{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read it in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>663</td>\n",
       "      <td>54.863</td>\n",
       "      <td>22856302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>4195</td>\n",
       "      <td>74.200</td>\n",
       "      <td>3071856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>5098</td>\n",
       "      <td>68.963</td>\n",
       "      <td>30533827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2446</td>\n",
       "      <td>45.234</td>\n",
       "      <td>13926373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>N. America</td>\n",
       "      <td>12738</td>\n",
       "      <td>73.544</td>\n",
       "      <td>77656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Country   Continent  GDP_per_capita  life_expectancy  \\\n",
       "0          Afghanistan        Asia             663           54.863   \n",
       "1              Albania      Europe            4195           74.200   \n",
       "2              Algeria      Africa            5098           68.963   \n",
       "3               Angola      Africa            2446           45.234   \n",
       "4  Antigua and Barbuda  N. America           12738           73.544   \n",
       "\n",
       "   Population  \n",
       "0    22856302  \n",
       "1     3071856  \n",
       "2    30533827  \n",
       "3    13926373  \n",
       "4       77656  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"country-gdp-2014.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change the background color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the background color with `ax.set_axis_bgcolor`, but it will only change the area **inside of the plot**. This is useful when you have multiple plots in the same figure (a.k.a. multiple charts in the same image) but most of the time is just a headache."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BfX8E/A3ws8CfAA+E3WlLSqPARUTKJ5/qqXXAhe4+CfynmX2LoE3j60WJLMve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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1091be0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(kind='scatter', x='GDP_per_capita', y='life_expectancy', color='white', alpha=0.5, linewidth=0)\n",
    "ax.set_ylim((0, 85))\n",
    "ax.set_xlim((0, 70000))\n",
    "# Specify background color for the axis/plot\n",
    "ax.set_axis_bgcolor(\"lightslategray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actually, really change all of the background color\n",
    "\n",
    "In order to change the background color of *everything*, you need to create a figure and set the `facecolor`. On top of that, **you need to specify the facecolor when you save**, or else it will show up as white/transparent in Adobe Illustrator even though it looks right in the Python world."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Lce+0iQjs3izJGcvYhOOmTK6SAQeziYjE86uk8fyKR3pNGMDNtaUEABO77546VlLuMhlw\nMJuISDy/ShohOtf7iOubW3CooMR699O+/BPYx2RARORRA5I02ttasfODt3D96mUIgoB5jy5HWEQk\ntm5ah8a6WuhCw7Fw2Soogpzv6W1R36jvsfy5xaGCErYYiIgkNiAD4Xs+2YwxSal45oe/xornfoHw\nyGHIP7ALoxKS8cyPfoO4+CTk7XO9qKDFX9ZtwY3aehhN9oPaN2rrrZP7BBfvJSKiWyd5S6O9rRUX\nK8vxwGNPAwBkcjkUciXKTxZh6aofAQBSJ0/DprUvI3vuol7PZQTwp3Vb7JYzt+yHEREWguSEOAiC\nALPZzJVniYgkIHnSaKi9AaVagx3vbcD1KxcxbGQc7nvwcTTr9VBruzZO0miD0dKkF39Sm5nhITqN\n3VMTxyVYZ4L722Q9LrVORL5O8qRhMplw7VIVZi94AtEjR2Hvti3I37/LcUWQHkuE2Dq4e5v159gx\nY/HUssddbteqUgbZPfanyXqcnU5E/VVVUYqqs2WSX0fypKENDoU2JAzRI0cBAJLGT0L+/l1QaXRo\n1jdCrdWhSd8Aldr1nVEzZs+3e+yYCKpr6lBT19hj61bAvybrcXY6EfVXbHwSYuOTrI8P7d0uyXUk\nHwhXa3XQBYei9npX4V155hQioqKRmDIBxYW5AICSwjwkpqT1eS5BEJCdmY7w0J77gZ+/dK3HrG9X\n6035Ks5OJyJfNyC33M6a/zi2bX4DJqMRIWERmLf4aZhMJmzduBbFBbnQhYZhwdJVfZ7Hcee+ppZW\naFRKRIaHIidrKsrOXbB7/bGScr8aE+DsdCLydQOSNCKHx2D593/W4/iSlavdOo9jd01QYKDdY6PR\nNOBbvnoSZ6cTka/zqxnhcrl9b1pAgLzH8yx0iYik41er3LpaqVbs80REdGv8Kmk4tjTcfZ6IiG6N\nX5WybGkQEXmXX4xpWGZKO95qe6O23m4BQylvUeVsbSIiN5LGR2+/jgl3zsCYseMgyAa2geJ4q211\nTR2OlZTji/wTmNm9BpXUd0txtjYRkRtJY2JmNooLc7Hnk3eRNH4yxk+5C+FDh0kZm5WzmdFxI6Iw\nMzNdVI3fE60EztYmInIjaYxKTMaoxGS0t7Xi5Ikj2PyPv0IbHIq0qTMwbmIm5HJ53yfpJ8dzR4aH\nIjI8VHSN3xOtBMctZDlbm4huR26NabQ2N+Hr44fx9fF8RA2PQUr6VFysPIOSwnws/fYPpYoRITq1\ny+fE1Pg90UrgbG0iIjeSxodvvY7aG9cwbmIGHln+fWh0wQCA5LQ78earv5MswL6IqfF7opXA2dpE\nRG4kjcnT70WczQqKtpb/v//yWEDOHCsptxsIv1Fbj+u1DaJr/GwlEBF5huikUVN9GVHDYxCkVAEA\n2lqacbLoKCZNy5YqNqsD+ScwJjbauux5RFgICorLRNf82UogIvIM0ffOFh05aE0YABCkUqPoyEFJ\ngnJkRs+Je9OnpHJPcCKiASa6pWEymWA2myF077BnMplgNHZKFpgtZ3toaNUqa5eVs1YEJ+MREXme\n6KQxJmkctm5ch/SMuwEAJw5/iTFjx0kWmC3b8Yx2gwEKmyXRp09JBYAeSYGT8YiIPE900sjOWYQT\nR77E8fwvAACjE5Mx4c4ZkgXmSkenEQqbbTRctTg4GY+IyPNEJw1BJsPEzCxMzMySMp4+BQUOQdm5\nC4iODIdWfXOMxTEpcDIeEZHniU4aFyvP4OCe7Wisr4HJZALMZkAQ8J0X+p6j8fpLP4MiSAlBECCX\ny/HU93+KtpZmbN20Do11tdCFhmPhslVQBCn7DjggAGNHx6Ds3AVoR99MGo5JgbfZEhF5nuiksfOD\nt3Dvg49h2IhYyAT3FiwUZAKWrvoBglQ3Z3bnH9iFUQnJyMiag/z9u5C3byey5y5y+v7qmjpEhofa\nHRseFYHPDhxBrIuk4O3bbDkQT0SDkejSXxGkRHxSKtQaHZRqjfU/UcxmmM32RWb5ySKkTpoGAEid\nPA3lJ10X8PWNTT2OaVRKmAG8+cFn2O+DBbJlID45IQ45WVORlZnu7ZCIiG6Z6JZG7Jgk7Pv0fYwd\nNxHygCHW48NGxPb9ZkHA5jf+CkEmQ/rUmUibOgPNej3U2q7baDXaYLQ06V2+3dXmSr48uM2BeCIa\njEQnjSsXzgEArl48b3NUwBOrftDne5/8zgvQ6LoSw+Y3XkHY0CgIjrPyehy4yXFQ2/a4r+JAPBEN\nRqKTxhOr+r+KrWVxQ5VGi8RxabhyoRIqjQ7N+kaotTo06RugUmtdvv+3//3f2LtjHNSqIGRnZyM7\nOxvVNXX44nARsh02YfKVbioOxBPRQKqqKEXV2TLJr+PW0ugVp4tx49pldHZ0WI9Nn/Vgr+/pMBhg\nNpsQqAiCwdCOyvJTmH7fg0hMmYDiwlxkZuegpDAPiSlpLs8xY/Z8rHhsrnXtKaBrnGOmyAl83hiU\n9vZAPBHdXmLjkxBrs6jsob3bJbmO6KTx2UfvoMNgQNXZUky4czpKi48hOmZ0n+9rbmrER2+/DkCA\nyWTEuIkZGD02BcNGxmHrxrUoLsiFLjQMC5aucnmO7Mx0REeG2x0zGk2ixw04O5yIyDNEJ41L5yuw\n4vlf4p9//Q1mzJqPqXfPxnvrX+3zfSFhEfjmc7/ocVypUmPJytWirm27jIiFZYxAzLgBB6WJiDxD\ndNIIGDKk+/+B0DfWQ6lSo0nfIFlgfRFws7XQ17iBq0FpzqUgInKP6KQRf8cEtLW2IGPm/Xjzb78D\nBHhl7SmLu6akwoyeCxU642pQmt1WRETuEZ00MrLuR0DAECSNn4T45PEwdnZCHuDWOLpHWRYqnJSa\niGMl5b0mD1eD0uy2IiJyj+gZ4f967Q/WnwMChkARpLQ75i2R4aHIyZqK7H7MuHYcA+FcCiKi3vXZ\nVGjSN6CpoR4dHR24dqnKWps3tLWio8MgcXj2TCYTZDLneW5iaiL2udm1xLkURETu6TNpnCs7iZLC\nXDQ11OHzHe8D3WkjUKHEzDkLpY7PjquE0V+cS0FE5J4+k8b4ydMwfvI0lBYfQ9L4SQMRU5/0zS24\nUl1jN9nv+NdnfHZ2OBHRYCF6JPvqpfOIS7gDQcquPSzaWppx5MvdA9LacFwa/VBBCQ7kn0CWTZIA\nwDuhiIgkJjppnC39Glk5D1sfB6nUOFtaMiBJ4y//eM8uQVhaEbZJYfkjc+zewzuhiIg8T/Qggdls\nQmfnzTWnOjoM6OzslCSoHnpZAdeCd0IREUlPdEsjJX0q3l33F4yfchcAoLggF+MnT5MsMFuOk/DG\nxEZj/ZaddmMWvBOKiEh6opNGZnYOIqNHovLMaQDAXfc9gDFjx0kWmC3Hrqaxo2OQlZlu1z3FO6GI\niKTn1pTu8MhoyGRyjEpMRofBgPb2NigUQVLFZuVsEyaOWRARDTzRYxonjnyJj99Zg88++hcAQN9Y\nh4/eek2ywGwdyD+BsnMX7I5xzIKIaOCJThrH8/bjye+8gECFEgAQFhGF5l729fYkM4D1W3Zi14Ej\nOHXmPHYdOMIxCyIiLxCdNOQBAXYLFJqMRggi7mryFDOAA4eLcP7SNcSNiEJWZjoG7upERAS4MaYR\nM3os8vZ9is5OA86Vn8TxvANISJ4gZWw9cClzIiLvEp00snMeRlHBIQwdNgInDn+JMXekIs2N/TTM\nJhPe/PuL0AaH4pHl30NbSzO2blqHxrpa6ELDsXDZKiiClL2eg0uZExF5l+ikIchkGD9pGobHjIYg\nCAiLiHKre6rg0OcIj4yGob0NAJB/YBdGJSQjI2sO8vfvQt6+ncieu6jXc7jagY+IiAaG6KRRcboY\nn330DkLChgIwo772BuYsehLxSal9vrexoQ4VpSW46565OHpwDwCg/GQRlq76EQAgdfI0bFr7cp9J\ngxP4iIi8S3TS+HzHe3jimR8gNCISAFBXcx3vb3hVVNL4fPsW3DPvEbS3tVqPNev1UGt1AACNNhgt\nIu7E4gQ+IiLvEn33VKAiyJowACAkLAKBIib2VZwuhlqjQ9TwGMDcy2LlA3gnFhER9Y/olsawEXF4\nb/2ruGP8ZEAQUFpciOiRo1BacgwAkJTqfK+Ni5VnUH6yCBWlJejsMMDQ3o7tm/8JtVaHZn0j1Fod\nmvQNUKm1Lq99cPc268+xY8YiNj5JbNhERLeFqopSVJ0tk/w6opNGZ2cHVBotqs51BaVUa9DRYcCZ\nU19BgOAyaWTlPGxdUr3qbBmOfrkbDz6+Avs+/QDFhbnIzM5BSWEeElPSXF57xuz57nwmIqLbTmx8\nkl2F+tDe7ZJcR3TSeGDx0z2OGTs77Sb8uSMzOwdbN65FcUEudKFhWLB0Vb/OQ0REA0d0ib9xzct4\nYPFyBIdFAACuXKjEzg/exornfyH6YrFjxiJ2zFgAgFKlxpKVq0W9j9u4EhH5BvFLo9+Tgy3r/4bJ\nd92LpsZ6VJwuwbzFy6WMzYqzwImIfIPopDFm7DgELFyGzW/8FUq1Bk8/+3NotMFSxuYUZ4ETEXmP\n6KRxaO8OnP6qAEu//SNcv3oJm9a+jHsfWIz4O8ZLGV8PnAVOROQ9opNGa0sTnvr+TzFkSCBGxMVj\n9Nhx2PnB2wOSNHYdOMJZ4EREPkB00pg1/3EAQIfBgCGBgQgODceSlc9LFpgtjmEQEfkG0TPCL52v\nwD/+/Cuse/mXAIDqyxfw7483ShYYERH5HtFJY+/2LXhsxbNQqjQAgMjhMbhwrlyywIiIyPeIThoA\noAsJs3ssCG69nYiI/JzoMQ1tcBgunq+AIABGoxEFh/YiPHKYlLEREZGPEd1UmPPwMhzP2w99Yz1e\ne/HHqL58AfcveELK2IiIyMeIbmmo1BrMX/Itl8/n7duJaffM9UhQRETkmzw2KFFaXOipUxERkY/y\nWNLobX8lIiIaHDyWNLjxHhHR4MeWBhERieaxpHHHBOc79xER0eAhOmnUXr+Gd9f9GW/85dcAgOor\nF5G7d4f1+Wn3zPN8dERE5FNEJ41dH76NmTkPQyaTAwAio0fi1FcFkgVGRES+R/Q8jY4OA4bHjLY7\nJpP1nXM6Ozuwcc2fYOw0wmjsRGJKGrJyHkZbSzO2blqHxrpa6ELDsXDZKiiClO5/AiIiGjCik4ZS\npUFdzXXrXVKniwuhFrFzX0DAEDzxzA8xJDAQJpMJ/3r9j7hYeQZnThVhVEIyMrLmIH//LuTt24ns\nuYv6/UGIiEh6orun7l/wBD776F+ouX4V//vij1FwcC/mPLxM1HuHBAYCAIydnYDZjCClCuUni5A6\naRoAIHXyNJSf5J4ZRES+rs+WRsHBvZgy4z406RuwZOVqGAztMJvNUCiCRF/EbDJhw6svor72OtIz\nZiIiajia9XqotToAgEYbjJYmff8/BRERDYg+k0ZxYS6mzLgPez55F08/+3MEBircvoggk+Gbz/0c\n7W2t2PLPV1BVUdpzMmAvswMP7t5m/Tl2zFjExie5HQMR0WBWVVGKqrNlkl+nz6QRHhmNtf/zCzQ1\n1uOff/2N9bjZbIYgCFjx/C9FX0wRpMSYpPG4cuk8VBodmvWNUGt1aNI3QKXWunzfjNnzRV+DiOh2\nFBufZFehPrR3uyTX6TNpPPTESjTpG7Dljb/hkeXfdfsCLc1NkMvlUAQp0dFhQGX5SUyf9SASUyag\nuDAXmdk5KCnMQ2JKWr8+ABERDRxRd09ptMFY8fwv+nWBZn0DdmxZD7O5q3UyblIGRiUkI2p4LLZu\nXIviglzoQsOwYOmqfp2fiIgGTp9J4+N31mLhslV44y+/hmAz7iC2e2rosBF4+tmf9ziuVKmxZOXq\nfoRMRETe0mfSmDX/cQDAo09/X/JgiIjIt/WZNDS6rgl8waHhkgdDRES+rc+k8ZdfPuv8dlizGRAE\nrP71K1LERUREPqjPpLH6N38biDiIiMgPeGw/DSIiGvyYNIiISDQmDSIiEo1Jg4iIRGPSICIi0Zg0\niIhINCYNIiISjUmDiIhEY9IgIiLRmDSIiEg0Jg0iIhKNSYOIiERj0iAiItFEbfd6Kxob6rBj83o0\nNzVCEASkTb0bU6bfi7aWZmzdtA6NdbXQhYZj4bJVUAQppQ6HiIhugeRJQyaT4d4HFyNqeAwM7W14\n89UXMTpY+c31AAAQb0lEQVQxBcWFhzAqIRkZWXOQv38X8vbtRPbcRVKHQ0REt0Dy7imNNhhRw2MA\nAIGKIIRHDoO+oQ7lJ4uQOmkaACB18jSUnzwhdShERHSLBnRMo6H2Bq5dvojhsaPRrNdDrdUB6Eos\nLU36gQyFiIj6QfLuKQtDexs+fmcNZs1/DIGKoJ47yDrbUrbbwd3brD/HjhmL2PgkiaIkIvJPVRWl\nqDpbJvl1BiRpmIxGfPzOGoybmInEcekAAJVGh2Z9I9RaHZr0DVCptS7fP2P2/IEIk4jIb8XGJ9lV\nqA/t3S7JdQake+rT999CeGQ0psy4z3osMWUCigtzAQAlhXlITEkbiFCIiOgWSN7SuFh5BidPHMbQ\nYSOw/pXfQhCAmXMWIiMrB1s3rkVxQS50oWFYsHSV1KEQEdEtkjxpjByVgBd+/39On1uycrXUlyci\nIg/ijHAiIhKNSYOIiERj0iAiItGYNIiISDQmDSIiEo1Jg4iIRGPSICIi0Zg0iIhINCYNIiISjUmD\niIhEY9IgIiLRmDSIiEg0Jg0iIhKNSYOIiERj0iAiItGYNIiISDQmDSIiEk3ynfs+ff8tVJz+CmqN\nDiue/yUAoK2lGVs3rUNjXS10oeFYuGwVFEFKqUMhIqJbJHlLY8KUu/DYiufsjuUf2IVRCcl45ke/\nQVx8EvL27ZQ6DCIi8gDJk8bIUQkIUqrsjpWfLELqpGkAgNTJ01B+8oTUYRARkQd4ZUyjpUkPtVYH\nANBog9HSpPdGGERE5CbfGAgXBG9HQEREIkg+EO6MSqNDs74Raq0OTfoGqNTaXl9/cPc268+xY8Yi\nNj5J6hCJiPxKVUUpqs6WSX6dgUkaZjPMZrP1YWLKBBQX5iIzOwclhXlITEnr9e0zZs+XOkIiIr8W\nG59kV6E+tHe7JNeRPGl8sukfuHC2DK0tzXjt9z/BjNnzkZmVg483rkVxQS50oWFYsHSV1GEQEZEH\nSJ40HnpipdPjS1aulvrSRETkYb4xEE5ERH6BSYOIiERj0iAiItGYNIiISDQmDSIiEo1Jg4iIRGPS\nICIi0Zg0iIhINCYNIiISjUmDiIhEY9IgIiLRmDSIiEg0Jg0iIhKNSYOIiERj0iAiItGYNIiISDQm\nDSIiEm1g9gh34WxpCfZu3wKz2YwJU6YjMzvHm+EQEVEfvNbSMJtM2P3Ju3hsxXP41upf4VTRUdRU\nX/VWOJKpqij1dgi3xJ/j9+fYAcbvbf4ev1S8ljQuX6xEaHgkgkPDIZfLkZx2J8pPnvBWOJKpOlvm\n7RBuiT/H78+xA4zf2/w9fql4LWk0NdRDFxJqfawNDkFTY723wiEiIhE4EE5ERKJ5bSBcExyCxvpa\n62N9Qz00uhCnr/3DT749UGFJ4tDe7d4O4Zb4c/z+HDvA+L3N3+OXgteSRvTIUairuY6GuhpotME4\nVXQU859Y2eN1P35pjReiIyIiZ7yWNGQyGWY/tARb3nil65bbO6cjIjLaW+EQEZEIXp2nMSYpFWOS\nUr0ZAhERucGrSaM3vjTx79P330LF6a+g1uiw4vlfAgDaWpqxddM6NNbVQhcajoXLVkERpAQA5O3b\nieKCXAgyGWbNfxyjx6YAAK5eqsKn721AZ2cHxiSlYtb8xwEAxs5ObN+yHtcuVUGpVmPB0lXQhYR5\nJPbGhjrs2LwezU2NEAQBaVPvxpTp9/pN/J2dHdi45k8wdhphNHYiMSUNWTkP+038FmaTCW/+/UVo\ng0PxyPLv+VX8r7/0MyiClBAEAXK5HE99/6d+FX97Wyt2fvAWrl+9DEEQMO/R5QiLiPSL+GuvX8PW\njesgCIDZDNTXXsfd9y9A6sQMr8UvnzFr/q888uk8yGwy4b0Nr+Lxbz2PzOwc7N22GbFjkqBSa7wS\nj1KlxoQp01H+9QlMzMwCABzcsw1Do0bgoaXPoKmxHpXlpzAqMRk3rl1G7t5PseK5XyAxeQK2blqH\nyXfdA0EQ8OHbr2HOw08ie+4iFObug1KtQWh4JIqOfAlDexse+9ZzCAxUoDB3H+4YP9kjsXd0GDBy\nVAJm3r8AqZMyseuDtzEqIRnH8vb5RfwymRwp6RmYPP0epGfMxJf//gThkcPwVcEhv4jfouDgXphM\nXYkvJX2q3/z+AEBh7uf4xn+8gDvvnoW0qXcD8J/ffwD47MN3MCoxGfMefQrpU++GQqlE/v6dfhG/\nUq3BxMyZSM+YifSpM3Asbz/ufeBRHD24x2vx++Qtt7428W/kqAQEKVV2x8pPFiF10jQAQOrkadb4\nzpz6CslpUyCTyxEcFoHQ8EhcuVCJJn0DDO3tiI4ZBQAYNykT5V+f6D7XCeu5ksZPxvmK0x6LXaMN\nRtTwGABAoCII4ZHDoG+o85v4AWBIYCCArhoRzGYEKVV+FX9jQx0qSkuQducM6zF/ih9mM8xms90h\nf4m/va0VFyvLMWHKdACATC6HIkjpN/HbqjxzGiHhQ6ELCfNq/D7ZPeVs4t+VC5XeC8iJliY91Fod\ngK6CuaVJDwDQN9RhRFy89XVaXQj0jXWQyWTQBt+8pVgXHGqdzKhvqIe2+/PKZDIEBanQ2tIMpUrt\n0Zgbam/g2uWLGB47Gs16/4nfbDJhw6svor72OtIzZiIiarhfxf/59i24Z94jaG9rtR7zp/ghCNj8\nxl8hyGRInzoTaVNn+E38DbU3oFRrsOO9Dbh+5SKGjYzDfQ8+7jfx2zr1VQFS0qcC8O7vj08mDb8k\nCB47lRnmvl/kJkN7Gz5+Zw1mzX8MgYqgnuH6cPyCTIZvPvdztLe1Yss/X0FVRanfxF9xuhhqjQ5R\nw2N6X8vIR+MHgCe/8wI0uq6CafMbryBsaJTffP8mkwnXLlVh9oInED1yFPZu24L8/bv8Jn4Lo9GI\nM6eKkD13EQAn4Q5g/D7ZPeXOxD9vUWl0aNY3AgCa9A1QqbUAAG1wqH3sjXXQ6kK7P1Od9XhjQ531\nM2mDQ6Dvfs5kMsHQ1ubRWorJaMTH76zBuImZSByX7nfxWyiClBiTNB5XLp33m/gvVp5B+cki/N8f\n/wufvPsPnK8oxfbN/4Ra6x/xA4BGFwwAUGm0SByXhisXKv3m+9cGh0IbEobokaMAAEnjJ+Ha5Sq/\nid/ibGkJho2ItY7rejN+n0wathP/jJ2dOFV0FAkpad4NyqFfNzFlAooLcwEAJYV5SOyOLyF5Ak4V\nFcDY2Yn62huoq7mO6JhR0GiDoQhS4vKFczCbzfj6WL7Ne9JQciwPAHC6uBCx8UkeDf3T999CeGQ0\npsy4z+/ib2lusnbrdHQYUFl+ElHDY/wm/qych/Hdn76E77zwOzz0xDOIi0/Cg4+vQEKyf8TfYTDA\n0N4GADAY2lFZfgpDh43wm+9frdVBFxyK2uvXAACVZ04hIirab+K3OFV0FMlpU62PvRm/8OOX1ni+\nLeUBZ0tLsHfbFuvEP2/ecvvJpn/gwtkytLY0Q6XRYsbs+Ribko6PN66Fvr4OutAwLFi6yjpYnrdv\nJ74qOASZTG5/y9vF89jx3gYYOzu7bnl7qOuWt87ODmzfvB7Vly8gSKXGgidWIjgswiOxX6w8g41r\n/oShw0YAECAIwMw5CxEdMxpb/SD+61cvYceW9TCbAbPZjHGTMpAx8360tjT7Rfy2qs6W4eiXu/HI\n8u/5Tfz1tTfw0duvAxBgMhkxbmIGMrNz/CZ+AKi+fAE7P3wbJqMRIWERmLf4aZhMJr+Jv8NgwOt/\n+Cm+/cLvoFAEAYBXv3+fTRpEROR7fLJ7ioiIfBOTBhERicakQUREojFpEBGRaEwaREQkGpMGERGJ\nxqRBRESice0p8nnNTY34fPt7uHzhHIKUKsjlAciYeT8UShU+fOs1hIQPRYehHWpNMDKy7kf8HeMB\ndC3fXXTkINQaLUwmE2bev8D7Kwv0Q1NjA/Zs24yFy1ah+vIF6PUNiOfmZeQlTBrk8z5863WMn3IX\n5i/5FgCgsb4WZ04VQaFUIWZ0Ih5Z/j0AXTN/P3z7dQQMCURc91IId949C1Pvno2a6qt4Z83/4NmU\nlz0en8lkgkwmXaNdowvGwmWrAADXrlzE1YuVTBrkNUwa5NPOnzmNgIAApHdv/gMAupAwTJp2D6rO\nltm9NnJ4DO667wEcy91nTRoW4ZHDIJPJ0NLc5HQzrx3vbUBAwBBcvXQehvY23PvAYsTfMR5mkwn7\nd32EC+fKYOzsxMRp2Uifejeqzpbhy39vRZBSjdobV/HMD3/jNP6Swjwc+XIPBEFAZPQIPPDYN3Hm\n1FfI/fxTmExGKFVqzH/8W1BptDi4Zxvqa26grqYabS3NmDrzfqRNnYGGuhq8v+Hv+OazP8fB3Z+g\ns7MDl85XIDN7LoJDw7Fn22YYjZ0ICBiCeYuXIywiygPfPJFzTBrk025cu4yo4bGiXz9seCyOfLG7\nx/HLVecgCLJed39srK/F8u//DHU3qrFp3Z/x7f/8LYqP5SFIqcRT3/spjJ2d+Nf//RGjE7vW8qm+\nfAHf+sGvXG6NeePaZeTt34lv/MePEaRSo621BQAQMzoRT33vJwCAoqMHcfiLz3DPvEcBdK219dT3\nfgJDexvW/+23iE/u6moTBAEyuRwzZj+Ea5fOY9ZDSwB0LXn/5Hf+E4JMhsozp3Bg18d4+Mlvi/6+\niNzFpEF+ZffWTbhYeQZyeQCy5z3S43nHhdSOfrkHJ48fRqAiCAu6u3hcsWxxGRoRiZCwoai5fhWV\n5Sdx/eolnP7qGADA0N6KuhvVkMnliI4Z1eteyucrSpE0fjKCupeZtiwo11hfi893vI9mfQOM3Yvo\nWSSmpEEeEABlgAZx8XfgyoVKREaPdHmNtrZW7NiyHrU3qiEIAkwmU6+fkehWMWmQT4uIGo7SkuPW\nx7MXPIHW5ia8+fcX4WzbmWuXqhAeGW19bBnTEMVuIxszBEGA2QzMemiJtXVhUXW2DEMCFW58kpv2\nfPIups7sGrCvOluGQ3u228Rg+0qz089o68t/b0Vs/B14+Bv/gYa6Gmxa9+d+xUQkFm+5JZ8Wl3AH\njMYOnDj8hfVYR4fBWsDb7nFSfeUicvd9isnTsvt1rdLiQpjNZtTVXEd97Q2ERURh9NgUHM87AJPR\nCACovXENHQaDuNjjk1BaXIjWlmYAQFv3/9vb26DRdm1sVFKYZ/eeMyeLYOzsRGtzE6rOlmNY957O\nFoEKBdrb2qyPDW1t0HZvplNckOv+hyZyE1sa5PMWfeO72Lt9Cw4f+AxKtRaBgYHIzlkEM4CLlRXY\n8Orvum+51WH2Q0v6vQmOLiQMb/3v72Fob8OcRU9CHhCAtDtnoLGuBhte/R3MZjPUGi0efuq7os4X\nETUc0+6Zh41r/gSZTI6o4TGYt3g5pt/3ID5+Zw2CVGrExSehoa7G+p6hw0Zi49qX0dbSjOn3PQCN\nNtju+bgxSTi8/zNs+NtvkZk9FxlZc7Bjy3rk7vsU8Unj+/W5idzB/TSI0HX3VELyBCSlTvJaDAf3\nbEOgIkh8dxqRF7B7igiA0OfoAREB7J6i20zevk9x+qtjEATAbO4aGkkaPxnzFi/v9zlbW5rx7rq/\nWMfRLeddsnK19c4pMWbMmt/vGIgGCruniIhINHZPERGRaEwaREQkGpMGERGJxqRBRESiMWkQEZFo\n/x/QlA88piyzlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b84518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify facecolor when creating the figure\n",
    "fig, ax = plt.subplots(facecolor='lightslategray')\n",
    "df.plot(kind='scatter', x='GDP_per_capita', y='life_expectancy', ax=ax, color='white', linewidth=0)\n",
    "ax.set_ylim((0, 85))\n",
    "ax.set_xlim((0, 70000))\n",
    "# Specify background color for the axis/plot\n",
    "ax.set_axis_bgcolor(\"lightslategray\")\n",
    "ax.set_clip_on(False)\n",
    "\n",
    "# Be sure to specify facecolor or it won't look right in Illustrator\n",
    "fig.savefig(\"output.pdf\", facecolor=fig.get_facecolor(), transparent=True)"
   ]
  }
 ],
 "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}