImz0ctAwACStRQoYF0XEFXn0\no5UDRW5+WpjHV7pVVxS7XC830ntxar0LpXjvTZn7bIZ7n0rZZUB/3XczXG1SUr3a5EpBY7gTg165\n0bRMPpt18jFS3bBNHTSs8liYc4C7IuILhUmV7tMzWbVb9fGSLiFdAF/kM9De0Oza5HAnBr1yo2mZ\nfDbr5GOkumGbOmgYpBs4jwBul3RrHncqKVhcJukY4EFW9Iq7BtgPmAM8Cxzd3uza6mhFbdJGHwcN\nIyJuoPaZJcDeNeYP4LiWZsqayrVJaxYHDbPRwbVJawoHDbNRwLVJa5aeDRr13shmZmbN17NBw8ys\nGW6fv6hu7yhbwUHDzGyEyrR89Mv76P0YETMzK81Bw8zMSnPQMDOz0hw0zMystLpBw8/hNzOzijI1\nDT+H38zMgBJdbv0cfutWvsHTrP0auk+jXc/hL6Pe8+1h5Rf21FIvzdvnL6qbxpRJG9Qc3+sv7TEz\nq6V00Gjnc/jLKHMHZ/GFPbXUewZ+mTSGWkYvvbRH0rnAAcDCiHhtHjceuBQYAOYC742Ip/LTUs8k\nPczuWWBa5f3iZtb/SgUNP4e/750HfBW4oDCucs1qpqQZefgUVr5mtSvpmtWubc2tNWw0nxjUa8ac\nPqVNGekTZXpP1XsOP6z6HP4jcy+q3fBz+LteRFwPPFk1+kDStSry/3cXxl8QyY3AhvmkwbrbecA+\nVePcmcUaVqam4efwj04jumYFrbluVe86VaO+ctGVw06vXLPq9WtUEXF9viZZ5M4s1rAyvaf8HH4r\nKnXNCoa/blWu59Oqu2e961TNVrlm1UvXqBrQ0hODbumUUe8ko9knIkNpxrbohm3qp9zaUHzNavRq\nyolBpwJttXodWtp1IlKv400Z3bBN/RgRG4qvWfW/RyvXo3xiYGW5pmFIupjUtj1B0jzgk/ia1WhQ\nOTGYyaonBsdLuoTUM65rTwx8g2f7OWgYEfH+ISb5mlWf8ImBNYuDhtkI1Tvb7YY3tvnEwJrFQcNs\nGJWAMH3KMr9H2gwHDTPrYv10zaIXaqRluPeUmZmV5qBhZmalOWiYmVlpDhpmZlaag4aZmZXmoGFm\nZqU5aJiZWWm+T8PMWuL2+Yvq3hDZK/cm2AquaZiZWWktqWlI2of0juE1gW9FxMxWpNMPytzx2o1n\nYy7j/teOMu6nO75Hi6YHDUlrAl8D3k56Lv/vJF0VEX9sdlrWGS7jxvTiiYHLuP3K7Cfn7TO2DTkZ\nXitqGrsAcyLiPoD8TP4Dgb5j+56jAAANeUlEQVTc2YYq6MoD7rrtYNAko6qMRymXsdXUiqBR6/3C\nu1bPVHy3MLBE0j3NzsgJMAF4fKjpOqPZKa6adjPSaHAZw65zwVarlZmkY2Vcr0xbpdXpDlPGI0m3\n02XckbJqVKf2qdXxljNWyetIyni1tCJolHq/cPHdwq0i6eaI2LmVaXRb2m1Kt2Nl3OfbtWvSpQll\n3MnfXyN6JZ/QHXltRe8pv1+4/7mM+5/L2GpqRdD4HbCtpK0lvQR4H+mdw20haa6kt7UrvVGqo2Vs\nbeEytpqa3jwVEcskHQ/8jNRV79yIuLPZ6ZTU0uavLk275el2uIz7drt2U7pNKuNO/v4a0Sv5hC7I\nq9LrgPuHpLnAByLiF53Oi5lZv+nXO8J3kHSbpEWSLpW0jqRfSfoHAEl7SApJ++Xht0m6VdLakp6U\nNKWyIEmbSPqLpJcNlZikqZLmSTpZ0kJJCyS9W9J+kv6Ul3lqYf7TJF2e87ZY0i2SXl+YvqOk3+dp\n38vznd6aTWVmVl6/Bo33AvsAWwOvA6YBvwKm5ulvBu4D9ioM/yoi/gpcAhxeWNb7gV9ExGN10twU\nWIfUVfETwDfzcnYC9gQ+IenlhfkPBL4HjAe+C/xQ0lq5/fgHwHl52sXAe0qvuZlZC/Vr0Pgy8L+k\nQDEe+M/8uRgkPlsY3itPBzgf+D+SKtvmCOA7QyUk6VzSQX4N4DMR8TdSO/AEYA/gClKvkztJAaxi\ndkRcnuf/Aing7Jb/xgBfjoi/RcQVwG+HSjvXbO4ojDtN0vxcc7q1UpvqJZImS7pO0l2S7pR0Yo15\npuaaZGU9P9GktOdKuj0v8+Ya0yXpy5Lm5Nrsjk1I81WF9bhV0jOSTqqapyXr2yqS9pF0T95OMzqd\nn6HUK+9OGeK3PV7StZLuzf836kTe+jVoPJL/vwU4C/g58BvglZImAjsAFwCTJU0g3f16PUBE3AQs\nBfaS9GpgG4bvNXIecDLwQkS8kMf9U/7/ZmAWMAP4CzCu8L3lN05FxIukLo6b57/5sfLFpuJNVtVp\n71Nj/BcjYof8d80wee9Wy4DpEbEdKYgeJ2n7GvP9urCe/9nE9N+Sl1mrP/y+wLb571jS/jUiEXFP\nZT1INdNnSSci1Vq1vk2lFY8g2RfYHnj/EOXXLYYr7045j1V/2zOAWRGxLSuOK23Xr0FjFRHxLDAb\nOBG4IyKeJ9VGPgb8OSKKd1meT2paOgK4PCKeG2a51wOLq0a/q2pZ767x1eV94HOtptIPfgEwSZJq\nzVsj7SeHyluviogFEXFL/rwYuIvU7NcNDgQuiORGYENJmzVx+XuT9scHmrjMdlv+CJL8O6s8gsRK\nGuK3fSDpeAJDH1darp+DRpBqGMeSaguQmqCOZ0VT1GDVcMV3SNcRDifVSBo1cXkmIhYAm9SYZydJ\nB0kaA5wE/BW4kVQjegE4XtIYSQeSfoSNOD43nZzbqSpss0gaAN4A3FRj8psk/UHSTyS9pklJBvBz\nSbOVHpFRrdbjNZoZ0N5Huo5VSyvWtxVavY2aqV55d5OJ+Xgy3HGl5fo5aOweETsCFwGvkvRmUnBY\nn9wUVWMYgIiYB9xC2qF+3aL8XQkcCjxFqtEclK9hPA8cBBwDPE0KXD8mBZUyzgJeQWqCWwB8vsn5\nbhtJ44DvAydFxDNVk28BtoqI1wNfAX7YpGQr+82+pGaxN1dnq8Z3mtJvPXeCeBepg0S1Vq1vK7Rs\nG7VAvfK2Kn335r6IGKga/ldJS4FdIuJzFHboiLiD2js4wIPAjVXXFoZyI1B8UNujwHYRsSA3XSyM\niD2qvvNcRBxODRFxM+mgD4Ckm4AflcgHEfFo4XvfJAWcniNpLVLAuCh3BlhJMYhExDWSvi5pQlUz\nY8Mi4uH8f6GkH1C43pW18vEa+wK3FMuwkK+WrG+L9MwjSEqUdzd5VNJmxeNKJzLRlzUNSWMlrV/5\nDLwDuGP4b630/QHS2f45q5mFq4Cj8uejSLWK0iTtJWnT3Dx1FKnX1U9LfrfYvv4eGljvbpGv55wD\n3BURXxhink0r130k7ULal58YYbpl9purgCNzL6rdgEWVJoMmeD9DNE21Yn1bqCceQTLS40QHjOi4\n0ix9V9PIJgI/yL+xMcB3I6LsQffTwD8Dn42I+wvjTwVOrfGVZ0i1lQmS5gGfBGYCl0k6hlRjOaTB\n/L8KuIzU2+rPwMG1DkySLibde1JMe6qkHUjNAXOBDzWYdjfYndRkd7ukW/O4U4EtASLiG8DBwEck\nLSP1THtfyVrhcGruN5I+XEj3GmA/YA6pl9PRI0wTAEnrkV549KHCuGK6rVjfluiyRwkNZ7WPE602\nxG97pMeV5uStS/c7MzPrQn3ZPGVmZq3RFc1TEyZMiIGBgZYse+nSpYwd2/n36jZTM9Zp9uzZj0fE\nkM/TMjOrpSuCxsDAADff3Jo7+AcHB5k6dWpLlt0pzVgnSb1885iZdYibp8zMrLSuqGl0ysCMq4ed\nPnfm/m3KiZlZb3BNw8zMSnPQMDOz0hw0zMysNAcNMzMrzUHDzMxKq9t7StJk0jslNgVeBM6OiDMl\njQcuBQZIzzh6b0Q8lR+qdibp+TzPAtMqL9TpNfV6V4F7WJnZ6FKmpjHUqzeHevVg01+HaWZm3aFu\n0Bjm1ZtDvXqw1a/DNDOzDmno5r6qV2+u9OpBSZVXDw71qseVHu2dX614LMDEiRMZHBxsPPclLFmy\nZMhlT5+ybMTLb1W+hzPcOpmZtVLpoFH96s38DPqas9YYt8rz1yPibOBsgJ133jla9Xyo4Z7TNK3E\nNYt65h5We9mt1I/P0zKz3lCq99QQr958tNLsVPXqwZ551aOZmTWmbtAY5tWbQ716sJWvwzQzsw4q\n0zw11Ks3h3r1YEteh2lmZp1XN2hExA3Uvk4BsHeN+QM4boT5MjOzLuQ7ws3MrDQHDTMzK61nX8Lk\nFyiZmbWfaxpmZlaag4aZmZXWs81T9VSar6ZPWdaUO7/NzMw1DTMza4CDhpmZleagYWZmpfXtNY12\ncddfMxtNXNMwM7PSHDTMzKw0Bw0zMyvNQcPMzEpz0DAzs9LKvLnvXEkLJd1RGDde0rWS7s3/N8rj\nJenLkuZIuk3Sjq3MvJmZtVeZmsZ5wD5V42YAsyJiW2BWHgbYF9g2/x0LnNWcbJqZWTeoGzQi4nrg\nyarRBwLn58/nA+8ujL8gkhuBDSVt1qzMmplZZ63uzX0TI2IBQEQskLRJHj8JeKgw37w8bkH1AiQd\nS6qNMHHiRAYHBxvKwPQpy8pldN3y87ZCo+tVxpIlS1qyXDOzepp9R3itd4lHrRkj4mzgbICdd945\npk6d2lBCZZ9cO33KMj5/e+dufJ972NSmL3NwcJBGt5eZWTOsbu+pRyvNTvn/wjx+HjC5MN8WwMOr\nnz0zM+smqxs0rgKOyp+PAq4sjD8y96LaDVhUacYyM7PeV7fdRtLFwFRggqR5wCeBmcBlko4BHgQO\nybNfA+wHzAGeBY5uQZ7NzKxD6gaNiHj/EJP2rjFvAMeNNFNmZtadfEe4mZmV5qBhZmalOWiYmVlp\nDhpmZlaag4aZmZXmoGFmZqU5aJiZWWkOGmZmVlrnnuQ3jIGSDyM0M7P2ck3DzMxKc9AwM7PSHDTM\nzKy0rrym0U/qXZ+ZO3P/NuXEzGzkXNMwM7PSHDTMzKw0Bw0zMyutJUFD0j6S7pE0R9KMVqRhZmbt\n1/SgIWlN4GvAvsD2wPslbd/sdMzMrP1a0XtqF2BORNwHIOkS4EDgjy1Iq+eVufu9Xg+rZizDzKyM\nVgSNScBDheF5wK7VM0k6Fjg2Dy6RdE8L8sIJMAF4vBXLbhedscqohtepxjK2Wu0Mmdmo1YqgoRrj\nYpUREWcDZ7cg/ZUzI90cETu3Op126sd1MrPe0IoL4fOAyYXhLYCHW5COmZm1WSuCxu+AbSVtLekl\nwPuAq1qQjpmZtVnTm6ciYpmk44GfAWsC50bEnc1OpwEtbwLrgH5cJzPrAYpY5XKDmZlZTb4j3MzM\nSnPQMDOz0vomaEiaLOk6SXdJulPSiXn8eEnXSro3/9+o03ltlKQ1Jf1e0o/z8NaSbsrrdGnucGBm\n1nJ9EzSAZcD0iNgO2A04Lj++ZAYwKyK2BWbl4V5zInBXYfgM4It5nZ4CjulIrsxs1OmboBERCyLi\nlvx5MekgO4n0CJPz82znA+/uTA5Xj6QtgP2Bb+VhAW8FLs+z9Nw6mVnv6pugUSRpAHgDcBMwMSIW\nQAoswCady9lq+RJwMvBiHt4YeDoiluXheaTgaGbWcn0XNCSNA74PnBQRz3Q6PyMh6QBgYUTMLo6u\nMav7TZtZW/TVO8IlrUUKGBdFxBV59KOSNouIBZI2AxZ2LocN2x14l6T9gHWAl5JqHhtKGpNrG35M\ni5m1Td/UNHJb/znAXRHxhcKkq4Cj8uejgCvbnbfVFREfj4gtImKA9DiWX0bEYcB1wMF5tp5aJzPr\nbX1zR7ikPYBfA7ezov3/VNJ1jcuALYEHgUMi4smOZHIEJE0F/iUiDpD0cuASYDzwe+DwiPhrJ/Nn\nZqND3wQNMzNrvb5pnjIzs9Zz0DAzs9IcNMzMrDQHDTMzK81Bw8zMSnPQMDOz0hw0zMystP8PfW2H\niVs7GvYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6584f93198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6584eab588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584c46f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584bfbef0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6584bb5ef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584b71ef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584b71f28>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6584adda90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584a95f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6584a5b4e0>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BsyM/dsDGtp6ae8rMBjXYA8qa/cAn6zHONMzGgBj8AWXNfuCT9ZhhVU+ZWffT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6584f93320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 987 entries, 0 to 986\n",
      "Data columns (total 13 columns):\n",
      "model                   987 non-null object\n",
      "displ                   987 non-null float64\n",
      "cyl                     987 non-null int64\n",
      "trans                   987 non-null object\n",
      "drive                   987 non-null object\n",
      "fuel                    987 non-null object\n",
      "veh_class               987 non-null object\n",
      "air_pollution_score     987 non-null float64\n",
      "city_mpg                987 non-null float64\n",
      "hwy_mpg                 987 non-null float64\n",
      "cmb_mpg                 987 non-null float64\n",
      "greenhouse_gas_score    987 non-null int64\n",
      "smartway                987 non-null object\n",
      "dtypes: float64(5), int64(2), object(6)\n",
      "memory usage: 100.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_08.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65849aae10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6584a0b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08['greenhouse_gas_score'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f658498ed68>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZuSEzh5n5I9s/Z+Y5e8aWmf8JvB4RlzRN1wDfbbGktr0GXBUR72/eN9dwDv9xepZ9wPZm\nezvwRLcH8D8H79zVwK3A4Yh4vmn7XHNlsHQX8FBzz6dXgdtbrqc1mflsRDwKPMfMp9W+wzl2lWxE\nPAyMAmsjYhK4B9gFPBIRdzDzj+GNXR/XK2MlqTaXbiSpOINekooz6CWpOINekooz6CWpOINekooz\n6CWpOINekor7f3Kyn8YMyFaPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f658478f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18['greenhouse_gas_score'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6584717668>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WVtPWAg+2+hqSpPa1czg0DHwrIo4/zz9n5r9GxPeArRFxM/AccG37MSVJrWq56DPzx8A7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\n1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65846f70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_08['cmb_mpg'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65846d0fd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6584625080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18['cmb_mpg'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>displ</th>\n",
       "      <th>cyl</th>\n",
       "      <th>air_pollution_score</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>hwy_mpg</th>\n",
       "      <th>cmb_mpg</th>\n",
       "      <th>greenhouse_gas_score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>displ</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.296241</td>\n",
       "      <td>-0.790440</td>\n",
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       "      <td>-0.818799</td>\n",
       "      <td>-0.847613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl</th>\n",
       "      <td>0.899304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.274657</td>\n",
       "      <td>-0.756304</td>\n",
       "      <td>-0.733064</td>\n",
       "      <td>-0.770538</td>\n",
       "      <td>-0.820940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_pollution_score</th>\n",
       "      <td>-0.296241</td>\n",
       "      <td>-0.274657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434884</td>\n",
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       "      <td>0.428317</td>\n",
       "      <td>0.394814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>-0.790440</td>\n",
       "      <td>-0.756304</td>\n",
       "      <td>0.434884</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.980891</td>\n",
       "      <td>0.910693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hwy_mpg</th>\n",
       "      <td>-0.801678</td>\n",
       "      <td>-0.733064</td>\n",
       "      <td>0.402069</td>\n",
       "      <td>0.902942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.960533</td>\n",
       "      <td>0.931744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cmb_mpg</th>\n",
       "      <td>-0.818799</td>\n",
       "      <td>-0.770538</td>\n",
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       "      <td>0.980891</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.947607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <td>-0.847613</td>\n",
       "      <td>-0.820940</td>\n",
       "      <td>0.394814</td>\n",
       "      <td>0.910693</td>\n",
       "      <td>0.931744</td>\n",
       "      <td>0.947607</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         displ       cyl  air_pollution_score  city_mpg  \\\n",
       "displ                 1.000000  0.899304            -0.296241 -0.790440   \n",
       "cyl                   0.899304  1.000000            -0.274657 -0.756304   \n",
       "air_pollution_score  -0.296241 -0.274657             1.000000  0.434884   \n",
       "city_mpg             -0.790440 -0.756304             0.434884  1.000000   \n",
       "hwy_mpg              -0.801678 -0.733064             0.402069  0.902942   \n",
       "cmb_mpg              -0.818799 -0.770538             0.428317  0.980891   \n",
       "greenhouse_gas_score -0.847613 -0.820940             0.394814  0.910693   \n",
       "\n",
       "                       hwy_mpg   cmb_mpg  greenhouse_gas_score  \n",
       "displ                -0.801678 -0.818799             -0.847613  \n",
       "cyl                  -0.733064 -0.770538             -0.820940  \n",
       "air_pollution_score   0.402069  0.428317              0.394814  \n",
       "city_mpg              0.902942  0.980891              0.910693  \n",
       "hwy_mpg               1.000000  0.960533              0.931744  \n",
       "cmb_mpg               0.960533  1.000000              0.947607  \n",
       "greenhouse_gas_score  0.931744  0.947607              1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cyl</th>\n",
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       "      <th>city_mpg</th>\n",
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       "      <td>0.992635</td>\n",
       "      <td>0.978206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.814982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>greenhouse_gas_score</th>\n",
       "      <td>-0.754193</td>\n",
       "      <td>-0.743946</td>\n",
       "      <td>0.390771</td>\n",
       "      <td>0.785513</td>\n",
       "      <td>0.826981</td>\n",
       "      <td>0.814982</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         displ       cyl  air_pollution_score  city_mpg  \\\n",
       "displ                 1.000000  0.928189            -0.384986 -0.536748   \n",
       "cyl                   0.928189  1.000000            -0.404966 -0.524090   \n",
       "air_pollution_score  -0.384986 -0.404966             1.000000  0.288700   \n",
       "city_mpg             -0.536748 -0.524090             0.288700  1.000000   \n",
       "hwy_mpg              -0.605202 -0.589986             0.309338  0.950309   \n",
       "cmb_mpg              -0.574880 -0.561166             0.301150  0.992635   \n",
       "greenhouse_gas_score -0.754193 -0.743946             0.390771  0.785513   \n",
       "\n",
       "                       hwy_mpg   cmb_mpg  greenhouse_gas_score  \n",
       "displ                -0.605202 -0.574880             -0.754193  \n",
       "cyl                  -0.589986 -0.561166             -0.743946  \n",
       "air_pollution_score   0.309338  0.301150              0.390771  \n",
       "city_mpg              0.950309  0.992635              0.785513  \n",
       "hwy_mpg               1.000000  0.978206              0.826981  \n",
       "cmb_mpg               0.978206  1.000000              0.814982  \n",
       "greenhouse_gas_score  0.826981  0.814982              1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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