1-data-science-and-ml-tools/matplotlib-exercises-solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Practice (solutions)\n",
    "\n",
    "This notebook offers a set of solutions to different tasks with Matplotlib.\n",
    "\n",
    "It should be noted there may be more than one different way to answer a question or complete an exercise.\n",
    "\n",
    "Different tasks will be detailed by comments or text.\n",
    "\n",
    "For further reference and resources, it's advised to check out the [Matplotlib documnetation](https://matplotlib.org/3.1.1/contents.html).\n",
    "\n",
    "If you're stuck, don't forget, you can always search for a function, for example if you want to create a plot with `plt.subplots()`, search for [`plt.subplots()`](https://www.google.com/search?q=plt.subplots())."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the pyplot module from matplotlib as plt and make sure \n",
    "# plots appear in the notebook using '%matplotlib inline'\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a simple plot using plt.plot()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11333e860>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deXxV9Z3/8dcHSICwBLKwhpAQNtmXsAUXFLT8rJVW2iouBaSidqxabR2n7dRpp/3VTt2rrdCCIFodq9baOo6A1A0QSdgEkZCwJmxZIBBC1vudP3KLTIZASG5y7vJ+Ph48uLn35J7PNwfeOTn5nO/XnHOIiEjoaeV1ASIi0jgKcBGREKUAFxEJUQpwEZEQpQAXEQlRbVpyZwkJCS4lJaUldykiEvKysrIKnXOJdZ9v0QBPSUkhMzOzJXcpIhLyzGzv2Z7XJRQRkRClABcRCVEKcBGREKUAFxEJUQpwEZEQpQAXEQlRCnARkRClABcRaUanKmv4tze3caysMuDvrQAXEWkmPp/jvlc2sXTtHjbuPxbw91eAi4g0k/94Zwdvbz3Ej66+iMsHdQv4+yvARUSawcuf7OPZ93O5aUIy8y5ObZZ9KMBFRAJsdU4hP35jK5cOTOSn1w7FzJplPwpwEZEA2nn4BHe8kEVaYkeeuXE0bVo3X8wqwEVEAqSwtIK5S9bTtk1rFs1Jp1O7qGbdnwJcRCQAyqtquO35TApLK1g0O52krjHNvs8WnQ9cRCQc+XyO+/+0mU37j/G7m8Ywsk+XFtmvzsBFRJro0RU7eGvLQR6cPpjpw3q22H4V4CIiTfBK5n6e+Xsus8b3Yf6l/Vp03+cNcDNbbGZHzGxrnee/a2Y7zGybmf1H85UoIhKc1uQW8sPXP+WSAQn8bMawZmsXrE9DzsCXANPPfMLMLgdmACOcc0OBRwJfmohI8Mo5Usody7JITejAMzeNIaoZ2wXrc949Ouc+AIrrPH0n8LBzrsK/zZFmqE1EJCgVlVZw65L1RLdpxeI54+jczO2C9Wnst4yBwCVmts7M3jezcfVtaGbzzSzTzDILCgoauTsRkeBQXlXD/GVZHD5ezu+/lU6fuOZvF6xPYwO8DdAVmAj8AHjF6rn445xb6JxLd86lJyYmNnJ3IiLec87xwKtbyNp7lMevH8Xo5K6e1tPYAM8DXne1PgF8QELgyhIRCT6Pr8jmzc0HeGD6IK4e3nLtgvVpbIC/AVwBYGYDgWigMFBFiYgEm9ey8nhqVQ7Xp/fhzsvSvC4HaMCdmGb2EjAFSDCzPOAhYDGw2N9aWAnMds655ixURMQrH+8q4sHXt5CRFs/Pv9by7YL1OW+AO+dm1fPSzQGuRUQk6OwqKOX2ZVkkx8Xwu5vGetIuWJ/gqUREJMgUn6zk1iXradPKeG7OeGJjvGkXrI8msxIROYuK6hpuX5bJgZJyXrptIsnx3rUL1kdn4CIidTjnePC1T1m/5yiPfmMkY/t62y5YHwW4iEgdT767kz9vzOf7Vw3kKyN7eV1OvRTgIiJneGNjPk+s3MnMMUn80+X9vS7nnBTgIiJ+n+wu5oFXtzCxXxy/vG540LQL1kcBLiIC7Ck8ye3LMknq2p5nbx5LdJvgj8fgr1BEpJkdK6ttFwR4bu44usREe1xRw6iNUEQiWmW1j9uXZZF39BQv3jaBvvEdvC6pwRTgIhKxnHM8+PoW1u0u5skbRjEuJc7rki6ILqGISMR6elUOr2/I53vTBjJjVG+vy7lgCnARiUh/2ZTPoyuyuW50b+6eGtztgvVRgItIxMnaW8wPXt3C+NQ4fjkz+NsF66MAF5GIsrfoJLc9n0XvLu1ZcPNY2rZp7XVJjaYAF5GIUVJWxdwl6/E5x+I54+jaITTaBeujABeRiFBZ7eOOF7LYX1zGgpvHkpoQOu2C9VEboYiEPeccP/rzp6zdVcTj149kQr94r0sKCJ2Bi0jY++17ufwpK4+7pw7ga6OTvC4nYM4b4Ga22MyO+Ne/rPva983MmZlWpBeRoPS3LQf49Ts7mDGqF9+bNsDrcgKqIWfgS4DpdZ80sz7AlcC+ANckIhIQG/Yd5b5XNpPetyu/mjkiZNsF63PeAHfOfQAUn+Wlx4EHAK1GLyJBZ39xGbctzaRnbDsWfiuddlGh2y5Yn0ZdAzeza4F859zmBmw738wyzSyzoKCgMbsTEbkgJadq2wWrfbXtgnEh3i5YnwsOcDOLAX4E/KQh2zvnFjrn0p1z6YmJiRe6OxGRC1JV4+M7L2axt+gkz948lrTEjl6X1GwacwaeBqQCm81sD5AEbDCzHoEsTETkQjnn+Nc3trI6p4hfXjeCSWnh0S5YnwvuA3fOfQp0+8fH/hBPd84VBrAuEZELtuCDXby8fj93Xd6fr48Nn3bB+jSkjfAlYC0wyMzyzGxe85clInJh3v70IA+//TnXjOjJfVcO9LqcFnHeM3Dn3KzzvJ4SsGpERBph0/5j3Pufmxid3IVHvjGSVq3Cq12wProTU0RCWt7RMr69NJNundvy+zBtF6yP5kIRkZB1vLyKW5esp6K6hpfnTyChY1uvS2pROgMXkZBUVePjn17cwK6C2nbB/t06eV1Si9MZuIiEHOccD725jQ93FvKrmcOZ3D8yp2PSGbiIhJw/fLibP67bxx2XpXH9uGSvy/GMAlxEQso72w7x/9/eztXDe/DAlwZ5XY6nFOAiEjK25B3jnpc3MiKpC499c1TEtAvWRwEuIiEh/9gp5i3NJL5DW/4QYe2C9VGAi0jQO1Fexbwl6ymvrOG5ueNI7BRZ7YL1UReKiAS16hofd/1xIzuPlLJk7jgGdo+8dsH66AxcRIKWc46f/vUz3s8u4N9nDOOSAZqS+kwKcBEJWotX72HZx3uZf2k/bpwQue2C9VGAi0hQWvHZYX7+1md8aWh3Hpw+2OtygpICXESCztb8Eu5+aSPDe8fyxPWjI75dsD4KcBEJKgdLTjFv6Xq6xkTxh2+l0z5a7YL1UReKiASN0opqbl2SycmKGl69cxLdOrfzuqSgpgAXkaBQXePj7pc2kn34BItmpzO4R2evSwp6DVlSbbGZHTGzrWc892sz+9zMtpjZn82sS/OWKSLh7udvbWfV50f4t2uHMmVQt/N/gjToGvgSYHqd51YAw5xzI4Bs4F8CXJeIRJAlq3ezZM0e5l2cyi0T+3pdTsg4b4A75z4Aius8t9w5V+3/8GMg/Jd/FpFmserzw/zsb58x7aLu/PDqi7wuJ6QEogvlVuDt+l40s/lmlmlmmQUFBQHYnYiEi20HSrjrjxsZ0qszT80aRWu1C16QJgW4mf0IqAZerG8b59xC51y6cy49MVG3wYpIrUMl5cxbkkls+ygWzR5HTLR6Ki5Uo79iZjYbuAaY6pxzgStJRMLdyYpq5i1dz4nyKv50Rwbd1S7YKI0KcDObDvwzcJlzriywJYlIOKvxOe55eSPbDx5n0exxDOmldsHGakgb4UvAWmCQmeWZ2TzgaaATsMLMNpnZs81cp4iEiV+8tZ2V24/w0FeGcvlgtQs2xXnPwJ1zs87y9KJmqEVEwtyytXtYvHo3czJSmJ2R4nU5IU9zoYhIi/j7jiM89OY2pg7uxr9eM8TrcsKCAlxEmt32g8e568UNDO7RmadmjVa7YIAowEWkWR05Xs68Jevp2K4Ni+ak06Gt2gUDRV9JEWk2ZZXVzFuaybFTVbxy+yR6xrb3uqSwojNwEWkWNT7HvS9vYtuBEp66YTTDesd6XVLYUYCLSLN4+O3tLP/sMD/+8hCmDenudTlhSQEuIgH34rq9/P7D3XxrUl/mTk7xupywpQAXkYB6P7uAn/xlG1MGJfKTa4Zgpo6T5qIAF5GA2XHoBP/04gYGdOvI0zeOoU1rRUxz0ldXRALiyIlybl2ynpjo1iyeM46OahdsdvoKi0iTnaqs4balmRSfrOSV2yfRq4vaBVuCAlxEmsTnc9z3yia25Jew4OaxDE9Su2BL0SUUEWmSX73zOW9vPcSPrr6Iq4b28LqciKIAF5FGe/mTfSx4fxc3TUhm3sWpXpcTcRTgItIoq3MK+fEbW7l0YCI/vXao2gU9oAAXkQu28/AJ7nghi7TEjjxz42i1C3pEX3URuSCFpRXMXbKetm1as2hOOp3aRXldUsRqyJJqi83siJltPeO5ODNbYWY7/X93bd4yRSQYlFfVcNvzmRSWVrBodjpJXWO8LimiNeQMfAkwvc5zDwLvOucGAO/6PxaRMObzOe7/02Y27T/GE9ePYmSfLl6XFPHOG+DOuQ+A4jpPzwCW+h8vBb4a4LpEJMg8umIHb205yIPTBzN9WE+vyxEafw28u3PuIID/73qXljaz+WaWaWaZBQUFjdydiHjplcz9PPP3XGaN78P8S/t5XY74NfsvMZ1zC51z6c659MTExObenYgE2JrcQn74+qdc3D+Bn80YpnbBINLYAD9sZj0B/H8fCVxJIhIsco6UcseyLFITOvDMTWOIUrtgUGns0XgTmO1/PBv4S2DKEZFgUVRawa1L1hPVuhWL54wjtr3aBYNNQ9oIXwLWAoPMLM/M5gEPA1ea2U7gSv/HIhImyqtqmL8si8PHy/n97HT6xKldMBiddzZC59ysel6aGuBaRCQIOOd44NUtZO09yjM3jmFMsm7zCFa6oCUi/8vjK7J5c/MBfvClQXx5hNoFg5kCXEROey0rj6dW5fDN9CS+MyXN63LkPBTgIgLAx7uKePD1LUzqF8/Pvzpc7YIhQAEuIuwqKOX2ZVkkx8Xw7M1jiW6jaAgFOkoiEa74ZCW3LllP61bGc3PGExujdsFQoTUxRSJYRXUNty/L5EBJOS/dNoHkeLULhhKdgYtEKOccD772Kev3HOWRb4xkbN84r0uSC6QAF4lQT767kz9vzOf+Kwdy7cheXpcjjaAAF4lAb2zM54mVO5k5Jom7rujvdTnSSApwkQjzye5iHnh1CxNS4/jldWoXDGUKcJEIsqfwJLcvyySpa3sW3KJ2wVCnoycSIY6V1bYLAiyeM44uMdEeVyRNpTZCkQhQWe3j9mVZ5B09xYu3TSAloYPXJUkAKMBFwpxzjgdf38K63cU8cf0oxqWoXTBc6BKKSJh7elUOr2/I595pA/jq6N5elyMBpAAXCWN/2ZTPoyuy+dro3twzdYDX5UiAKcBFwlTW3mJ+8OoWxqfE8fBMtQuGIwW4SBjaW3SS257PoldsOxbcMpa2bVp7XZI0gyYFuJl9z8y2mdlWM3vJzNoFqjARaZySsirmLlmPzzkWzxlH1w5qFwxXjQ5wM+sN3A2kO+eGAa2BGwJVmIhcuMpqH3e8kMX+4jIW3DyWfokdvS5JmlFT2wjbAO3NrAqIAQ40vSQRuRDlVTVk7T3K6pxC/r6jgO0Hj/PoN0YyoV+816VJM2t0gDvn8s3sEWAfcApY7pxbXnc7M5sPzAdITk5u7O5ExK+qxseWvBLW5BSyJreIrH1Hqaz20bqVMTIpll/NHM7MsUlelyktoNEBbmZdgRlAKnAM+JOZ3eyce+HM7ZxzC4GFAOnp6a4JtYpEJJ/P8fmhE6zJrQ3sT3YXU1pRDcBFPTvzrYl9yegfz/jUeDq21b15kaQpR3sasNs5VwBgZq8DGcAL5/wsETkn5xx7ispYnVPI2twi1u4qovhkJQCpCR2YMaoXGWkJTEqLJ06/oIxoTQnwfcBEM4uh9hLKVCAzIFWJRJhDJeWs9l8SWZtbyIGScgB6dG7HlEGJZKQlkJEWT68u7T2uVIJJU66BrzOzV4ENQDWwEf+lEhE5t6MnK1m7q6j2skhOEbsKTwLQNSaKSWnx3JmWwOS0eFITOugGHKlXky6YOeceAh4KUC0iYetkRTWf7C5mTW4hq3OK2H7oOM5Bh+jWjE+NY9b4ZDL6x3NRj860aqXAlobRbzxEmkFFdQ0b9h5jbW4hq3OL2Lz/GNU+R3TrVozp24XvTRvI5P7xjEjqQlRr3RAtjaMAFwmAGp/j0/yS05dE1u8ppqLaRyuD4UlduO3SfkxOS2Bs3660j9Zt7RIYCnCRRnDOkX249PQlkXW7izhRXtvaN6h7J2aNT2Zy/wTGp8YR2z7K42olXCnARRpoX1FZbWD7O0UKS2tb+5LjYvjy8J5k9E9gUr94Eju19bhSiRQKcJF6HDleztpdRafb+/KOngIgsVNbJvdPYLK/F7tPXIzHlUqkUoCL+JWUVfHx7qLTt6jvPFIKQOd2bZjYL57bLulHRlo8/bt1VGufBAUFuESssspqMvccZXVu7R2PW/NL8DloF9WKcSlxzBybREZaPEN7xdJarX0ShBTgEjEqq31szjt2+pLIxn1HqapxtGlljE7uwnevGEBGWjyjkrtoAQQJCQpwCVs+n+Ozg8dPd4qs31NMWWUNZjC0V2fmTk4lIy2ecSlxdNAkUBKC9K9WwoZzjtyCk7U3z+QU8fHuIo6VVQGQltiBmWOSmNw/ngmp8VqlRsKCAlxCWv6xU6d/6bgmt5DDxysA6BXbjmkXdWdy/3gy0hLo3lmr/Un4UYBLSCkqrfC39tX2Yu8pKgMgvkM0k9LiT8/a1zc+Rp0iEvYU4BLUTpRX8cnuYlbn1J5hf37oBAAd27ZhQmoct0xKISMtnkHdO2kSKIk4CnAJKuVVNWzYW9vatya3iC15JdT4HNFtWpHetys/+NIgJqXFM6J3LG00CZREOAW4eKq6xseW/C/Wd8zc+8X6jiOSYrnzsjQy0uIZ07cr7aLU2idyJgW4tCifz7Hj8InTy4WtO2N9x8E9OnHLxL5kpMUzPjWOTu00CZTIuSjApVk559hbVHb6ksja3C/Wd0yJj+HaUb3ISItnUr944jtqEiiRC9GkADezLsAfgGGAA251zq0NRGESug6VlJ9eQX1NzhfrO3bv3JYpAxNru0X6J9Bb6zuKNElTz8CfBP7bOfd1M4sGNC1bBDpWVsna3CLW5BaxOreQXQW16zt2iYliUr947pxSG9j9tL6jSEA1OsDNrDNwKTAHwDlXCVQGpiwJdoePl7NkzR4+yC7gs4O16zvG+Nd3vGFcHzLSEhjSU+s7ijSnppyB9wMKgOfMbCSQBdzjnDt55kZmNh+YD5CcnNyE3UkwKD5ZybPv57J0zR6qfY70vl25d+oX6ztGt1Frn0hLMedc4z7RLB34GJjsnFtnZk8Cx51z/1rf56Snp7vMzMzGVSqeOl5exR8+3M3ij3ZzsrKar43qzT3TBtA3voPXpYmEPTPLcs6l132+KWfgeUCec26d/+NXgQeb8H4ShMoqq1m6Zi8LPsjlWFkV/29YD+67ciADunfyujSRiNfoAHfOHTKz/WY2yDm3A5gKfBa40sRLFdU1vLRuH0//PZfC0gqmDErk/isHMTwp1uvSRMSvqV0o3wVe9Heg7ALmNr0k8VJ1jY/XNuTx1Ls55B87xYTUOH538xjGpcR5XZqI1NGkAHfObQL+z3UZCT0+n+OvWw7wxMqd7C48ycikWB6eOZyL+yeo9U8kSOlOzAjnnGPFZ4d5bEU2nx86waDunVh4y1iuHNJdwS0S5BTgEco5x0c5hTyyPJvN+4+RmtCBJ28YxVdG9FLvtkiIUIBHoMw9xfz6nR2s211Mr9h2/GrmcGaOSdL0rCIhRgEeQbbml/DI8h28t6OAhI5t+bevDGHWhGStwC4SohTgEWDn4RM8tiKbt7ceIrZ9FP88fTCzM/oSE63DLxLK9D84jO0rKuOJldm8sSmf9lGtuXvqAL59SSqdNc+2SFhQgIehQyXlPLVqJ6+s30/rVsa3L+nHHZelEdch2uvSRCSAFOBhpKi0gt++l8uyj/finGPW+GTuuqI/3Tu387o0EWkGCvAwUHKqij98uItFH+2mvKqG68Ykcc/UAfSJ0/TsIuFMAR7CTlZUs2TNHha8n8vx8mq+PKIn35s2kP7dOnpdmoi0AAV4CCqvquHFdfv43Xs5FJZWMnVwN+67aiBDe2miKZFIogAPIVU1Pv6UmcdvVu3kYEk5GWnxLLhlEGP7dvW6NBHxgAI8BNT4HH/dfIDHV2azt6iM0cldePQbI8non+B1aSLiIQV4EHPO8c62wzy2YgfZh0u5qGdnFs1O54rB3TTRlIgowIORc473swt4dHk2n+aX0C+xA0/fOJqrh/XURFMicpoCPMh8sruYR97ZwSd7iknq2p5ff30EXxvdWxNNicj/oQAPElvyjvHI8mw+yC6gW6e2/PuMoVw/LlmrvItIvZoc4GbWGsgE8p1z1zS9pMiy49AJHluxg3e2HaZrTBQ/vHowt0xMoX20ZggUkXMLxBn4PcB2oHMA3iti7Ck8yeMrs3lz8wE6Rrfhe9MGcuvFKXTSRFMi0kBNCnAzSwK+DPwCuC8gFYW5A8dO8ZtVO3klM4+o1sbtl6Zx+6X96KqJpkTkAjX1DPwJ4AGgUwBqCWsFJyr47Xs5vPjxPgBumdiX71yeRrdOmmhKRBqn0QFuZtcAR5xzWWY25RzbzQfmAyQnJzd2dyGrpKyKBR/k8tzqPVTW+Pj6mCTunjaA3l3ae12aiIS4ppyBTwauNbOrgXZAZzN7wTl385kbOecWAgsB0tPTXRP2F1JKK6p57qPdLPxwF6UV1XxlRC/unTaAfomaaEpEAqPRAe6c+xfgXwD8Z+Dfrxvekai8qoYXPt7Lb9/LpfhkJVcO6c79Vw1kcA/9jldEAkt94AFSWe3jlcz9/GbVTg4fr+CSAQncf9UgRvXp4nVpIhKmAhLgzrn3gPcC8V6hpsbneGNjPk+8m83+4lOk9+3KkzeMZmK/eK9LE5EwpzPwRvL5HP+97RCPrcgm50gpw3p35mdzhzFlYKImmhKRFqEAv0DOOd7bUcAjy3ew7cBx+nfryO9uGsP0YT0U3CLSohTgF2BtbhGPLN9B1t6jJMfF8Ng3RzJjVG9aa4ZAEfGAArwBNu47yqPLs/kop5Aendvxi68N45vpfYjSDIEi4iEF+DlsP3icR5dns3L7YeI7RPPjL1/EzRP70i5KE02JiPcU4Gexq6CUx1fu5G9bDtCxbRu+f9VA5k5OpUNbfblEJHgokc6Qd7SMp97dyWsb8mnbphXfmZLG/EvSiI3RDIEiEnwU4MCRE+U8syqHP36yDzNj9qQUvnN5Ggkd23pdmohIvSI6wI+erOTZD3JZumYP1TWOb6T34btX9KeXJpoSkRAQkQF+oryKRR/tZtGHuymtrOaro3pz77QB9I3v4HVpIiINFlEBfqqyhufX7uHZ93M5WlbF9KE9uO+qgQzsrunMRST0RESAV1b7eHn9Pn6zKoeCExVcNjCR7181iOFJsV6XJiLSaGEd4NU1Pl7fmM+TK3eSf+wU41PjeObGMYxPjfO6NBGRJgvLAPf5HG99epDHV2azq+AkI5Ji+eV1w7lkQILmKxGRsBFWAe6c493tR3h0RTbbDx5nUPdOLLhlLFcN6a7gFpGwEzYBvjqnkEeW72DjvmOkxMfw5A2juGZEL000JSJhK+QDPGvvUR55ZwdrdxXRK7YdD183nJljkzTRlIiEvZAN8G0HSnh0eTarPj9CQsdoHvrKEGaNT9ZEUyISMRod4GbWB3ge6AH4gIXOuScDVVh9co6U8viKbN769CCx7aN4YPog5mSkEBMdst+LREQapSmpVw3c75zbYGadgCwzW+Gc+yxAtf0v+4vLeGLlTv68MY/2Ua25+4r+zLukH7HtNdGUiESmRge4c+4gcND/+ISZbQd6AwEP8N+8u5OnVu2klRnzLk7ljsvSiNdEUyIS4QJy3cHMUoDRwLqzvDYfmA+QnJzcqPdPimvP9eP6cNflA+gR267xhYqIhBFzzjXtDcw6Au8Dv3DOvX6ubdPT011mZmaT9iciEmnMLMs5l173+Sb12plZFPAa8OL5wltERAKr0QFutbc2LgK2O+ceC1xJIiLSEE05A58M3AJcYWab/H+uDlBdIiJyHk3pQvkI0H3qIiIe0f3mIiIhSgEuIhKiFOAiIiFKAS4iEqKafCPPBe3MrADY28hPTwAKA1iOlzSW4BMu4wCNJVg1ZSx9nXOJdZ9s0QBvCjPLPNudSKFIYwk+4TIO0FiCVXOMRZdQRERClAJcRCREhVKAL/S6gADSWIJPuIwDNJZgFfCxhMw1cBER+d9C6QxcRETOoAAXEQlRQRfgZjbdzHaYWY6ZPXiW19ua2X/6X1/nXw0oKDVgLHPMrOCM2Ry/7UWd52Nmi83siJltred1M7On/OPcYmZjWrrGhmjAOKaYWckZx+MnLV1jQ5lZHzP7u5ltN7NtZnbPWbYJlePSkLEE/bExs3Zm9omZbfaP46dn2Saw+eWcC5o/QGsgF+gHRAObgSF1tvkO8Kz/8Q3Af3pddxPGMgd42utaGzCWS4ExwNZ6Xr8aeJva2SknAuu8rrmR45gC/M3rOhs4lp7AGP/jTkD2Wf59hcpxachYgv7Y+L/OHf2Po6hdYnJinW0Cml/BdgY+Hshxzu1yzlUCLwMz6mwzA1jqf/wqMNW/uESwachYQoJz7gOg+BybzACed7U+BrqYWc+Wqa7hGjCOkOGcO+ic2+B/fAL4x6LiZwqV49KQsQQ9/9e51P9hlP9P3S6RgOZXsAV4b2D/GR/n8X8P5OltnHPVQAkQ3yLVXZiGjAVgpv/H21fNrE/LlBZwDR1rKJjk/xH4bTMb6nUxDXGORcVD7rica4F0QuDYmFlrM9sEHAFWOOfqPSaByK9gC/CzfSeq+x2sIdsEg4bU+VcgxTk3AljJF9+ZQ02oHJPz2UDtnBMjgd8Ab3hcz3n5FxV/DbjXOXe87stn+ZSgPS7nGUtIHBvnXI1zbhSQBIw3s2F1NgnoMQm2AM8DzjwLTQIO1LeNmbUBYgnOH4vPOxbnXJFzrsL/4e+BsS1UW6A15LgFPefc8X/8COyc+y8gyswSPC6rXg1YVDxkjsv5xhJqx8Y5dwx4D5he56WA5lewBfh6YICZpZpZNLUX+d+ss82bwGz/468Dq5z/NwJB5rxjqXM98lpqr/2FojeBb/m7HiYCJc65g14XdaHMrMc/rkea2Xhq/38UeVvV2TVwUfGQOC4NGUsoHBszSzSzLv7H7YFpwOd1NtcoD6cAAADLSURBVAtofjV6Tczm4JyrNrO7gHeo7eJY7JzbZmY/AzKdc29Se6CXmVkOtd+5bvCu4vo1cCx3m9m1QDW1Y5njWcHnYGYvUdsFkGBmecBD1P6CBufcs8B/UdvxkAOUAXO9qfTcGjCOrwN3mlk1cAq4IUhPDuCLRcU/9V9zBfghkAyhdVxo2FhC4dj0BJaaWWtqv8G84pz7W3Pml26lFxEJUcF2CUVERBpIAS4iEqIU4CIiIUoBLiISohTgIiIhSgEuIhKiFOAiIiHqfwB2sJ/Hw8/xhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a single Python list\n",
    "plt.plot([2, 6, 8, 17])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create two lists, one called X, one called y, each with 5 numbers in them\n",
    "X = [22, 88, 98, 103, 45]\n",
    "y = [7, 8, 9, 10, 11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10578f278>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot X & y (the lists you've created)\n",
    "plt.plot(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's another way to create plots with Matplotlib, it's known as the object-orientated (OO) method. Let's try it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot using plt.subplots()\n",
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1134b0588>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot using plt.subplots() and then add X & y on the axes\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try a small matplotlib workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import and get matplotlib ready\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Prepare data (create two lists of 5 numbers, X & y)\n",
    "X = [34, 77, 21, 54, 9]\n",
    "y = [9, 45, 89, 66, 4]\n",
    "\n",
    "# Setup figure and axes using plt.subplots()\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Add data (X, y) to axes\n",
    "ax.plot(X, y)\n",
    "\n",
    "# Customize plot by adding a title, xlabel and ylabel\n",
    "ax.set(title=\"Sample simple plot\",\n",
    "       xlabel=\"x-axis\",\n",
    "       ylabel=\"y-axis\")\n",
    "\n",
    "# Save the plot to file using fig.savefig()\n",
    "fig.savefig(\"../images/simple-plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, this is a simple line plot, how about something a little different?\n",
    "\n",
    "To help us, we'll import NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import NumPy as np\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an array of 100 evenly spaced numbers between 0 and 100 using NumPy and save it to variable X\n",
    "X = np.linspace(0, 10, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113570c50>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot using plt.subplots() and plot X versus X^2 (X squared)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(X, X**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start with scatter plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11372b5f8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of X versus the exponential of X (np.exp(X))\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X, np.exp(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1134a06a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of X versus np.sin(X)\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X, np.sin(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about we try another type of plot? This time let's look at a bar plot. First we'll make some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Python dictionary of 3 of your favourite foods with \n",
    "# The keys of the dictionary should be the food name and the values their price\n",
    "favourite_food_prices = {\"Almond butter\": 10,\n",
    "                         \"Blueberries\": 5,\n",
    "                         \"Eggs\": 6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Food'),\n",
       " Text(0.5, 0, 'Price ($)'),\n",
       " Text(0.5, 1.0, \"Daniel's favourite foods\")]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar graph where the x-axis is the keys of the dictionary\n",
    "# and the y-axis is the values of the dictionary\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(favourite_food_prices.keys(), favourite_food_prices.values())\n",
    "\n",
    "# Add a title, xlabel and ylabel to the plot\n",
    "ax.set(title=\"Daniel's favourite foods\",\n",
    "       xlabel=\"Food\",\n",
    "       ylabel=\"Price ($)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Food'),\n",
       " Text(0.5, 0, 'Price ($)'),\n",
       " Text(0.5, 1.0, \"Daniel's favourite foods\")]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the same plot as above, except this time make the bars go horizontal\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(list(favourite_food_prices.keys()),\n",
    "        list(favourite_food_prices.values()))\n",
    "ax.set(title=\"Daniel's favourite foods\",\n",
    "       xlabel=\"Price ($)\",\n",
    "       ylabel=\"Food\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All this food plotting is making me hungry. But we've got a couple of plots to go.\n",
    "\n",
    "Let's see a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,  19.,  73., 184., 295., 269., 118.,  34.,   3.,   2.]),\n",
       " array([-3.71922182, -2.93930767, -2.15939353, -1.37947939, -0.59956525,\n",
       "         0.1803489 ,  0.96026304,  1.74017718,  2.52009133,  3.30000547,\n",
       "         4.07991961]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random NumPy array of 1000 normally distributed numbers using NumPy and save it to X\n",
    "X = np.random.randn(1000)\n",
    "\n",
    "# Create a histogram plot of X\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([109., 110., 114.,  93., 107.,  99.,  90.,  86.,  94.,  98.]),\n",
       " array([4.33144794e-04, 1.00050572e-01, 1.99667999e-01, 2.99285426e-01,\n",
       "        3.98902853e-01, 4.98520281e-01, 5.98137708e-01, 6.97755135e-01,\n",
       "        7.97372562e-01, 8.96989989e-01, 9.96607416e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a NumPy array of 1000 random numbers and save it to X\n",
    "X = np.random.random(1000)\n",
    "\n",
    "# Create a histogram plot of X\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the distributions (spread of data) are different. Why do they differ? \n",
    "\n",
    "What else can you find out about the normal distribution? \n",
    "\n",
    "Can you think of any other kinds of data which may be normally distributed?\n",
    "\n",
    "These questions aren't directly related to plotting or Matplotlib but they're helpful to think of.\n",
    "\n",
    "Now let's try make some subplots. A subplot is another name for a figure with multiple plots on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an empty subplot with 2 rows and 2 columns (4 subplots total)\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2,\n",
    "                                             ncols=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the subplot has multiple figures. Now let's add data to each axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the same plot as above with 2 rows and 2 columns and figsize of (10, 5)\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2,\n",
    "                                             ncols=2,\n",
    "                                             figsize=(10, 5))\n",
    "\n",
    "# Plot X versus X/2 on the top left axes\n",
    "ax1.plot(X, X/2)\n",
    "\n",
    "# Plot a scatter plot of 10 random numbers on each axis on the top right subplot\n",
    "ax2.scatter(np.random.random(10), np.random.random(10))\n",
    "\n",
    "# Plot a bar graph of the favourite food keys and values on the bottom left subplot\n",
    "ax3.bar(favourite_food_prices.keys(), favourite_food_prices.values())\n",
    "\n",
    "# Plot a histogram of 1000 random normally distributed numbers on the bottom right subplot\n",
    "ax4.hist(np.random.randn(1000));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Woah. There's a lot going on there.\n",
    "\n",
    "Now we've seen how to plot with Matplotlib and data directly. Let's practice using Matplotlib to plot with pandas.\n",
    "\n",
    "First we'll need to import pandas and create a DataFrame work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas as pd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the '../data/car-sales.csv' into a DataFame called car_sales and view\n",
    "car_sales = pd.read_csv(\"../data/car-sales.csv\")\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-628fda813398>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Try to plot the 'Price' column using the plot() function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcar_sales\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Price\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    792\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 794\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.6/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     60\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left_ax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.6/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    277\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.6/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    412\u001b[0m         \u001b[0;31m# no non-numeric frames or series allowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no numeric data to plot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m         \u001b[0;31m# GH25587: cast ExtensionArray of pandas (IntegerArray, etc.) to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "# Try to plot the 'Price' column using the plot() function\n",
    "car_sales[\"Price\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why doesn't it work?\n",
    "\n",
    "Hint: It's not numeric data.\n",
    "\n",
    "In the process of turning it to numeric data, let's create another column which adds the total amount of sales and another one which shows what date the car was sold.\n",
    "\n",
    "Hint: To add a column up cumulatively, look up the cumsum() function. And to create a column of dates, look up the date_range() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the symbols, the final two numbers from the 'Price' column and convert it to numbers\n",
    "car_sales[\"Price\"] = car_sales[\"Price\"].str.replace(\"[\\$\\,\\.]\", \"\")\n",
    "car_sales[\"Price\"] = car_sales[\"Price\"].str[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total Sales</th>\n",
       "      <th>Sale Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2020-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "      <td>9000</td>\n",
       "      <td>2020-01-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>16000</td>\n",
       "      <td>2020-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "      <td>38000</td>\n",
       "      <td>2020-01-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "      <td>41500</td>\n",
       "      <td>2020-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "      <td>46000</td>\n",
       "      <td>2020-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>53500</td>\n",
       "      <td>2020-01-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>60500</td>\n",
       "      <td>2020-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>66750</td>\n",
       "      <td>2020-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>76450</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Total Sales  Sale Date\n",
       "0  Toyota  White         150043      4   4000         4000 2020-01-13\n",
       "1   Honda    Red          87899      4   5000         9000 2020-01-14\n",
       "2  Toyota   Blue          32549      3   7000        16000 2020-01-15\n",
       "3     BMW  Black          11179      5  22000        38000 2020-01-16\n",
       "4  Nissan  White         213095      4   3500        41500 2020-01-17\n",
       "5  Toyota  Green          99213      4   4500        46000 2020-01-18\n",
       "6   Honda   Blue          45698      4   7500        53500 2020-01-19\n",
       "7   Honda   Blue          54738      4   7000        60500 2020-01-20\n",
       "8  Toyota  White          60000      4   6250        66750 2020-01-21\n",
       "9  Nissan  White          31600      4   9700        76450 2020-01-22"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column called 'Total Sales' to car_sales which cumulatively adds the 'Price' column\n",
    "car_sales[\"Total Sales\"] = car_sales[\"Price\"].astype(int).cumsum()\n",
    "\n",
    "# Add a column called 'Sale Date' which lists a series of successive dates starting from today (your today)\n",
    "car_sales[\"Sale Date\"] = pd.date_range(\"13/1/2020\", periods=len(car_sales))\n",
    "\n",
    "# View the car_sales DataFrame\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got a numeric column (`Total Sales`) and a dates column (`Sale Date`), let's visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115a48f60>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the plot() function to plot the 'Sale Date' column versus the 'Total Sales' column\n",
    "car_sales.plot(x=\"Sale Date\", y=\"Total Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113d14cf8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert the 'Price' column to the integers\n",
    "car_sales[\"Price\"] = car_sales[\"Price\"].astype(int)\n",
    "\n",
    "# Create a scatter plot of the 'Odometer (KM)' and 'Price' column using the plot() function\n",
    "car_sales.plot(x=\"Odometer (KM)\", y=\"Price\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115cb87f0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a NumPy array of random numbers of size (10, 4) and save it to X\n",
    "X = np.random.rand(10, 4)\n",
    "\n",
    "# Turn the NumPy array X into a DataFrame with columns called ['a', 'b', 'c', 'd']\n",
    "df = pd.DataFrame(X, columns=[\"a\", \"b\", \"c\", \"d\"])\n",
    "\n",
    "# Create a bar graph of the DataFrame\n",
    "df.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115e37fd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar graph of the 'Make' and 'Odometer (KM)' columns in the car_sales DataFrame\n",
    "car_sales.plot(x=\"Make\", y=\"Odometer (KM)\", kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115f729e8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD4CAYAAADhNOGaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAATxUlEQVR4nO3df7BkZX3n8ffHGRSJRNSZrFMD44VIzLJWFJwQLGPWNWoAFcrE7A6VisSYTNZoRSpJlaApdK3aKt3daGIwwlhQIiEGf0UnEYvFxNUkVQEHwk8H4tWwy4RZBEwYCQQy5Lt/9Bm3p6f73r7XebpnOO9XVdc95zlPn/O9p/v2554ffU6qCklSfz1p3gVIkubLIJCknjMIJKnnDAJJ6jmDQJJ6bu28C1ipdevW1cLCwrzLkKTDyg033HB/Va0fN+2wC4KFhQV27Ngx7zIk6bCS5H9PmuauIUnqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6rlkQJDkyyfVJbk5ye5L/MqbPU5JclWQxyXVJFlrVI0kar+UWwaPAy6vqBcALgdOTnDbS503AP1TVc4EPAO9rWI8kaYxmQVADD3WjR3SP0ZsfnA1c3g1/CvjJJGlVkyTpQE2/WZxkDXAD8FzgQ1V13UiXjcDdAFW1N8mDwLOA+0fmsxXYCrBp06aWJTezcP7n57Lcu9776rksV9Lho+nB4qp6vKpeCBwLnJrk+SNdxv33f8At06pqW1VtrqrN69ePvVSGJGmVZnLWUFX9I/C/gNNHJu0CjgNIshZ4OvDtWdQkSRpoedbQ+iTHdMNPBV4B3DHSbTtwbjf8euDPy5soS9JMtTxGsAG4vDtO8CTgE1X1p0neA+yoqu3ApcAVSRYZbAlsaViPJGmMZkFQVbcAJ49pv3Bo+J+Bn21VgyRpeX6zWJJ6ziCQpJ4zCCSp5wwCSeo5g0CSes4gkKSeMwgkqecMAknqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQpJ4zCCSp5wwCSeo5g0CSes4gkKSeMwgkqecMAknqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6rlkQJDkuyZeS7Exye5K3jenzsiQPJrmpe1zYqh5J0nhrG857L/AbVXVjkqOBG5JcW1VfG+n3F1X1moZ1SJKW0GyLoKp2V9WN3fB3gJ3AxlbLkyStzkyOESRZAE4Grhsz+cVJbk7yhST/bsLztybZkWTHfffd17BSSeqf5kGQ5GnAp4HzqmrPyOQbgedU1QuA3wM+O24eVbWtqjZX1eb169e3LViSeqZpECQ5gkEIXFlVnxmdXlV7quqhbvhq4Igk61rWJEnaX8uzhgJcCuysqvdP6PPsrh9JTu3qeaBVTZKkA7U8a+glwM8Dtya5qWt7B7AJoKouBl4PvDnJXuARYEtVVcOaJEkjmgVBVf0lkGX6XARc1KoGSdLy/GaxJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPdcsCJIcl+RLSXYmuT3J28b0SZIPJllMckuSU1rVI0kab23Dee8FfqOqbkxyNHBDkmur6mtDfc4ATuwePwZ8uPspSZqRZlsEVbW7qm7shr8D7AQ2jnQ7G/hYDfw1cEySDa1qkiQdqOUWwXclWQBOBq4bmbQRuHtofFfXtnvk+VuBrQCbNm1qVeYT0sL5n5/bsu9676vntmxJ02t+sDjJ04BPA+dV1Z7RyWOeUgc0VG2rqs1VtXn9+vUtypSk3moaBEmOYBACV1bVZ8Z02QUcNzR+LHBPy5okSftredZQgEuBnVX1/gndtgNv6M4eOg14sKp2T+grSWqg5TGClwA/D9ya5Kau7R3AJoCquhi4GjgTWAQeBt7YsB5J0hhTBUGS51fVbSuZcVX9JeOPAQz3KeAtK5mvJOngmnbX0MVJrk/yq0mOaVqRJGmmpgqCqvpx4OcYHNjdkeQPk7yyaWWSpJmY+mBxVX0d+C3g7cC/Bz6Y5I4kP92qOElSe1MFQZIfSfIBBt8Ofjnw2qr6t93wBxrWJ0lqbNqzhi4CPgK8o6oe2ddYVfck+a0mlUmSZmLaIDgTeKSqHgdI8iTgyKp6uKquaFadJKm5aY8RfBF46tD4UV2bJOkwN20QHFlVD+0b6YaPalOSJGmWpg2Cfxq+aUySFwGPLNFfknSYmPYYwXnAJ5PsuyDcBuA/tSlJkjRLUwVBVX01yQ8Dz2Nw2Yg7qupfmlYmSZqJlVx07keBhe45Jyehqj7WpCpJ0sxMe9G5K4AfBG4CHu+aCzAIJOkwN+0WwWbgpO5qoZKkJ5Bpzxq6DXh2y0IkSfMx7RbBOuBrSa4HHt3XWFVnNalKkjQz0wbBu1sWIUman2lPH/1ykucAJ1bVF5McBaxpW5okaRamvQz1LwOfAi7pmjYCn21VlCRpdqY9WPwWBjej3wPfvUnND7QqSpI0O9MGwaNV9di+kSRrGXyPQJJ0mJs2CL6c5B3AU7t7FX8S+JN2ZUmSZmXaIDgfuA+4FfgV4GoG9y+WJB3mpj1r6F8Z3KryI23LkSTN2rTXGvo7xhwTqKoTDnpFkqSZWsm1hvY5EvhZ4JlLPSHJZcBrgG9V1fPHTH8Z8Dng77qmz1TVe6asR5J0kEx1jKCqHhh6/H1V/Q7w8mWe9lHg9GX6/EVVvbB7GAKSNAfT7ho6ZWj0SQy2EI5e6jlV9ZUkC6uuTJI0E9PuGvrtoeG9wF3AfzwIy39xkpuBe4DfrKrbx3VKshXYCrBp06aDsFhJ0j7TnjX0Hxos+0bgOVX1UJIzGVyy4sQJy98GbAPYvHmzX2STpINo2l1Dv77U9Kp6/0oXXFV7hoavTvL7SdZV1f0rnZckafVWctbQjwLbu/HXAl8B7l7tgpM8G7i3qirJqQyOPTyw2vlJklZnJTemOaWqvgOQ5N3AJ6vqlyY9IcnHgZcB65LsAt4FHAFQVRcDrwfenGQv8AiwxVthStLsTRsEm4DHhsYfAxaWekJVnbPM9IuAi6ZcviSpkWmD4Arg+iR/zOAbxq8DPtasKknSzEx71tB/TfIF4KVd0xur6m/alSVJmpVprz4KcBSwp6p+F9iV5PhGNUmSZmjaW1W+C3g7cEHXdATwB62KkiTNzrRbBK8DzgL+CaCq7mGZS0xIkg4P0wbBY92pnQWQ5PvalSRJmqVpg+ATSS4Bjknyy8AX8SY1kvSEMO1ZQ/+ju1fxHuB5wIVVdW3TyiRJM7FsECRZA1xTVa8A/PCXpCeYZXcNVdXjwMNJnj6DeiRJMzbtN4v/Gbg1ybV0Zw4BVNWvNalKkjQz0wbB57uHJOkJZskgSLKpqv5PVV0+q4IkSbO13DGCz+4bSPLpxrVIkuZguSDI0PAJLQuRJM3HckFQE4YlSU8Qyx0sfkGSPQy2DJ7aDdONV1V9f9PqJEnNLRkEVbVmVoVIkuZjJfcjkCQ9ARkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPdcsCJJcluRbSW6bMD1JPphkMcktSU5pVYskabKWWwQfBU5fYvoZwIndYyvw4Ya1SJImaBYEVfUV4NtLdDkb+FgN/DVwTJINreqRJI037R3KWtgI3D00vqtr2z3aMclWBlsNbNq0adULXDjfm6zNUh/X913vffVcljuvdT2v33ee5vm+brW+53mwOGPaxl7quqq2VdXmqtq8fv36xmVJUr/MMwh2AccNjR8L3DOnWiSpt+YZBNuBN3RnD50GPFhVB+wWkiS11ewYQZKPAy8D1iXZBbwLOAKgqi4GrgbOBBaBh4E3tqpFkjRZsyCoqnOWmV7AW1otX5I0Hb9ZLEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk91zQIkpye5M4ki0nOHzP9F5Lcl+Sm7vFLLeuRJB1obasZJ1kDfAh4JbAL+GqS7VX1tZGuV1XVW1vVIUlaWsstglOBxar6ZlU9BvwRcHbD5UmSVqFlEGwE7h4a39W1jfqZJLck+VSS48bNKMnWJDuS7Ljvvvta1CpJvdUyCDKmrUbG/wRYqKofAb4IXD5uRlW1rao2V9Xm9evXH+QyJanfWgbBLmD4P/xjgXuGO1TVA1X1aDf6EeBFDeuRJI3RMgi+CpyY5PgkTwa2ANuHOyTZMDR6FrCzYT2SpDGanTVUVXuTvBW4BlgDXFZVtyd5D7CjqrYDv5bkLGAv8G3gF1rVI0kar1kQAFTV1cDVI20XDg1fAFzQsgZJ0tL8ZrEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk91zQIkpye5M4ki0nOHzP9KUmu6qZfl2ShZT2SpAM1C4Ika4APAWcAJwHnJDlppNubgH+oqucCHwDe16oeSdJ4LbcITgUWq+qbVfUY8EfA2SN9zgYu74Y/BfxkkjSsSZI0Ym3DeW8E7h4a3wX82KQ+VbU3yYPAs4D7hzsl2Qps7UYfSnJnk4pXZx0j9R4irGt6B62mHNxt2kNxXcFQXQf59/1eHPLr6mD4Htf3cyZNaBkE4/6zr1X0oaq2AdsORlEHW5IdVbV53nWMsq7pHYo1gXWtxKFYExy6dY1quWtoF3Dc0PixwD2T+iRZCzwd+HbDmiRJI1oGwVeBE5Mcn+TJwBZg+0if7cC53fDrgT+vqgO2CCRJ7TTbNdTt838rcA2wBrisqm5P8h5gR1VtBy4FrkiyyGBLYEureho6JHdZYV0rcSjWBNa1EodiTXDo1rWf+A+4JPWb3yyWpJ4zCCSp76qq9w8GZy59CdgJ3A68rWt/N/D3wE3d48yh51wALAJ3Aj811H5617YInD/UfjxwHfB14CrgyVPWdhdwa7f8HV3bM4Fru3ldCzyjaw/wwW7ZtwCnDM3n3K7/14Fzh9pf1M1/sXtupqjpeUPr5CZgD3DePNYXcBnwLeC2obbm62fSMpao6b8Dd3TL/WPgmK59AXhkaJ1dvNplL/X7LVFX89cMeEo3vthNX5iirquGaroLuGmW64vJnwlzfW81+wxsvYDD4QFs2PfCAUcDf8vgshjvBn5zTP+TgJu7N/jxwDcYHBBf0w2fADy563NS95xPAFu64YuBN09Z213AupG2/7bvDxA4H3hfN3wm8IXuTXkacN3QG+ub3c9ndMP73sDXAy/unvMF4IwVrrs1wP9l8GWVma8v4CeAU9j/Q6T5+pm0jCVqehWwtht+31BNC8P9Rn63FS170u+3TF3NXzPgV+k+sBmcEHLVcnWNTP9t4MJZri8mfybM9b3V6jH3D+FD8QF8DnjlEn8kFwAXDI1f072gLwauGe3XvdD38/8/CPbrt0wtd3FgENwJbBh6w97ZDV8CnDPaDzgHuGSo/ZKubQNwx1D7fv2mrO9VwF91w3NZX4x8OMxi/UxaxqSaRqa9DrhyqX6rWfak32+ZddX8Ndv33G54bdcvS9U11B4GVx84cR7ra8xnwtzfWy0eHiMY0V0B9WQGm7AAb01yS5LLkjyjaxt3+YyNS7Q/C/jHqto70j6NAv5nkhu6S20A/Juq2g3Q/fyBVda1sRsebV+JLcDHh8bnvb5gNutn0jKm8YsM/gPc5/gkf5Pky0leOlTrSpc96fdYTuvXbL9LyQD7LiUzjZcC91bV14faZrq+Rj4TDvX31qoYBEOSPA34NHBeVe0BPgz8IPBCYDeDTVSYfGmMlbZP4yVVdQqDq7i+JclPLNF3lnXRfVHwLOCTXdOhsL6WMvc6krwT2Atc2TXtBjZV1cnArwN/mOT7V7ns1TxnFq/Z97Iez2H/fzRmur7GfCasdF6zfo+vikHQSXIEgxf8yqr6DEBV3VtVj1fVvwIfYXBFVZh8+YxJ7fcDx3SX0RhuX1ZV3dP9/BaDg4ynAvcm2dDVvYHBgbbV1LWrGx5tn9YZwI1VdW9X49zXV2cW62fSMiZKci7wGuDnqtvur6pHq+qBbvgGBvvff2iVy57msi77mdFrtqpLyXR9f5rBgeN99c5sfY37TFjFvGby3vpeGQRAd+nrS4GdVfX+ofYNQ91eB9zWDW8HtnQ31jkeOJHBgZ+xl9Xo/ui/xOAyGjA4i+BzU9T1fUmO3jfMYH/8bex/aY7heW0H3pCB04AHu03La4BXJXlGt+n/Kgb7b3cD30lyWrcO3jBNXUP2+29t3utryCzWz6RljJXkdODtwFlV9fBQ+/ru3h0kOaFbN99c5bIn/X5L1TWL12y1l5J5BYP96N/dhTKr9TXpM2EV82r+3jooWh+EOBwewI8z2Cy7haHT6IArGJzedUv34mwYes47Gfw3cidDZ9p0z/vbbto7h9pPYPCHtMhgV8pTpqjrBAZnZdzM4BS2d3btzwL+jMHpZX8GPLNrD4ObAX2jq3vz0Lx+sVv2IvDGofbNDP74vwFcxBSnj3bPOwp4AHj6UNvM1xeDINoN/AuD/7LeNIv1M2kZS9S0yGBf8X6nPQI/0722NwM3Aq9d7bKX+v2WqKv5awYc2Y0vdtNPWK6urv2jwH8e6TuT9cXkz4S5vrdaPbzEhCT1nLuGJKnnDAJJ6jmDQJJ6ziCQpJ4zCCSp5wwCSeo5g0CSeu7/AViTTBlXUjp+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of the 'Odometer (KM)' column\n",
    "car_sales[\"Odometer (KM)\"].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115a36b00>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of the 'Price' column with 20 bins\n",
    "car_sales[\"Price\"].plot.hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've seen a few examples of plotting directly from DataFrames using the `car_sales` dataset.\n",
    "\n",
    "Let's try using a different dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import \"../data/heart-disease.csv\" and save it to the variable \"heart_disease\"\n",
    "heart_disease = pd.read_csv(\"../data/heart-disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
       "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
       "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
       "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
       "9   57    1   2       150   168    0        1      174      0      1.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  \n",
       "5   0     1       1  \n",
       "6   0     2       1  \n",
       "7   0     3       1  \n",
       "8   0     3       1  \n",
       "9   0     2       1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the first 10 rows of the heart_disease DataFrame\n",
    "heart_disease.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of the \"age\" column with 50 bins\n",
    "heart_disease[\"age\"].plot.hist(bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call plot.hist() on the heart_disease DataFrame and toggle the\n",
    "# \"subplots\" parameter to True\n",
    "heart_disease.plot.hist(subplots=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That plot looks pretty squished. Let's change the figsize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x2160 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call the same line of code from above except change the \"figsize\" parameter\n",
    "# to be (10, 30)\n",
    "heart_disease.plot.hist(figsize=(10, 30), subplots=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try comparing two variables versus the target variable.\n",
    "\n",
    "More specifially we'll see how age and cholesterol combined effect the target in **patients over 50 years old**.\n",
    "\n",
    "For this next challenge, we're going to be replicating the following plot:\n",
    "\n",
    "<img src=\"../images/matplotlib-heart-disease-chol-age-plot.png\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAGDCAYAAACFuAwbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzddXicZdbH8e+ZJDNJmgo1rKUFihanFHcWWNzdfWEFdykOy7IsLC8Oi7uWxbe4tRSHYqWFGnVLO8noef+YJyVNpmkmTfKkye9zXXNl5n6euefMZJI5c6u5OyIiIiISnkjYAYiIiIh0dErIREREREKmhExEREQkZErIREREREKmhExEREQkZErIREREREKmhExEFjCzlcxsnpkVhR1LazKzIWb28BLc/xcz26mJ93UzG9DUx24NS/r6NDcze9vMTgg7DpHmpIRMpAXk+4A2s2PM7P0WfMwGP9iDx88ECdc8MxtrZv8xs9VrznH3ce5e4e6ZlopzaWRmXczsX2Y2LnjtRge3e4YdGyxZQtgMj72dmU0I47FF2hMlZCJLOTMrLuD0j9y9AugK7ARUAZ+a2TotElw7YGZRYBgwENgV6AJsAcwABocYWrOwHH0WiIRMf4QiITGzFczsGTObFrRW/bXWscFm9pGZzTaz38zs1iAxqDnuZnaamf0E/GRm7waHvgxacA5u6LHdPePuP7v7qcA7wJCg3v5B3cXB7WPMbIyZVQYxHl4rhuPM7Dszm2Vmr5lZv1rHbjaz8WY218w+NbOt6zy3kcGxKWb2z1rHNjOzD4Pn/aWZbdfA63e+mf0cxDbKzPatdewYM3vfzP4RxDfWzP5Y6/jKZvZOcN83gIZauo4CVgL2dfdR7p5196nufqW7v1zrvA3M7Cszm2NmT5hZaa3HOzFoVZtpZkPNbIVFPKdYEPO44LW5w8zKgmM9zey/wWsz08zeM7OImT0UxPdi8Ls/d3GvZdDld7WZfQDEgVWC9+PQoO7RZnZiA69Joyzm+XxnZnvUOrfYzKab2UaLi7/OYwwIfpdzgvs/saRxi4TC3XXRRZdmvgC/ADvVKTsGeD+4HgE+BS4FosAqwBhgl+D4xsBmQDHQH/gOOL1WXQ68AXQHymqVDWggpgWPX6f8OGBKcL1/UE8x0AmYC6wRHFseGBhc3wcYDawVnHsx8GGtOo8AegTHzgImA6XBsY+AI4PrFcBmwfUVybU67Ra8Pn8IbvdaxPM5EFghOPdgYD6wfK3nmgJOBIqAPwGTAKsVwz+BGLANUAk8vIjHeRx4oBG/7xFBPN2D39cpwbEdgOnARsHj/Rt4t87vckBw/V/A0KCOzsCLwLXBsWuBO4CS4LJ1refzC7Xeb4t7LYG3gXHkWv2Kg/reAW4DSoENgGnAjsH5Qxp4fbYDJiziWEPP51LgkVrn7g58X0D8JwTXHwMuCs4rBbYK++9fF12aclELmUjLeT74dj/bzGaT+7CrsQm5D5cr3D3p7mOAu4FDANz9U3f/2N3T7v4LcCewbZ36r3X3me5etYRxTiL3gZlPFljHzMrc/Td3/zYoPzl4/O/cPQ1cQ66FqF8Q/8PuPiOI/0ZyicgawX1TwAAz6+nu89z946D8COBld3/Zc61QbwAjyX0o1+PuT7n7pODcJ4CfWLgL8Vd3v9tz4+EeIJdQLmtmK5F7/S9x94S7v0suUViUHsBvDRyvcUsQz8ygvg2C8sOB+9z9M3dPABcAm5tZ/9p3NjMjl0CeEfxeK8m9rocEp6SC59DP3VPu/p67L2oz4sa8lve7+7fB7285YCvgPHevdvcvgHuAIxvxvPNqxPN5FNjLzMqD24cFZY2Nv0YK6AesEMTeYuM0RVqSEjKRlrOPu3eruQCn1jrWD1ihTsJ2IbAsgJmtHnRPTTazueQ+yOp2q41vpjhXBGbWLXT3+eRank4BfjOzl8xszVrx31wr9pmABXVhZmcFXVJzguNda8V/PLA68L2ZfVKr26ofcGCd12QrcklIPWZ2lJl9UevcdVj4NZpc67nEg6sV5FqxZgXPr8avDbw+MxYVQx2Ta12PB49F8HgL6nf3eUGdK9a5fy+gnNyYvprn9GpQDnADuVbJ1y3XjXx+A7E05rWs/f5ZAahJmmr8mifGQjT4fNx9NLmWxD2DpGwvfk/ICnkvnEvuvTfCzL41s+OWIGaR0BQyGFhEms94YKy7r7aI47cDnwOHunulmZ0OHFDnnEW1jhRqX+C9fAfc/TXgtWDcz1XkWvG2Jhf/1e7+SN37WG682HnAjsC37p41s1nkPjRx95+AQy03kHw/4Gkz6xHU+ZC7L3bsUtASd3fwGB+5e8bMvqh5jMX4DVjGzDrVSspWYtGv5/+Aq+qcX4hJ5BKMmtg7kWt1m1jnvOnkJlkMdPe6xwiSpbOAs8xsIPCWmX3i7sPyxN6Y17L2fSYB3c2sc62kbKU8MRaiwecTeAw4lFzjwKggSYMC3gvuPplcSxxmthXwPzN7t1ZdIksFtZCJhGMEMNfMzjOzMjMrMrN1zGyT4HhncuO35gWtUn9qRJ1TyI1FW6zg8VY2s3+TGwN0eZ5zljWzvYIEIgHMA2qWw7gDuCBIDDCzrmZ2YK3Y0+TGIBWb2aXkZibW1HuEmfVy9ywwOyjOAA+Tay3ZJYiv1HJLKvTJ8xQ6kUsopgV1HkuuhWyx3P1Xct1fl5tZNPgQ37OBuzxELkF4xszWtNxA+h5mdqGZ5e1OreNR4Fgz28DMYuRaO4cHXdG148qSSzJvMrPewfNa0cx2Ca7vEQxgN3LvjQy//z7q/u4LeS1x9/HAh8C1wbnrkWvJrJdwL0pwvwUXcr+fRT6fwOPAzuTe34/WKm90/GZ2YK3yWcHjatkWWeooIRMJQTCuaU9y44zGkmtNuIdc1x7A2eTG1FSS+1BrzMyxIcADQRfPQYs4Z3Mzm0fuA/1tconSJu7+dZ5zI+RaZCaR65LclqDb1d2fA64HHg+6VL8BamYxvga8AvxIrturmoW7x3YFvg3iuBk4JBj7Mx7Ym1zX7bTgPueQ5/+Uu48CbiQ3OH8KsC7wQQOvTV2HAZsGz+sy4MFFnRiM+9oJ+J7cRIq55BLqnsDwxT1Q0IJ1CfAMuda5Vfl9HFVd55Hrlvw4eF3/x+9j71YLbs8j97xvc/e3g2PXAhcHv/uzC3ktazmU3KSOScBzwGXB2K3GWJFca1jty6qLeT64+2/Bc9mCWu/xAuPfBBgevJ+GAn9z97GNjFukzaiZoSMiIiIiIVELmYiIiEjIlJCJiIiIhEwJmYiIiEjIlJCJiIiIhEwJmYiIiEjIluqFYXv27On9+/cPOwwRERGRxfr000+nu3uvfMeW6oSsf//+jBw5MuwwRERERBbLzBa5TZu6LEVERERCpoRMREREJGRKyERERERCtlSPIRMREZGOIZVKMWHCBKqrq8MOZbFKS0vp06cPJSUljb6PEjIRERFp8yZMmEDnzp3p378/ZhZ2OIvk7syYMYMJEyaw8sorN/p+6rIUERGRNq+6upoePXq06WQMwMzo0aNHwS15SshERERkqdDWk7EaTYlTXZYiIiKy1JoxYwY77rgjAJMnT6aoqIhevXJrr44YMYJoNNrsj/nZZ58xdepUdt1112arUwmZiEgH5Z4GipaaVgeRfHr06MEXX3wBwJAhQ6ioqODss89u9P0zmQxFRUUFPeZnn33GN99806wJmbosRUQ6mGzVq2SnbodPGYhP3ZTs/Htx97DDEml2e+65JxtvvDEDBw7knnvuASCdTtOtWzcuvvhiBg8ezIgRIxg6dChrrLEGW2+9NX/5y1/YZ599AJg3bx7HHHMMgwcPZsMNN+TFF1+kqqqKK664gkceeYQNNtiAp59+ulliVQuZiEgH4om3Yc65QDDg2GdD5S24Z7CKk8IMTaTZPfDAA3Tv3p14PM6gQYPYf//96dy5M3PmzGGjjTbiqquuIh6Ps/rqq/PBBx+w0korcdBBBy24/xVXXMGuu+7K/fffz6xZs9h000356quvuPTSS/nmm2/417/+1WyxqoVMRKQD8cqbWJCMLVAF8+/EPRNGSCIt5qabbmL99ddn8803Z8KECfz8888ARKNR9t13XwBGjRrFGmusQb9+/TAzDj300AX3f/3117n66qvZYIMN2H777amurmbcuHEtEqtayEREOpLM+PzlXg1eCdatdeMRaSH/+9//ePfdd/n4448pKytjq622WrAURVlZ2YKxkw1117s7zz//PKuuuupC5e+++26zx6sWMhGRjqR4lfzlVg7WuXVjEWlBc+bMoXv37pSVlfHtt9/yySef5D1v4MCB/PDDD4wfPx5354knnlhwbJddduGWW25ZcPvzzz8HoHPnzlRWVjZrvErIREQ6EKs4CyitU1oGFX/FrLCZZiJt2e677048Hmf99dfniiuuYNNNN817Xnl5Obfeeis77bQTW2+9NSussAJdu3YF4LLLLiMej7PuuusycOBAhgwZAsAOO+zAl19+yYYbbthsg/ptaZ5ZM2jQIB85cmTYYYiILFU88T5eeR2kx0KkF1T8hUj5/mGHJdKg7777jrXWWqtF6p43bx4VFRW4OyeffDLrrrsuf/nLX5aoznzxmtmn7j4o3/kaQyYi0sFYbCss9t+wwxBpM26//XYeeeQREokEgwYN4sQTT2z1GJSQiYiISId2zjnncM4554Qag8aQiYiIiIRMCZmIiIhIyJSQiYiIiIRMCZmIiIhIyJSQiYiIiIRMCZmIiIhIyJSQiYiIiDTSq6++yhprrMGAAQO47rrrmq1eJWQiIiLS7gx79D0O7/8ndi46iMP7/4lhj763xHVmMhlOO+00XnnlFUaNGsVjjz3GqFGjmiFaJWQiIiLSzgx79D1uOukOpo6bjrszddx0bjrpjiVOykaMGMGAAQNYZZVViEajHHLIIbzwwgvNErMSMhEREWlX7rvwURLx5EJliXiS+y58dInqnThxIn379l1wu0+fPkycOHGJ6qyhhExERETalWnjZxRU3ljuXq/MzJaozhpKyERERKRd6dW3R0HljdWnTx/Gjx+/4PaECRNYYYUVlqjOGkrIREREpF057prDiJVHFyqLlUc57prDlqjeTTbZhJ9++omxY8eSTCZ5/PHH2WuvvZaozhrFzVKLiIiISBux42FbA7mxZNPGz6BX3x4cd81hC8qbqri4mFtvvZVddtmFTCbDcccdx8CBA5sjZCVkIiIi0v7seNjWS5yA5bPbbrux2267NXu96rIUERERCZkSMhEREZGQKSETERERCZkSMhEREZGQKSETERERCZkSMhEREZGQKSETERERCZkSMhEREZGQKSETERERaaTjjjuO3r17s8466zRrvUrIREREpN3JxoeSnbod2clr5H7GhzZLvccccwyvvvpqs9RVmxIyERERaVey8aEw92LITgI893Puxc2SlG2zzTZ07959yYOsQwmZiIiItC/z/glU1ymsDsrbJiVkIiIi0r5kfyusvA1QQiYiIiLtS2T5wsrbACVkIiIi0r5UnAmU1iksDcrbJiVkIiIi0q5EyveCLldBZAXAcj+7XJUrX0KHHnoom2++OT/88AN9+vTh3nvvXfKAgeJmqUVERESkDYmU7wXNkIDV9dhjjzV7naAWMhEREZHQKSETERERCVmLJmRm9ouZfW1mX5jZyKCsu5m9YWY/BT+XCcrNzG4xs9Fm9pWZbdSSsYmIiIi0Fa3RQra9u2/g7oOC2+cDw9x9NWBYcBvgj8BqweUk4PZWiE1ERESWEu4edgiN0pQ4w+iy3Bt4ILj+ALBPrfIHPedjoJuZtd0FQ0RERKTVlJaWMmPGjDaflLk7M2bMoLS07rIbDWvpWZYOvG5mDtzp7ncBy7r7bwDu/puZ9Q7OXREYX+u+E4KytrusroiIiLSKPn36MGHCBKZNmxZ2KItVWlpKnz59CrpPSydkW7r7pCDpesPMvm/gXMtTVi8NNrOTyHVpstJKKzVPlCIiItKmlZSUsPLKK4cdRotp0S5Ld58U/JwKPAcMBqbUdEUGP6cGp08A+ta6ex9gUp4673L3Qe4+qFevXi0ZvoiIiEiraLGEzMw6mVnnmuvAzsA3wFDg6OC0o4EXgutDgaOC2ZabAXNqujZFRERE2rOW7LJcFnjOzGoe51F3f9XMPgGeNLPjgXHAgcH5LwO7AaOBOHBsC8YmIiIi0ma0WELm7mOA9fOUzwB2zFPuwGktFY+IiIhIW6WV+kVERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCVhx2ACIi7ZF7GhJv4qlRWPFKUPpHzMrCDktE2iglZCIizcyzc/AZB0N2Cvh8nHKY+3fo8WQuORMRqUNdliIizcwrb4LMePD5QUkcfDY+5/xQ4xKRtksJmYhIc6t+GUjVKcxC6gs8Gw8jIhFp45SQiYg0O2vgUAPHRKTDUkImbd78OfOpnDUv7DBEGq9sDyBapzAC0UEa2C8ieWlQv7RZU8dN4/qjb2XURz+CO/3XWYlzH/gzK6+jQdHStlnFGXjyE8iMA0+AlYJ1wrpeF3ZoItJGmbuHHUOTDRo0yEeOHBl2GNICMukMR656GjMmzSKbyQK5np5OXTvx0Jj/o6Jbp5AjlLZgyq/TePIfQ/nuox9Zae0+HHzOXqy8br+wwwLAPQvJDyD1HRT3hdiOmNVtNRORjsTMPnX3QfmOqYVM2qThL3/GvNnxBckYgDukkmmGPfoee5+6a4jRSVvw63cT+OvmF5KoSpJJZfj5i7G8/+xwrhx6HhvusG7Y4WEWgdjWuYuIyGJoDJm0SVN+mUY6ma5XnognmDR6cggRSVtz97kPUVVZRSaVASCbdRLxBP865a6QIxMRKZwSMmmTVttoZYqK6789yypKWXPwaiFEJG3N1+9+R74RF1N+mUa8sqr1AxIRWQJKyKRNGrjlmgzYaGWipSULyoqjxXRffhm22m9wiJFJW9GpW3ne8qLiyELvGxGRpUGLJ2RmVmRmn5vZf4PbK5vZcDP7ycyesGCUq5nFgtujg+P9Wzo2abvMjOtevZgDz9qTnit2Z5llu7LHSX/g3x9fQ0lUH7YC+5+xB7Hy2EJl0dISdjx8G4pLNDxWRJYuLT7L0szOBAYBXdx9DzN7EnjW3R83szuAL939djM7FVjP3U8xs0OAfd394Ibq1ixLkY4rm83yf3+7j1fvfZOSWAmpRIpBu2zAhY/+jVhZbPEViIi0soZmWbZoQmZmfYAHgKuBM4E9gWnAcu6eNrPNgSHuvouZvRZc/8jMioHJQC9vIEAlZCIyZ/pcxn8/kWX796ZXnx5hhyMiskhhLnvxL+BcoHNwuwcw291rps9NAFYMrq8IjAcIkrU5wfnTa1doZicBJwGstJIWCBXp6Lr27ELXrbqEHYaIyBJpsTFkZrYHMNXdP61dnOdUb8Sx3wvc73L3Qe4+qFevXs0QqYi0Fs/OJzvnSrJTBpGdsiHZ2WfhmWlhhyUiErqWbCHbEtjLzHYDSoEu5FrMuplZcdBK1geYFJw/AegLTAi6LLsCM1swPhFpRe6Ozzwa0t8DyVxh9Su5LYZ6vaY9HkWkQ2uxFjJ3v8Dd+7h7f+AQ4E13Pxx4CzggOO1o4IXg+tDgNsHxNxsaPyYiS5nUSMiMZkEyBkAasnOh6uWwohIRaRPCWIfsPOBMMxtNbozYvUH5vUCPoPxM4PwQYhORlpL6ATyT50AcT33d6uGIiLQlrbJYj7u/DbwdXB8D1FvZ092rgQNbIx4RCUFxf7Bi8MTC5VYGxQNCCUlEpK3QSv0i0jqiW0CkNwt/DzQghpXtHVJQIiJtgxIyEWkVZhGsx2MQ245cUhaBkkFYjyexSOfF3FtEpH3T/iIi0mos0h1b5jZyk6yzBDuniYh0eErIRKTV5Va2ERGRGuqyFBEREQmZEjIRERGRkCkhExEREQlZgwM5zOzf5NlPsoa7/7XZIxIRERHpYBY3snZkq0QhIiIi0oE1mJC5+wO1b5tZ51yxz2vRqEREREQ6kEaNITOzdczsc+AbYJSZfWpmA1s2NBEREZGOobGD+u8CznT3fu6+EnAWcHfLhSUiIiLScTQ2Ievk7m/V3Ag2C+/UIhGJiIiIdDCNXS57jJldAjwU3D4CGNsyIYmIiIh0LI1tITsO6AU8G1x6Ase2VFAiIiIiHcliW8jMrAi4UGuOiYiIiLSMxbaQuXsG2LgVYhERERHpkBo7huxzMxsKPAXMryl092dbJCoRERGRDqSxCVl3YAawQ60yJzeeTERE6vDEcHzeDZD+CSLLYxV/xcp2CzssEWmjGpWQubsG8IuINJInR+CzTgSqcwWZMfic83GfR6T8oFBjE5G2qbEr9a9uZsPM7Jvg9npmdnHLhiYisnTyyhtYkIwtUA2VN+KeDSMkEWnjGrvsxd3ABUAKwN2/Ag5pqaBERJZq6dH5y31e7iIiUkdjE7Jydx9Rpyzd3MFI+xCvrOLnL39h7szKsENZiCe/JDvzWLJTNyc742A88UHYIUl7VbRC/nKLgmmTExGpr7GD+qeb2arkBvJjZgcAv7VYVLJUcnfuueARnr/lFYqjRaSTabY/bCtOv/0kiksa+1ZrodiSn+Ezj2FBN1J2Bj7rT3jXG4iU7RJmaNIOWcXf8Nlns3C3ZRl0OoHc0o4iIgtrbAvZacCdwJpmNhE4HTilxaKSpdLzt77CC7e+SrI6SXxuFcnqFG8/9gH3XPBI2KHhldeTf0zPNbh7GCFJO2alO0OXyyHSEygGq4CKk7FOfwo7NBFpoxqbkLm770Ru+6Q13X2rAu4rHcRTNwwlEU8sVJaoSvLSnW+QzYY8kDn1Xf7y7FTwqtaNRTqESPm+WK8PsN4jsN4jiVScipn+bYpIfo397/AMgLvPd/eagUFPt0xIsrRa1JixZHWKVCLVytHUUdQrf7mV5i4iLcDMsEiFEjERWawGB/aY2ZrAQKCrme1X61AXQJ9ispA1NhnAV++Mqle+XP/exMpiIURUS6dTofKKOq1hZVB+jD4sRUQkdIv7JFoD2APoBuxZ67IRcGLLhiZLm1NuPJrSTjEiEQPADGLlUf586/EhRwZWth9U/DWY4VZKLhk7HKv4c9ihiYiIYI0Z0Gxmm7v7R60QT0EGDRrkI0eODDsMqeXXUeN55Kpn+PHTMay01oocftH+rLHJgLDDWsA9CdkZEOmOWcitdiIi0qGY2afuPijfscauRbCvmX0LVAGvAusDp7v7w80Uo7QTK60O598+G1LjobgM69S2ZjCaRaFo+bDDEBERWUhjB8/s7O5zyXVfTgBWB85psahkqeSpH/EZe0HVk5D+FqpfwGcegCc/CTu0FuHZeXjVS3jVC3h2ZtjhiIjIUqyxLWQlwc/dgMfcfaaZtVBIsrTyymvB59cqyYJX4XMuxXq9ElpcLcGr38Jnnw4WAXcgg3e5mEj5wWGHJiIiS6HGtpC9aGbfA4OAYWbWi/qrbEpHl/w0f3lmLO7t5+3i2Tm5ZIyqIAGNAwmYexWeHhtydCIisjRqVELm7ucDmwOD3D1F7hNo75YMTJZCkc6LOFDC742s4VvilfmrhwH5WogzeNWLS1a3hM7dtXuDiLS6RiVkZlZObvuk24OiFci1lon8rvwYoKxOYQzK9m8T+/e998zHHLnqaexcdBAHr3Ai/73z9SZ+8CaAfDsPZLTq/1KsctY8rjvq3+xefhi7Rg/hwj9ezeRfpoYdloh0EI3tsvwPkAS2CG5PAK5qkYhkqWWdjoOyvYEoWGcgBrHtsC4XhB0aHw79hOuP/jeTx+Y+YGdOns2dZz3IC//3auGVxbYB8iVypVjpTksUp4TD3Tlru8t458kPSSXSZDNZPn3jS/686QXEK5Vki0jLa2xCtqq7/x1IAbh7Ffn7bKQDMysi0vUKrPe72DJ3YL3eILLMv9vEel//uegxEvHkQmXV8QQPXf5kwa1kVrQiVPyJ3AKzEcDAyqFsNyjZqNliltbz5dvfMnnsVNLJ9IKybNZJxBMMe+S9ECMTkY6isbMsk2ZWRtAsYGarkuu3EanHIt0h2j3sMBby29gpecvnz6miOp6grFNhO4FFKk7FY1vj8eeBFFa6G0Q3pT3OPp4/Zz6v3f823338I/0G9mW3E3ak+3LLhB0WkIvt9QfeZtRHP9Jv7T7sduJOTYpt3HcTyaTrd0NXz08w5qtfmyNUEZEGNTYhu4zcgrB9zewRYEvgmJYKSqS5rbDqcoz9ely98opu5ZSWN60Fz0rWxbquu6ShtWnTJszgtE3OI15ZTSKeIFpawlM3DOXGdy5nwAYrhxrb9IkzOHXQ+cQrq36P7R8vNim2fgP7UFRcv8OgtFOMVdfv30wRi4gsWmNnWb4B7EcuCXuM3GzLt1suLFlaVc2r4sXbX+PG42/jqRuHMndmZdghAXD8NYcRK4suVBYrj3HU5Qe3y1at5nL3uQ8xZ3oliXiuQTxZnSJeWcU/T7wj5Mjg7vMeZs70ufViu/H42xdzz/rW22ZtVlxteUqiv39HjRRFKKsoY8fDt2q2mEVEFqXBvSzNrMEBMe7+WbNHVIClaS9L9yykvweyULxWm5h12NymT5rJaZucT3xunOr5CWJlUUpiJfzrg6vot1afsMPjw6GfcPd5DzNp9GR6rNCdo4YcyK7H7hB2WAB89e4o7sWHgU0AACAASURBVDn/YX4dNYFl+/Xi6MsPZst9BocdFnt3O5r43Hi98qLiCM/NeqDgrt7mtM8yRzN/ziJim3k/ZRV1Z/w2bP6c+dx+5gO8/cT7ZNJZNtl1A/58y/H0XqlXc4UsIh3ckuxleWMDxxxoG59mbZynvsVn/Ql8Lrm5EDHo9i8stlnYoTWrO896gNlT55DN5MbiJKqSJKuT3HTiHfzr/fAn5W6x1yZssdcmYYdRz5fvfMtFu11Doio36WDs1+O49oib+dvtJ/GHI7cNNbZoaQnxufXLzYziknC/VERLS5g/p365mVFU0tjRGL8rLx/Pmdc+yZnXzCb3dzoG67YVoIRMRFpeg12W7r59AxclY43g2Tg+8yjITgaP51Z295n47JPxzPSww2tWw1/+bEEyVsMdvhv+E8lEKqSo2r67z3t4QTJWIxFPcvd5D4e+QOkfT9iBaJ2u3uKSIjbbcxAl0XAX+93thB2Jli4cQ3G0iM32GEQ0Vlhs7ong73RSrb/T2fisU/HM5OYMW1qZuzPhx0mM+epXMplM2OGILFKjvkaaWQnwJ2CboOht4M5g1X5pSGIYeRcR9Sxe9SJWcWyrh9RSihfRKmERo6iosSusdDy/fDM+b/nc6ZVUz68uuOutOR1xyYGM/mwsX707ikgkgjusuNpynHHXyaHFVOOwiw/gx8/G8tU73xKJ5N5fK6zaxNgSbxOs6lNHFq96Fqs4dUlClZCM/2Eil+17A1PHTcMiEUrLY1z46N/YcIf2PRlHlk6Nbde/ndzeN7cFt48Myk5oiaDalewMyJu3JiDbvlYB3/nobXnx9tdJVv/+fItLith8r0EUFbe/MXMAk3+ZSjqVYcUByzV5ckCvPj2Y8OOkeuWx8iixJs4AbS7RWAnXvHwRY7/+lTFfjWP5VXqz1mart4mJENFYCde8dGHzxJadCZ6v9SQJmWlLHKu0vnQqzZnbXsacaXOoaWiunlfNpXtfz33f3UyvPj3CDVCkjsYmZJu4+/q1br9pZl+2REDtTnQweXuGrRyLbd7q4bSkY648lB9G/szoz8aC51rGevfrxel3hN+a0tzG/zCRS/e+nt/GTAWDbr26cOnTZ7P2ZqsXXNdRQw7ixhNuXzBbEHIzQA86e68FLT+F8swkqH4VPA2lO2DFA5pUT42V1+3Hyuv2W6I6WkqzxBZdxNhCK8diWy5Z3RKKT179gmRVkrq9/pl0hlf/8yZHXnJgOIGJLEJjE7KMma3q7j8DmNkqgDrjG8FK1sZLd4TEm7X2OSyD4nUh2r6m05eWx/jn21fwwyejGfPVOFYcsBzrbbt2m2hNaU7JRIrTBp9PVWX1grIZk2Zx1raX8uj4O1mmd9eC6tv+kC2JV8a578LHiFdWEY2VcODZe3Lohfs1Kb5s/GmYezm5eTdZmHcr3uk4Ip1Pb1J9HYEVD8DL/ghVrwK1/07XhNj2YYYmTTRr8mwymfrDRVKJNNPGzwghIpGGNTYhOwd4y8zGkJt+1A9oP4OfWph1vRGqh+LxJ4E0lO6Lle+PWfsbV2VmrDl4NdYcvFrYobSYYQ+9u1AyViOdyvDE9c9zyo1HF1zn7if+gT8evyPzZs+nU5fyJnfxemZ6kIzV3kgjDfPvw0t3xkrWblK9HYF1uRaiW+Hxx4EklO6NlR/ULpeo6QgGbrUm9ZrHgNKKUjbeab0QIhJpWKMSMncfZmarAWuQS8i+d3dtndRIZhEo2wcr2yfsUKQZfPbm14s89vV7o5pcbyQSoUv3zk2+P5BribVInr3Pk3j1S0rIGpD7O90z2Dw+k9sCTJZa/dbqw9YHbMb7zw6nen7u4ypaFqXv6suz5b7hr/EnUlchi/VsDPQP7rO+meHuD7ZIVCJt2AqrLLvIY736hr1mVUPdw+2r67i5eeY3fPZZkMoNj/Wi/li3G5TELsXO+c9pbLD9Ovz3zjdIViXZ4bCt2PvPf1zkjHCRMDW4Uv+Ck8weAlYFvuD3sWPu7n9twdgWa2laqV/aj1lTZnPwiifh2YX/dsyMW4dfy+qDVg0pMvDMDHzadizcZQlQivV4XMnFIrin8Wk7QXYKCw2PtQqs1//UWiYizWJJVuqvMQhY28NepVJahGfnBuswpSG6LVak6eANWWbZbpx+58nccurdZNIZcCgqLuLAs/cMNRkDsKIeeJchMHcICwb1UwSdjlcy1pDk++BzmD/XGf6/bqRTxqDtK+neO43HX2hX6wWKSNvU2ITsG2A54LcWjKVN8cxEfN59kP4KilfHOh2PFa8SdljNLlv1Bsw5CygCc/AM3vkiIp0OCTu0Nm2343dk0M7r8/4zw0kl02yx9yD6rrFi2GEBECnfH49tAdWv5dbAK91xid+77k51PLc/aVOX4mjTMhMZ/kYpV5+8CpGI4w6ZjHHiJZPY+7Rfwo5ORDqAxW0u/iK5r9mdgQ2AEdTqC3H3vVo6wIa0VJelp37CZx4EniS3encREMW634dFN272xwuLZ2fhU7cF6s4YjGE9X8SK+4cQlbQ1rz/4Nvec/whzp8+ltFMph5y/Dwefu0+7Ws6kctoIDu1/PYmqhZPNaGmWW9/bm5U3PiqkyESkPVmSLsuhwLLAe3XKtwUmNkNsbZJXXsP0SQmev7cnP3xezsprVbPvidNZvngI1vPFsMNrPtX/A7M8M/IyuW2dOv8ljKikDXnv2eHccurdJOK5vTbnz4nzyJXP4A6Hnr9vyNE1n49fqcLytPxl0sabzxjHt5/vYSKSRyaToago3CVuFtf3sDcw1N3fqX0BXgba7RoO40d9xUnbrcnz9/Tiq486898He3DKjqvz/ScTcE8uvoKlhSfA8+yzSYb6g8LbB0//jFe9jKe+DTuUpcIDlz6+IBmrUR1P8MT1z5PN5nvvLJ1SyTTu0Xrl2YyRqEqHEJGItIYPnh/Bkauexq4lh7Bfz2N54u/PE9Zw+cW1kPV396/qFrr7SDPr3yIRtQF3DFme+LwI7rkumUw6QiYNt5zXh9t3bT/TpT22DVReme8IRLdu9XhaknsSn/0XSHwEVgxk8OI1sGXuxSJLuPZXOzZl3PS85dXxBFXzqunUpbyVI2oZm+y6AZ6pv/lIrKyYrffbLISI6vPEh3jVU+BJrGwPiO2sRWtbmWdn5RYOTn4KxQOw8sOx4r5hhyVN9OkbX3LtETcv+NJZOXMeD1/xNMnqFEde2vpbay2uhay0gWNlzRlIW/L1R50WJGO1jRlVSjrVfnaMssxY8ufkRVhmfGuH06J83v9B4kOgGnxebhur1Lf43MuaXGcqmeKzYV/zyaufUx1vny2KK62Zf6JC52UqKO/cfv4F9FyhlKPOnUysNEsk4pg5peUZtt1rOgM3a+jfYOvIzr0Bn/UnqH4JEm/gcy7AZ5+K523hlpbgmd/wabvCvNsg+S7EH8Jn7IEnPws7NGmi+y/J3wPw1D+Gkk61fsv44pp7PjGzE9397tqFZnY88GlDdzSzUuBdIBY8ztPufpmZrQw8DnQHPgOOdPekmcWAB8ktQDsDONjdf2nCc1pipRUVJKrm1isviZY0eUubNik9lvyLhWbw9E/taxnR+BPU74ZNQfVruKcxK6zl8+v3vuPSfa4nG+yVl81kOec/p7HNAe1rw/gTrz+Ci/e4lkTV7/+0YuVRjrvm0HY1qJ/EWxx46hw22mYmw55ehmQiwjZ7zmbdzRJQ/QKUhLcPqKfHQfxBFnr/ehySwyH5AcQKb82eN3s+r9w7jFEf/Ui/tfuwx8l/oOeKWu6mIV75D/A55JaSAUiBp/A5F2K9Xg0zNGmiiaMn5y3PpDPMnVFJ9+WWadV4FvcpdDrwnJkdzu8J2CAgCixuRG8C2MHd55lZCfC+mb0CnAnc5O6Pm9kdwPHA7cHPWe4+wMwOAa4HDm7Ss1pCOxy6Jc/d8kq98sG7bdikKf+ercQr/w7V/wWyENsJ63wBVtSzGaJdAsWrgZUEs0lrsXKsZM1wYmox9feezMkCaQrZtCJeWcVFe1xTbz/Lvx99K6sPWpXl+vducpRtzQbbr8OVL57P3ec9zLjvJtKrbw+Oufxgtj1oi7BDa14+DzzDqgOrWXVgndV9spXhxFQj+RF5vzh5HK9+EyswIZs+cQanDjqPeGUViXiS4S99yrP/eokb3hzCGiGvo9emJd7l92Sslsw4PDsHi3Rt9ZBkyfRbuw/fvP99vfKSWAlde3Zp9XgazC7cfYq7bwFcDvwSXC53983dPX9q+ft93d3nBTdLgosDOwBPB+UP8PvkgL2D2wTHd7SQvoJ/N/ynvOWjP/+l4Lrcs/jMw6DqWfD5ua6y6lfwGfsT+nag0c2hqA+5X02NIrAuUPrHsKJqGdGtyft2L16LXGNu4334wid5ZqZCJpNl2CPvNi2+NmzDHdbltk+u57/zHuY/393c/pIxgOhW5P2lUo6V7tja0SzMKiDvWLFiiBT+oXH3+Y8wZ3rlgq6aVCJN1bxq/nnC7UsYaDtnixovaWCxVg1FmsexVx1KrGzhyTylnWIcdtH+ofSGNaq5x93fcvd/B5c3G1u5mRWZ2RfAVOAN4GdgtrvXdM5OAGoGqawIjA8eLw3MAeq1oZvZSWY20sxGTps2rbGhFOSHEaPzlk8eO5VkosBZlsmPIDOB3HpmNdK5pu/qcJu5zSJY90egbJ/gn00MYrtgPZ4uOElpKe5ZPPUNnvyC3982hbPO54F15fdhkVGwTljXqwquKz63ikyeAeDpZJrKWfObHGNHkc1m+fHTnxn18Y+hjNPIx4r7QKfjwMr4vTWqHGJb5r64hCm2PfmHFhRhZfsVXN2Ilz9b0NVe26+jJjB/brzw+DqK8sOpP6y6BGI7tJn/l1KY9bZZmyteOI9V1u9HcUkRvfr04OQbj+bAs/YMJZ4WnTLo7hlgAzPrBjwHrJXvtOBnvv849b6yuvtdwF2QWxi2mUKt8xiLPpZJZ3Kj4hor/WNutfR6DxLHU99jZXsXHF9zskgXrOvV0PXqUOPIx1Pf4LNOyXUnYUAxdLsJi21VcF1W3Ad6vYbHn8htHl28OlZ+CFa0XMF1bbTTuuR7u5Z2ijH4jxsVXF9LGPPVrwx7+F1SqTTb7L8ZA7dcs02M+Rr9+Vgu2es65s+JY2ZEiiNc+Mjf2GTXDcMOjUjnM/DYVnjV07mZjKW7Bx+24b5uFimHZe7O/S3U7LPpGeh6FVbcr+D6YmVR5uX54mARo7ikHY2RbWbW6Tg8/T1UvxEM9chAyeq5/5+y1Npop/W48/N/hB0G0MIJWQ13n21mbwObAd3MrDhoBesDTApOmwD0BSZYboR1V2Bma8RXV0U3Z97s+v+ES6JOrKzApumi/vnHaVGOFWu8xqK4V+EzjwZfePyOzz4Ner6OFS1bcJ0W6YZVnLzEsfVZfQV2P3EnXrl3GNXzc93OpZ1ibLjjumy4wzpLXP+SevIfQ3nwsidya2tlnVfuGcaOR2zD3247MdTkIlGV4JydLq+XDFx+wI385/ub6dUn/EHlFt0Ei24Sdhj1WHRj6P0hJEfk/pdEB2ORTk2qa/eT/8AT1z2/0ESN4pIiNt1to8L/v3UgZsVYt3/i6fGQ/h6K+mAl+doYRJqmxTalM7NeQcsYZlYG7AR8B7wFHBCcdjTwQnB9aHCb4PibYW1mftpVk4hEFn5oizhHnj0ZswK7WGLbQKQHC+e+EYiUQeluSxxru1U9jLwDaD2DVw1t9XDq+tNNx3DpU2exzQGbsflegzj7vtO47JmzQ29NmTp+em4x16ok2Uw2twfl/ATDHn6XUR/9GGpsH//3MzLp+r/TbCbD6w++3foBLWXMSrDYlljp9k1OxgAOOW8fNvrDesTKopR1LqO0U4z+66zEmfec0ozRtl9W3Bcr/YOSMWl2LdlCtjzwgOVWLowAT7r7f81sFPC4mV0FfA7cG5x/L/CQmY0m1zIW2u7WOxxYTjY7jruvWIHZM4qp6JLhyLMns/fxaRYeAL94ZkXQ/fHceleJt8gturoZ1uWKXFdEG+DukBkHpKFolSVKKr4f8RP/ueRxxnz5KysMWI6jhxzERjutV3hF2Vm5KeUOk36JkkkbfQckMEtCNv9ipYXFtixHDzm4abEBZsYmu27YrF1t8coqfhszhV59e9Cle9MWq/3klc/zbgFUHU/w/nPDGbjFGksaZpPNnT6XbLr+2LtUIs2syXNCiKhjKomWcMXz5/HrdxMY8+WvLL9Kb9bYZEDoXyZEOroWS8iCFf7rfVq5+xhgcJ7yaqD1l8bNp9Pf2OnAi9npgFGkkkZxiWNWBp3/2qR/WlbUE1vm/8gNqfOC17xqSZ4ejc86DTK/AQaRrtDtZixaeKLxzQffc/4uVy6YvTV76hwu3ed6znvwr2y936aFVRYdzK8/lnLlCSszZUKUSMTp3C3DBXdMYZ0/bFlwbN9++APn7XwliWAB1wWxPfAXtt4/3JXY3Z3/XPwYz9z0EsXRIlKJNDscthWn33ESxSWFvVeKo8VYpP57tKgoQrS0sC8TzW297QbmLS+rKGXQzuu3cjTSb60+9FurT9hhiEigxbosl2aR8j2h84Vg3SmJFuW21qn4M1Z+3BLVa1bUtpIxT5D87XCeunUex2/dj2O36MeD1zvxCcfh2cKH79159oP1Vj1OxJPcceb9Be8NlsqszNn7r8qEn2MkqyNUx4uYNinKhYf2ZdbsdQuO7a5zHlyQjNWO7fYmxNbcXrz9NZ69+WWS1Unic6tIJVK8/fgH3H3ewwXXtcXem+B5ZtAVlRSzw2HhbofVb60+bH/oVpR2+n2cUqw8ymobrcImf9wgxMg6nrFf/8qQ/W/gsH6ncPYOQ/jirW/CDkmkw2s72UEbEyk/GC87MLcitpVj1v5yV6/6H5cd3YNvPi4jUZ17fk/d1puPX+/Gv997jpJuxxdU39ivfs1bPn3iTJLVyYIGDH/04qekklFyDae/y2ajvPno+xxwRmHTkn/+8pe85TN/m0V1PEFZp8KnrU+bMIP3nx1OOpVh870G0We15QuuA+CJG4bWTxarkrx01xuc9PcjC1oPp/MyFVzwyN+49vCbiRRFcHcymSwnXn94m2gNOfPuUxi08/q8dNcbpBJpdjxiG3Y5djuKijS7r7WM/nwsZ2xzCYl4Endn2vgZfD/iJ869/8/tbqcJkaWJErIGmEVyizK2Qalkipm/zaZb7y5Nnhn1/fCf+Gb478kYQDIRYeLYEj5+6Ue2Pryw+pZZrhuTx06tVx4rj1ISK6y7bNbk2aQS9SdQJKuSTJ8wo7DAgO7LdeO3MfVji5ZF6y0M2Biv3T+MW069C/cMnoX7L3mYQ87fhyMvPbTguuZOr79NF+TWNUtWJymrKGzPyC33GczjE+9i2KPvkUqk2OHQrVp9C5BFMTO2PWiL9rm4bB41f6dde3WhtLxtzGC869yHFswOrpGIJ7nt9P+w9f6baSyZSEjaX7NPO+fuPH79c+zf8zhOGHgG+/c8jrvOfSjvQqWL8/3nFSSr6//zrY4X8c2IwmdxHXbRfvU+dGLlMfY/Y8+Ct5xae4vV8w4At4gxcMvCZzcddtH++WM7fY+CY5s1ZTa3/OkOktVZUgkjnTKS1VmeuP4ZRn/xc8GxrbnpannLe/XtSWkTWu4m/zKVC3e7mjvPfID7LnyMc3a8nNGfjy24npYya+ocXrrrDZ6/9RUm/1I/SW4vnr7pvxzQ63hOGHgGB/Q6jtvP+E9uHcOQ/fBJ/oWv50yvZN5sLWwsEhYlZEuZV+4dxsNXPkPVvGqq4wkSVUmG3vYaD1/5TMF1JdM9cc+/Hm9VvPB9Nnc9dgeOHHIg5Z3LiJVHiZXH2PvPu3LEJfsXXBcAi/im3pTv77scsz1HXX7QwrGdtgtHXHrA4u9cx0fPDyVSVH+cVioJ7zz2eMH1nfyPoyjtFCMSDMY3yy3e+ZdbTyi4tSKdSnPG1pfww4jRpJJpUokU476byFnbX8bcGSHvyQi8/eQHHLHyqdx+5gPcfe5DHL/26Tx+/XNhh9Xs3njoHe6/5HHilVUL/k5fuvt/3HfxY2GHRvfluuUtLyqOUFahFedFwqKEbCnz6DXP5hmcnuDZm/5LNptn3a4GFBUt6tdvFEcL78YzMw46e2+ennYv9313M89Ov48TrzuiSeODRn34Y96xU551vvnguybFduBZey0c2/VHNik2T43Lv5uDg6cn5TnQsAEbrMytI65ju0O2ZMXVlmfT3TfmhjeHMPiPhc90HfHK58yfW0U2u3CA6VSGNx56p+D6mtPcGZXccOxtJKuSJOIJktUpktUpHr7iacYsYvzh0urRq/P9nSYZeuurTW4l8+xcvOo5PP4knmlwK+EGHXrBfsTqthaXRdn9xJ0KntUrIs1Hf31LmVmTZ+ctr44nSCfTREsbn0j17tuTWKcoifkLz4wsjhXTZ/WmDVCH3DpHvfsW3sJWW/fluwVLQCy87VS0LEqvJai7qLiITl3KKFqCLWI23X0gt53zeb3ykpiz9b5N232h31p9uODhvzU5phpTx00nk2d/yGRVMu/4vtb04dCRC1oBa0sl07z52Pussl7h2wC1VTN+m5W3PJ3KbeRd0a2wIQFe/SY++wxy7cMOXIl3PoNIp8Jnfv/hqG2ZMelXHr3mZfAMmYyx0xHrc+Lfjyy4LgjWMax+BY8/DiSgdC+s/EDMCv9SJ9KRqYVsKbPK+v3zlvfs073ggfOb7zWIWFmsXs9gSUkxOx4e7hIJm+05iGgsWi+2oqJIk2N75d5hHLTcCRzQ+3j263Esj17zTJOWvOjRf3dOvWo20ViW4miWoqIs0dIs+508i9U3P3rxFbSgNTYZQCRPy2dZRSlrbx7eorBAbkPrPC+3Z71NjK1qTqtttHLe8i49u9Cpa2ELQnt2Lj77dKAKiAc/E1D5Lzz1feHBZcZy8HG38NTX33DbG9/z5Nff8tfLH6YoO7zwugCfewk+5wJIfQypz6HyenzmUeR2xxORxlJCtpQ5+R9HEStf+JtnrCzKqTcdW/B4o2hplH++fTl91liRWFluXFWvvj257rWL6dqzS3OGXbBorIQb37mcvmvmYivtlIvt2lcvpluvrgXX99bjH3DrX+5jzvRK0qkM8blVPHLVMzzx9+cLrsssym5/u4u736/mmPOmc+Q5M/n3qzM59rp/YEVN34/R3UkmUku0Ltqagwew9uZrEK01c7QkVkyvvj3Yar966zG3qk133yhvt3q0LNrulls48fojiJUv/GUnVh7llH8eXfgsxsSb5P9XncSrXyw4Nq+8AXwe0dIUK66cpLwiDVTjcy8t+L3n6bF4/AU+eq2ES47sz3kHrsJrj5eRrvo+iFtEGsvCXhRzSQwaNMhHjhwZdhit7odPRue2APrqV1ZcbTmOHnIwG2zftE2tPTMdn3spv/34AemU02et9Yl0vRorXqmZo26638ZMIZ1K02f1FZo8Jf+IVU5jSp4ZfaUVMV6Y/WDBMy1reLpmy6mVl2i5gGGPvsfd5z7EzMmzqejWicMu2o/9T9+jSXWmkimevvFFXrn3TdKpNNsdvCWHX7Qfnbo2ff/D5jL0tle585yHyKQyZLNZoqUl7H7iTvzppmPDDq3Z/fTZGO6/5HF++nwsy6/Sm6MuO4iN/1D4jgQefwqvvAq8qs4Rg/KjiHS5qKD6slM2Bs83waMY6/0xFmn8lzGPP8Gtf72b1x/vQnU8NwygtDzDGhvEuXboYEq6X1FQbCLtnZl96u6D8h5TQtZxuWfw6btCZiJQ070QAeuG9Rq2RBsYtzW7FB9Ub6B7jaGVDzVpYVhgQYvCkiRj7z83nOuOvGWhXQ5i5SUcNeQQDjp7rybX21ZN+HESbz/xAalEmi33HczqGzdt3F1H4Zkp+LSdgESdI2VY93uw6CYF1Zeduj1kJ+Y5EsWW/aygsV8Tvn2Kkzd5nGT1wl9oSjtluODe9djioCEFxSbS3jWUkKnLsiNLvhds1F17rEcWvBqqXworqhbR3F875s6s5Nojbma3ssP4Y+wQLtrjGqb8Oq1Jdd1/8UN5tpxK8dg1TxU8c3Zp0Gf1FTjikgM59qpDmyUZmzj6N957dvgid2MIk6fH4dWv4anCZwbXsKJlofPZQClQRG5gfxmU7QElef+vN6zTsbn7LyQGZXsUPBD/yw86512dpnp+EZ+8lX95jdaWSqb49I0v+ejFkcQr67YyyqK4p/DEB7kJJdl5YYfTIWiWZUeW/gU8ledAHE//3KT1vtqqim6dqJxZ/59KUXER0QInQ2SzWc7c5lIm/vQb6VRuMPrI177kL5tdwAOjby24tW3yL1PyllfNq6J6foLyzoWt1N/Wffn2t7z6nzdJVafY/tCt2HyvQU3qMk6n0lxz+M0Mf+kzikuKyKSzrLJeP6595cLQu2fdU/jssyDxFlgJeAYvWQNb5p6CugRrRDodjUe3wKuHgiew0p2hZOMmtcxa+RF45heIPwkWA09CbEusy2UF19WlRzcixaVAnZna0Qjdejd9pnZz+eaD77lkr+tyE0qATCrD6XeexE5HbBtyZG2bJz/HZ50EBJNtPI13GUKkfL9Q42rv1ELWkRWvDhSRTsGXH3bis3drVu4vxUrWDju6ZnXAmXvUm4VaXFLEzkdvV9BekQCfD/uaqeOnL0jGIDeDsGpeNW899kHBsfUdEM9bXtE1Tay8fc1Uu/3M+zn3D1fwv4fe5Z2nPuKKA2/k4j2ubdJEhseue44RL31Gsiq3KXsinmD0Z2P41yl3tUDkhfH5d0PibSABPg+ogtQofM4lTa7TSlYj0vksIl0uxKKDmtxNbhYh0uVSrPe72DJ3Yr1eJ7LMHZgVnvhvuvtGFBXV/0JTVFTMzsds16T4mkt1PMFFu1/DvFnzic+tyr1HqpLcdPJdTPix8PUCOwr3anzW8eBzcu9dnwdUw9wheDr/Lg/SPJSQdWBeMphvJBgJvAAAIABJREFUhsc4ZP2BDDlmZa48sT8HrTuQj16L/T975x0eRdXF4ffO9k0jofeugghKk6oIqCgKduz6ib1hRSyfvaGCvYCKn4IgiqiIiiIiHQSkSJXepZO6fe73x4RAshNwJgkb4L7PE9E77uFkd3bmzLnn/A7S0yXR7pUqffpfRJc+HXB5XCSl+XF7XZzWvTl3vmm9mHzTiq1Ew/EyDcHcEOuWbLRsr+/j2/B4C29Nenw6/xnwD4JjJyDbuuYfxr75Q0G2AoxAdu6Ehfzx45+W7Y3/YCKhQOHMTCQcZfo3fxAJm2V+jyB5I4FgkcUwhH5FyrDZK444QsswAjuH/UyW2+tm4MT/kl6tAr4UL/5UH74UL49+3o/q9auWorfWmfPDn6aBfiwa4+f/TbZlU+o56LnD0ffdh579NjJmnt0+qglNwbzII4IMHHtTNcoTasvyKESGphut69G14KiBSLkf4e1h2U5gzzSeuLY2gdzCcflLd9ZhWMfxVGlkcbp4OcbhdND/f3dz0wtXsWHZZmo0rEb1BvZuGHWa1sLpihet9SZ5aWhD3LTlmV6eHLaej56rzua1HipXj3Ddw//Q9eI8hGZd4qO88t27E4ot5hvzxg+c3rOVJXtFB2TvR+qSaDiKy21tKxpASh0ifwFhcLWwL24a1xFZcMDYIjyGRFNPaNWQLzYPYcUfq4kEIzRpf4LlMoCyIC8rD2nSyBOLxMjZa31mp4ztQO6+BPRsDC04NzJvGGQMR7jsdbmXS/RczL+oMdAzj7Q3xxUqIDvKkKFpyL13UfD0HVuH3NcfmZpneX9/5rczMPvixWLw6+d/cPVT1gMyqeciA+MguhgcjRD+SxFa+SjuBahUsyKVatrXCgM49ayTqVa/CptWbiUaNjJYmkPDn+rjzD4drBtMe57WXe6kdZe/D1rUILk/Qhw7SWyzGr795GVav0G2PrcF07+eHdc9W6dpLXzJ1rffZGQJcu/tIHMpmJiaNgjhPcuyLdydITQBKNKU4WyA0JKt2yvnaJpG03YnJNqNQrTs3rxQNnY/3mQvHXpb60wFkDmDQd/DgSaoMMgwMnMAotL4kjlbnvC0hyyTzLzwIzzdjrw/xxHHztW+lJGhOei7L0ff3hJ9Vy9k0F6Ku7SR2a8SvxUShJxBlutwsrNSiUXj61CiYUH2vhTrvsW2I3edC9kvQ+BryHkTubN7uak7WDx1Gf06Pk7vCtdza4sHmTluri07mqYxeMqzdL2qEx6fG6fbSfsLW/POnJdsyWdo3q6ICu+BVhMQICpB6nNoydbH4pQVWbuzWTpzJbu27rFt4/SeLYs91vzMky3bu/WV60jOSC4QwXW6HfiSvTww9DbLtqQMIvfcCPoOIyDLr52R+/ohY2YSEYdGpD4MIg3YPzPSZdzQUl+wbEthj6p1K3PZgxfiPWhupzfJQ4szm9LqHOt6cAR/A7MSguhapJ5l39FyhnBUh6RbMDpx8+8Pwm909HpUM0RZojJkJsjQTONJeX/gE11hXJjTXkTzXZBQ34iuM1/Xd2PoFP37gKBVj4sY9sTUuHWvX9L2gostuyazX873Y399VRBkCJn5BKLiF5btlSYLJy/hiQteKqg5WvfXRl68+g3uG3Ib3a85w7K95ApJXP5QLyrWSCcaidH50nZUrmU/8ya8XRHerrZfX1bous57933CTx9NwuVxEQlFaHt+Sx4dca+luamGLYkQwvTBwaotMG64nyx/k/FDfmHZrL+p27Q2ve86lyp1Klu2RWgyB87bg4kh875BpNxtyZxw1ITKE4z5jpEF4GyE8F9jrCuOGP957ipadmvOTx9PIpQXosuVneh0SVt7QtDCU8yWuzA6aY8htJR+SE97ZN6XIAMIb0/wnntMZezLIyogM0FmD8Q0C5X9CtLbs0QioCXGUR1i6+PXRQoHnsb/HXWb1qH7DZ2YNHw6wTzjd/L6dU49qymndi0+m1Esod+Jv6lJiCxEynBChw1/+MjwuALwUF6YD/uPoNvVnS1/pmMGf8///vsFkXAUqUvGvfczPW46i7vf6luabtsiZ18OT138Kkumr0BKSf1T6vL02IdsFVl/89aPTBg2mXAwQjho1Mz98eOfvHf/J9z3vrVMVM6eHFweZ4Gdg8nNNO80PRypFVO4+rFLbb22EPo+kGYBWST/IcM6QktHJN9RMr8UJaZFl5Np0cV6BjYOfx/IGUJhgV4nuDva6lAt7wh3W4Q7sePWjjdUuGtGdK35ur4DSHD3VnI/4kUdfZB8h+WgQkrJPc/N4dH3ttC+xz7adM3i/te28eSHczFNzR+W4p4SNeyeaptXbWPki1/z2dOjWb2wmOzgv2DDss2m65k7MwnmmReHF8fOzbv55IlRhAJh9JiOlJJQXogJwyazfM4q2z6WBrquc239u1g8ZZnhmy5Zu2g9N53Uj9ws60HP16+PJ1Tk/QkHI0z8dArRiLVzpMVZzTBTEfUle2nT41TLvpUq7tPN14Uf4bGeQVUce4ikW8HTAfAaW3jCb9QEVng50a4pjhFUhswMrZL5aBHhp/igo3jCoQhjBn/Pz8MmE4vF6HpVJ64ccLEtwU/N1xNdGjVj6HtBJEPy7Qi/jVmAkfmI2HLanZNHu3MOzgLkQXAi+M63Zs93EeSNIu4J0tMNIayfat+9+xND+48gFo0hdcmXr43jorvP4+aXr7Vsq1LNimxZtS1u3ZvkxeOzlrmb88OfCJMtj3AgzPSxs2lyemPL/pUWE4b9ZpptikZiDHt8FPe8bS2DV1w3WiyqEwlFcLr+/eda56SadL/uDH77fFpBh6TH7+HENo1KFJAF80L8s24HlWpmkFzBniCscDZA+i6C4DiQ+98/HzhPgRIEZMG8ENvX76BiDfu+lRXKN2sI4UakD0FGVkF0GThqgatlYndMFMcUKiAzQ6taTEBW0VYW6vHzX2TZ7L8J52+ZjRk8njk//Ml78wZaFiUF0PyXIn2XYGyreu1fECJLQJplOfKQkUUIiwGZSLkfGVkCkaX5CwIcNRFp1gcM79qym6EPDy+0vRXKC/PtOz9x5hUdaNyygSV71z91OYNvHVIo2+Pxe+jTv7flehKny2E6LkZzCFtSC6XJvF8WFXvsr6nLLNtr1qkJ835eQNGyr2r1q9jqZLzv/VtpfXYLfvxoEpFQhG7XdObs68+0VdMjpWT4s1/x5avj0ByCaDhG92s7c+97t1gKFPcjUp8BT8f8upkwwtcLfBchhPXvqJSSEc+NYfQr3xX41u2azvR7355vpYmUkhHPj2H0wAO+db26E/3evyXh56+Uks9f+JovXv623Pm2H+FqDK7EPXQpjl1UQGZGbKX5ur4JKSMICwWcy2b9zYo/VhUEYwCRUIRta7cz6/t5dLq4mK2Sw2AEYSWsW3DUzB/rUlSo0odw1rHhkw8yPofIYoiuBEddcLe1FTDOHv8nQot/XSQYYeqYWZYDsq5XdyYvJ8gnj48kkB3E5XFx+UMXcuUA680LHXq34e17Po5bd7icnHVVR8v2SpO6TWoxrZhjNRpVs2zvtteuY8mMFYQDYWLRGJomcHnd3PveLbb8E0LQ+dJ2dL60na3XH8yPH/3KV69+SyjvQND+28ip+JK93PG69YyxEMIoXPaeW2Lffvp4El++8l2hB4DJo6bjS/Zy5xs2stmlyM+fTObLgYV9+/2LGXiTPAmvgfzlf78zeuC38b75PdxtMburUBxtqBoyU4oLIMQhjpmzYs4qYpH4YuFATpBls/42ecURxNPF2PIsehoIF3gvtGlU5o/cyDL+tKk0rzk0hNl7LQQOh/WMBcAFt57NV9s/5outQxm7+xOu/e/ltoLF1IopDPjsHjw+N94kDx6/G7fXRd8Xr6Ju09q2fCstetxUvGbWuTd0sWyvbtPaDFn4Kuf17Uqj0+pzZp+OvDXzBVp2O6UEXpYOX7z8FcG8wjWdoUCUH4b+QixqVqB/5Bg98Lu42sRQIMyPH/5qufautPni5W9Mffvp498SPuFg1MvfxAn+hgJhfhqWeN8UirLmqM6Qrd2ZS58hswqtXdC8Ote1r0cgHOPGT/6Ie81lrWpxeeva7MkNc8eI+XHHr21Xl571erJ1xyQenNDnoCMCtDRuOWs33ZtWZc3OHB4b+1fc6+/p2phOjSuxdGsmz36/jMxcF5suP6dAoLDi1Pn4tu4k1qAGX/oymFTE/ycvbMrJNdKYvmoXb/8WXyD+4iWn0LByMr8u286H0+KbD17vcyo1Kvj4ftFWRszeEHf8/WtbkZHk5qt5mxgzfzPwnNHEoBuincMu+x1/xZcZMWcP4xcvjXv96NvaAzB06homLd9R6JjXJfmk10sQ28zbszszc1MQWAOupoCbdL+bD64z1NgHTljBnxv2Fnp99TQvb1x5GgDTUzJYc1HXQkrbrr2Z1J4yjzP7dODRsYtZu7NwfVPTGqk8daHRTXXfFwvYllm4U7Zl3XQe6XESqRkp3D58PnvzCmcGOzaqxL3djK2IG4b9QbBIIN2tSRVuPaMhAO/sknhfuYPMXZlIqZNaMY3c1kZW0c65FwqEqLRsLfr0xWScXIe/Tz8VX5Eaw1s6NzjsuZc15S9iNauwrXN8l+yEmatp36sN8zfs4ZUJ8VngQ557p57Ci89eXXDuPVrkvAU7515h/veftvjcDobPWs/4xfH1fkXPvSUdTkfPTzCLaJSaY34F4J+WTekzdFahwN3KuffM90tZtrWwrlSDykm8dElzgH917s1r3xK9zQFRUu/WHVSa+iexaIzbh88nO1JYsNTKuVf0mgfWrntz27VEb134709buIL0tZtZvy2TJybEX3f+zbl38HWvKP17nEiruhmHPfc2uT1svTJ+6kj1yX8QyAky5Z89pXTdK4zVc+9gvC4Hn95kdCO+NWkVM1bvKnT8SJ97xV33gBJf90p67hV3z72wRQ227gtw/+iFcceP1Ll35O65xaMyZCaIlP7gqA1Cw9CYcRijTpz1LdtKrZSC5tDiEmuaQ6NClfIwFscDzibgPg3cp6FV/Arhsqm4HV1n/MhcIGbICMgIRNdb98pvZJ+KklY5lTpNyoOWUwgHf5ORsYaKFdfhEittz7UL5gZZNX8dS2euZNOKLcyfuIjVi9aTvbd4ZfviEAI0p1lmseAfxwz+5BhmwlD+5Bg2k6ilRnENO+lVK+B0JdY5f6q5bxWqpOFP9R9hbwpT56Rapuup6UmkpB97Ew4UioMRVtXdyxOtW7eW8+bNKxPbUurI0DSI/gWOxgivvU5BgC2rt/HCVW+wfskmEFC9fhUe/bwfjU61HuAByOgGZO6Hxsw9ZyNE0q0I14m2bJUm+vbWxlZlHA5E1cWWau9W/bmW+894Mk5ywZfsZcCIe+nQy/rok9JCyghyZ7d8GZSDMg0iBVH5N8vzJ/uf/SwLJsU/+VVvUJVPV71taVt1745Mrq13R5zWl8fv5o1pz9PoNHvnXHnk79/O4cFeyYSCGlIXgMTjlTz2wVbaXzfN8vdV13Umj5rB+CG/EAlF6XZNJ3redo6tuYyjXhrLsMdHxa13uqQtT4152LK9/b79MHQi4WDE8O3Ws20J6q5ZtJ77Oj1BOBAuGDvl8bt5dEQ/Ol6UWN2ptYs30K/TE4TzQgd887kZMOJe2/W2AFLPA/RjcmyV4uhCCDFfStna7NhRvWVZlsi8kZDzpjEkWLiQsVsgybrWF0DNRtV5b+5A9m7fRywaK9EsRRlZgdxzJcgQEIPoSmTwV0gfivDYv2CVDvFz4w5gLfBfOmOl6Ry6QE6QxVOWJjQgIzQVZDZxv6+MIAPfIZKut2Ru+WzzWsIdG3cRzA1a6mZMr5LGve/dwlt3foiUIHUdzengsgcvLDfBWG5mLnN++JNIOErb804jvaq9WaeN2t3Mmz+8yuevp7FqkZ8a9UNcc98+Tu7S29bD02t932PamNkFNUzrl27k99EzGTzlWcvd0BM/m2K6Pu/nRcSiMcv2Bt38PlO/mlXIt8lfzOT1qdZ9a9iiHu/MeYkRz41h5dw11Gxcjasfu5RTOjexZKcsaNC8Lu/MeYnPnxvDij9WU6NRNa553L5vMrYTmTkAwsZWm3SegEh7GeE6qTTdVihKBRWQmaDnfQPZrwIBY0GGIXcIEici+Vbbdu3eeA5GZr90kE4SGEFBAJn1NKLyTyW2XyK850BgHIUL+TVwt7Gs0p9erQJOt4NIqHCmx+11UbFGRsl9LQmxLcZWbBxBiG20bC61YkpcITMYsxntZEDOvfEsTut2CtPGzCYaidG+V2vqnGR/m1fGtiFzPzkwAiipL8LZyJat2ePn8/yVr6NpAolEj+rc+up19L7rPMu2hO9y6rXaweMffGiUF8iIIVORYj0DtWHZJqZ8OatQN3QoL8y6vzYy54c/LQ+j3rV1r+l6NBIjkBO0pK21YflmpoyeWWjSRCgvzLolG213atdtWpvHR91v+XVHgrpNavHYyPtKbEdKHbnnGohtpuCaFF1mrFX+FaGll/jvUChKE1VDZkbuOxQEY/uRAcgdanmAd6kTXmC+HluHlNYU50sbo/auOrC/DsUPWrqtgcrtL2xlqjukORx0v87+gNs1i9Yz8bMpLJ250v5n6Wpm1BXG4Ue4rAucXv5QLzz+wvVyHp+b8/p2s6VTB1CldiUuvf8C+vTvXbJgLLoWuasn5I2AyCIIfIvcfSkyHF+8ezhy9uXyfJ/BhPJCBHKCBHNChIMRPuw/gg3LD13saoYQAi35bkTV2YiKYxBVZqGlPW9pa3w/i6cuN62wC+QEmTexeG234mh0aj3T9dSMZJLSrNVp/TV1uWn5XzAnyJ+/Lrbs23FDeBboO4nr9JYRZN7YhLikUBwKFZCZEdthvi5zSPjoJC21mANu7EwR2E8sFiUWK1k7vtAyEJV+QqS9CEm3I9KeNGqqnNalINxeN69NfpqajdLw+MDrh4rVfbz44wDSbTRDhINhBpz7HP06PsHbd3/EgB7Pc0er/rYK53GdBs7mFH6/HeCoamQJLdL7rh70vqsHbq8Lf6oPl9dF58vaceur11n3DUNcUwZ/Q997B/qevsjAOKSpAPC/sJU1ML9JY//rYyADyMwnLduaNW6e0eBShGgkyqQR8UPu/y1C+BDOhggtxbaNClXS0EyCX5fHScXq1jMpNw+8Fo+/cHbT43dz26DrLZc9VKiSair1Yte3owVpOlvUArFNxcwnDUKsmPF4CkUCUVuWZjgbQHR5/LpWhZIEPaWC/wbIKZrB84L/CoSwHl/v3b6Bt297nJk/BpE6nHamk35DHqN6o+a23BPCDb7zEVgcu2RC3dof8/HUuWxZEyMaFdRprKH53kTKIZZvap89/SV/TVteqNh9w9JNvHH7UP47+gFLtoQQSEc9iBRp4dZqYOcrJYSgUcv6pFZKYdfmPaRkJHFi64a2Fd1l1rMQGMv+c0SG5xlbyelDrZ8jkT8wrf+LbUDquQjt32+9hYNhdD2+LlCPybjB70ea03u2xOV2FM2LozkcnGNDv61puxMYNPkZhj0xijUL11OtXhWuf/oK2p53mmVbbc9vidPjhGwT324sXnfuaEUP/Aw5L0NsC1JUKBgNZ7l+19XUaDuOO319CJf1z0GhKGtUl6UJMjQLufc2jNFE+/FC2ktovp6l/vdZQUo9/4b7tSHFIUPg7Y5Ie8VynVY0GuGWJn3YtkEQixo3ak3TSc2QfLrmY/wpiXv6ltHVyF0XU3guJsaw5wrvITwdLNm7rMpNZO7Kjlt3uhx8nzPCUvBzaN/eRXisqfXPHDeXF69+g9BB+kBev4cbn7+SS++7wJItY4uxdzG+vYPwdLJkT9/RGXQzOQ8PouqflrYHd27ezY0n3BPXAepN8vDST4/TrFNii8rX/bWB//YeSObOLIRmjMF6bGQ/Wp3dIqF+AaxbspEnew9k347MAt8e/bwfrc+x51vmrix+GDqRpTNWUqdpLXrf1YNq9aqUstfWkaEpyL33UPja64PkO9GSb7NmS0rk3hsh/CcHvg9O0KogKv9kTBZRKI4wqsvSIsLTHjI+Qma/BtE14KiFSL4P4U3806gQGiLtaWRKP0Pfy1EL4ahsy9b8H0ex+58DwRiArmsE83SmfP4h593ev5S8tkFoFqaZGZmHDE21HJCFQ+ZbzboujW5OK4nP0Gzz9QLfrAVknzw+qlAwBsZw5RHPjeHie8+3NucxPAvTgiOZhwxNsRyQ4b/eJCPrAd+Flmu1KteqyPXP9GH4018SCUWQUuLxe+jSpyMnd0x811v9U+oyfM27rF+ykUg4SsMW9WzX8JU29ZvV4bPV77B+yUbCoSiNTrXv246NO7mz9SMEcoKEgxH+/PUvxn/wCwMnPknTdjY1CEsJmf06hYMxgIDRVJXU11L3rBAC0ocic96HwBijOct7NiL5ARWMKcolKiArBuFui6j4ZaLdMEXq+5B5Y4wia+eJ4L/SVlC2ecUaoiZxSjBPY/2SeMXhI4kUKRRbryf+/TbZfhq2qMeS6Svi1ivVqmi9k1FLBeHkr9lOfvkinWhEo8tFe2nbLYjQrHfSbltnXrMYyA4SzA0VKzJqikgzGg7iYlkX2PBNJN1E9p61TBg2g2Xzkqh7YpAL+jakUpP/WrYF0Ofh3rQ6uzmTRkwlHIpwxmXtaX5GU1tyMmWBEIL6p9RNtBumlJZvHz4yguw9OQU6X9FIlGgkyuBbPuCjvwaX2H6JiG0yX5dhQ2pGWMvaC+FBpNwHKSXv2lQoyhoVkB0BpJ6NzH4FguMBHTzdESmPIhyVrNuKbkbuvtTo+iQIod+ReZ9AxkjL2jr1TjkJp2sJkSLlO94knYanWhveXepoKRSrXSasq4mbbVcC5GXmIqW0FhB4uvHJS2/yzYdphIMaUgpmTkil3dm5PPpVL8u+1TqhEmsWbolbT67gwZfstWbM0xUwK7h3IHwXWfZt5+a93NV2J4HsWoQCEf6YVIFvPgoz+Pd/bOuaNTq1vm1R5KLI0HRk9kBjQoSjKiTdg+a3/nseT8z7eVFBMHYwm//eSm5mLklp1h94Sg1HbYjGj78BF4jiGpoUimMD1WVZxhhaOFcbRdYy1wikgj8Z0gE2ZCoMHbJMDqT1wyBzkFlPWLZ16jmXU62ujtN9oNDa4ZAkp+qccfUtlu2VJiK2hWKfF/R/LNvbsXGn6XogJxg3aPlwbFuXzdihVQgFHEhpBHLBPAezJ2bw14x9ln276fEAHm/hYnePL8YNj+RYzhwJzY9I/9jIJIgkY3i8SEJUGIRwWJe/GPrwcLJ2ZxMKGNnKSChKIDvAoFvet2xrPzKyGD3zWfTMJ5ChGbblR2RoBnLvnRBdCYSN7ErWU+i5I237djxgNpIMjAycy8ZUglJFFJPF1ZIQplIzCsWxgwrIyprwLAJZW3j/ycpcfvLJXNrkZAY/WM3I2AQn2LA3HVNF/MgSpLTWqaaJEK+N3Ui3S/fiS4rh9up07LmPt35ch8eTd3gDZYmzgdG0EIcf4bQ+Jqo4tXvNoeH1m9+gimP+L4swq9MKBqLMHh8/PPdwtO70B098uJ46JwRxOHWq1g5xz0tb6HnNMqQs2vd3eIKRJgwZeDuXNzuFS05qyuBHb2Jflr2ROHMnLDSdmLBu8QYCuUVrfQ6PnvM+cve1EBgJgS+R++5EZg6wFZTJ7EGY1hvlvImUh5oacXxz4Z3nxklyuNxOOvRuY0uIuFSJLjVf1/cgdfMst0JxrKC2LMsYGVnJw5fUYP0KD5GwEf9OGpPO4pnJDP1jGV5fb4sWPcSJ1gLgyP+xQOhXktN0Hhi0mQcGHSzM6UYGxpVoKkGJcXcwZCRiGzhQS6aBlgRe65Ia0Yi5DlcsGkOP6ZYKpD1+D5GwiT0Jbp+dG5qHtt320LbbyiLrLqx+plJKHjn7WVYvWF8w5eDXz2ewcPJyhi1/w/IN1+NzkWcynlRomuUh2TK2FXLeo1AHqAxAaAJELgO3xXFYsXXF/EXZRjZa2NclK4+sXTCFX4ePIRKM0unSsznlrIusNXzkc/mDF/LzJ5PZuvpAptnj93DH6zeWorc2Ed78HYC4A2BD8FehOJpQGbJikOG56LuvRN/eGn3XJciQ+Wy6w7FwuovNaw4EYwDRiMa+XU5m/mDj5u2/HCMoOxgXeM+1ntLXM4sZARQG3Xz0y5FCCA1RcSR4e2CI3jrAcyai4lcIzXoNmdloIjA0sEyDq0OQVjkVaVKDA5CUat03/H0w/Uw9Z1uWMlkyfQXrlmwqNHIqFomRuTubKV/Osuza+bd0jwsynW4nHXq1Np2kcEhCUzHvAA0igxMt+4ajlvm68NmqM5QyhJ79BvqOTujb26FnPoVM8PdgP2MGPsm9Hd/i67e38d2QnTzR+3MG/+cWU123wzH+g1/YXWS0UzgY5pMn4oehH3H8VwJF6yZdxndfWKynVCiOMlRAZoIMzUbu6QuRP0FmQXQJcu896AHrsyLXLk8jGom/CQVyHaxaakPnK+lu0IoMJxdJkGK9hgx3e0xPAeFHeDpbt1fKCK0CWoVBaNWWIKouQ0sfgnDUsGeruFosYWzXWGHbmu04iskO7dqy26priOS7wNMO8OZ3kPrAeRIi7VnLttYsWo8ejVcnD+YE+Xv+Gsv2rnniUk7r2gyPz40vxYc3yUODU+pw3xBrmlCAkf0wveQ48o9ZNJd8H/E3bx8k3Wb54URKidxzM+R+DPoOkHsg8BVy92W2R5KFQxF+GzWdDx76jB+GTiQ3y14ZwK5NK/nk6aWEghp6TCClIJinMeXrffw1+VvL9r5+4wdCReomw8EIk0fNKFYe5kghkm4FTycKvgvCb8xPTbM+fk2hONpQW5YmyOyXia9NCUL2y0hvD0uF1jUaVsfp8REJF7bnTXJT+4Q61p3L+xz0PUUcDkLu+5D6uCVTwtUY6bsQgj8cNLDcB67W+cEnemgfAAAgAElEQVRa+aGksgjFCdQLIYiEo5a2LKs3rGrqj+bUqNXYesAohBuR/iEyuhoiK8BZB5yn2PqdazSshsPlhKLiq34PtU607pvL7eL57x9lw7JNrF28kRoNq3JC64b2Pg9PN+ApkwNOhOWtexDebsi0lyD7FaPRQ6QVqLpbJrIIIospLKgbBX03BH8E38WWzGXtzuaedo+yd3smgZwg3iQPHz82krdmvkCtE6x9Dn+MH4vmiM/IhgKC6WN+pEW3SyzZy9mba7oudZ1wIIw7gYX9QrgQ6e8ho2sgstzounQ1LzeyKApFWaIyZGZEV5uv69uxOsvy9PNbkprhLXRBFZrE7XXQ5UprAqIA5P0P02Axb7StQmaR+jwi7RVwdwF3B0Tas4j0D465C2CLM042Xa99Yg3LRf11mtYiarLNqUd1ajSqass/AOFshPBdgCjBDajVOc2pUDm1UIAphMDlddH9GvtZz7pNa3PWlR05sU0j274JLQWR/raR9RBJGEPoPZD6OMLZ0JZNzdcTrcoURNW/EFXmoCXdZM+/6FKKFSIOWx8uPuzxkezYuItAjvFdDeaGyNmby6v/edeyLZdzF2a/ktDA5TYp8DsMLbqcbGqvSp1KlgeflxXC2dD4LrhbHHPXIoWiOFRAZkbRLcH9CD9WZ1lqjgivf7uElmdk43BKHA7JyW1yeWPcCnxJ1jvo0Iu7AIexM/hcCIHwnoOWMRQt438IX29LathliZRh9JyP0Xf2QN/ZDT37TaRu/nR/OG4bdD3+FF/BVuP+7sp+71tvXBj//s/FHhv3ro3OWUBGlhnDwHecib7nBmR4ri07DoeDN6Y/R6tzmuNwOXA4NZq2P4E3pj+fWH2pfITnDETlGYi0F43gv8oUNH+fktsV7pLduB21DEHdOLzgrGfZ3NQxs4lGCm8dSylZOXeN5e7U088/EV2P/91cLkm3K6xrGfZ9uiL+5BhOl/EAp2k6Hp/OvYO8KvhRKBJI+bjzljcc1UHfFr8uKlq/YAUnUbFakBc+3004KJASPD4JeJGB7xFJN1iz524B4TkmPtdDCGuZnvKMMYfuDgjPpSAjmPsRMjQJKn5teWxP3aa1Gbp4EF++No4Vc1ZR7+TaXPFwL+o2rW3Zt52b9hR7bPc26zpkMrIYufs6jN9TQngbcs8CZNpgNF93y/YyqqXzwvjHCIci6DHdcgawrBFaEnjPS7QbhXF3Ai0dYkHgoEBKuGwJ6jocxT/rapq1a0hK1a489sHHvHh7LTRNIiXoMcGNA7bToJX1B4paNUbxwW/bGPNBZZbP91O7UYjL79hJ/SarkDJsuZFEoVCUDiogMyNaVH4gH30TUkasBQP6zoJORrf34C2RIMSsC5yKlMeQe64y6sbQMZKcbkTq05ZtlRWRcIQ92/ZRoUoqHp/NYCCyGMLzKLw9G4LYRgj9Bt5zLZusWrcy97zd154/B9G6RzMmfzGN+I5ByWldG1m2J7MGsuFvnc9fr82qxX5qNwxyVb8dNGnzPNLbzXbWIpG1QEcbQjggYyQy85H8hwDA2RiR9oqtcVjdrjuDce/+XKjbVXNonHrWyZa/E8LZiHYXnsGo0ycye6KbSEijTdcQFWs3zK/Ls4i+hyo1I9z53NYiB1yGDIkKyBSKhKACMlOKuwGKQxwrBtdp5rMFhR9hVXcJEK4myIyxkP28ETg66kLKYwh3M8u2ShspJV++No7PnxuD1CVSSnrddS59X7oGh8OiJEdkEYUyFQV/SR4yPB9hIyArLc66OMygvhI9BgfOB+MDvvhW69vGqxes4sGLGhMOCnRdY+s6NwtnpPDE0I2cflWerdmdCusIRzVExqdIPQeIIbQ027ZuePoKlkxfwYZlm4mFozg9TlIyknlo2J32fEsbSLKnI92u/AJkCLy9EElX2ysvcJ0G4Wnx61olNZ5IoUggKiAzw3s+BL6hcE2WA9ydrV8AXc3B3Q5Cszkg6OoFZ2PwnGnZNannQOb9hmCqjBjdkZn3IjNG2xowXshuCW9CE4b9xvBnvirUUj/uvV/w+Dzc8IzFOiFHVUMIMm76gBdsjAAqTdYtXo2mGdtGBxA4nDrLZq2ls8UxoB8+W51gnsaB4E4QCgjefbwmba/yWH0EUJQQoSWX2IYv2cfbs15k8ZRlrFm0nuoNqtL2vNMsdfMW8klo4LvI1vZpnK2U/sg98w/KsgN4EalPqRoyhSKBqKJ+E0TKw+Cog6FxJIw/tSqItOes2xICUeFdSOkPzibgOAGS70VkDLc1m01mvwbRNfkyFRFDkTz2j61ZlmCop+u7r0XuOB25oz36rosN+QUbjHxxbJy+USgvxNdvjLcuYOk5yxD4LBqOCAfCZ32Ad2ky/3eHqbZcLKox4wfrn+mKBSa/J7Bzq4tQXmJ1oRT2EULQosvJXNKvJ+0vbG07GCtthOtERMWx4L0QHPXA3QWR8SnC2zXRrikUxzUqQ2aKE0jC2IZy5P/psz26Q0qNyd/W4cePTkWP6nS/ri7n/seJ04654PcYHZUHE4XQVKSMWsrgSRlB7r7SEMLc/6QcXWasVZ6M0KyNntn7j3lBeygvTCQUsVQ7I4TbqOnZd58RgAI4qiEqDEZoNgR1S5EdW1yYb11L9uy0LnCaWimDYG788HOn221zFJPCDlJKpnw5k/FDjNqvbtd04bybu1qfSFCGvv3w4a+EA2G6XXMGPfp2tV0nuHNrCl+91oRlszRqN6nFFQ9l0KB5SX0MgYzZmqShUChUQGaKzB4MsRUcCHyioG9EZj6JSH/Hsr2B17/NzO/mFozvWb1gHVO+msXLPz9hYxZdcZkmeYhjxRCaYsz9K/Q6CUSMwM9/tSVzDVrUZfnsVXHrlWpl2BpaLJz1EJW+Rcb+AWKg1SgXWyr1Ti6uM1NQ5yTrXZt9HurA0P7fEAocOBc8Pp3zb2pmvfZOYZtBN7/BlNEzCeZrJK9ZuJrJoyby2u8DE/45vH7rECZ/Mb3gGrJm0Xp+GzWdQb8/bdm3zX9v5a62AwgHwkQjMVYvWMf0sbN59ttHaNndelQmY7uQWY9BaDogka6TEakvIVyNLdtSKI5n1JalGcFvMc9C/YaU1uYerlm0nhnf/lFolmIwL8TyOauYP3Gxdd88ZxMfR2vgbmO9XT222XyWpQwgoxssu3bbazfgKSKx4PG7uWPwjSUKpISjGsJRs1wEYwAderfC5YoXEXW5dbpfa71Ro+eVk7nklp14vDr+5Bguj06X3nu5ecDvpeBt+UJKiQxORN99M/ru65F5Y5Gm81SPLBuWbeT3UTMKgjGAUABWL1zHnPHTE+cYsHHFFiaNnFboGhLKC7Nm0Xpmfz/fsr2h/YcTyA4W6KTpuiSUF+aN24cipfmM1uKQUkfuuSY/GIsCMYj8hdxzFVK3LgGjUBzPqIDMFJPuPsDIJFm7YC36fSl6LP41wZwgC3/7y7JnIuWR/IL3/dsCfhAVEKk2Zr25TgbTLU4/wmX9SfnkDicyaPLTtDqnBelV02jW6SSe/W4AnS4+3bpvZYSM/I0MfI8ML7J889lPpcpLueWpnbi9OppDRwiJ26tz4X/2cUIzG59pdD43DviH0X8tZfB3qxm1YBkPDN6MU9uI1O3NPyxt1i/dxG8jp7Hij1W23zcAmfkoct89EJkKkdnIrMeQe661NWWiNFn823jMvtvBXI35P31/5B06iMVTlplukAdzgsz/1fpD3eIpy0w/w52bdpGbafF8C8/KL3k4+EFVggwjA9bnbCoUxzNqy9IMz1kQnEDhwEwDV2vLgqRplVJxuhyF9IgA3F4XFapa1zcSjopQ6WcI/oyMLEM4G4D3fENs0yqu1kjHicz9eQMTRqYQiwq6XppNp16pOL1nW7cHnNimES9PeAIpZbnJaIGh+i/33gnhP/JlSHRwNoSM/yE0i63+0Y30vmk3rc7czZRxFYiEBR3Py6Jx8wDE1lt3TqSAzMOXpFO/ycG6a05bmlBSSuZOWMiEYZOIhKJ0u6YznS9rZ2vbLRyK8Mylr7Fo8hI0p4bUJbVPqsnAX/5LSrq1bkQ9shqC31A48NEhsgAZ+gXh7WHZv9IiNT0HzWme9UyvlNigOK1SimlDgMvtJMPGNSQ5Pck08BIOzXrNYmwTSLMH2OCB2k+FQvGvUBkyE0TKo4YmD/lZKOEDkYpIe96yrQ4XtUFzxAcmQhN0u7qTPf+EG+G7EC31EYT/cnvBGEYX2HvPdOWF2+oy46cKzJ6YxuAH6/D87WdgdUTUfqaOmcV1De7iHOcV9KlxC+OH/FKijEppIXPezp9wEDQ6UwlAdCUy60nrxlxNQDip1TDMNffv4MZHthvBmPAjXOYzMw+J/0bAx5a1bqb/mMbaZV7AY8gc2NCZev/+//HcFYOY9vUcZo+fz6Cb3+epi1+19Tl8/vwYFk5eQigQJpAdJJgbYt1fG3n9tiGWbREYRbEZ5pwR1u2VIu0u7ILTCUX90xxw9vXtE+LTfk7v2bJg5NfBaE6Nc26wLp1z6f0XxJUWuL0uzrqyo/UmAWcTzBtc7GXZFYrjGRWQmSAcVRCVfkakPgG+qyD5YUTlSQgbM+28fhf/eWwPQkiMi73xc9ntu0ivar0jDyBzVxYjXxrLk70H8skTo9i1ZbctOxuWb2bCsKkE8w5sFwVzdf6ctJzFU5ZZtjdz3FxeufEd/lm/AyTs+WcfHzz4GePeszffsVTJ+woIFVmMQHCi9RomV2twngAcnE1wgpZhayRQzH0dz97aitu7n8ig+2tz34WNuf+iZuTxgGVbm1dt44ehEwvXLOaGWPT7UhZMsr6d+tNHkwgHCtdTRsNRZo2bRyRs8X075PartfmOpY07pQ0Dx7qoXCOKNymGLzlGclqM/36cRZXGlyfWN6+bVyc9ReXaFfEle/Gn+khOT+LJrx6iSh3r2oO97+pBj5vOwuVxkZTmx+110fLsFtzzzs3WnXM1N0ofODjAc4KWCr4LrNtTKI5jRHnIXtildevWct68eYl245Dk7PiFqxoMyRf+PIDHq/PxgouoeuJ1luxtW7edu9oOIJQbIhyM4PI4cbpdDJ7yDI1OrW/J1rdv/8SHjwwnHCx8YxUCLnuwF7e+Ys23m5vdz4Zlm+PWUyulMGb7xwndwtS3n5afGSuKhqi6ECGsBcdSz0PmvAWBb4EoeM9FpDyI0DIs+/bZM1/y5SvfEToo8HG5nXS+rB2Pjuhnydb37//MkIc+K2RrP5fc15M7Bt9oyd6FqdcRzIkPljSH4Nt9n+FL+vfvmx6aDntvMj+Y8ghaUsnHWpUEKQPo2W+x5o/xRCM6jdp0xpX+sFEmUA6QUrJm4XrCoQgntGqA01WyipPMXVlsWrGFKnUrU6W29SHlB/wKGhnovK8xvgvdEckPIRz2bSoUxypCiPlSytZmx8osQyaEqC2EmCyEWC6EWCqE6Je/niGEmCiEWJX/Z3r+uhBCvCWEWC2EWCyEaFlWvh1JZnwzB7NtmpgOk76w3iH1wQOfkrs3tyCIioSiBLIDvHHbUMu2ktL8OEyu6U63Rkq69W3Qf9btMF3P2ZtrGiAcUVwdzdcdJ1kOxgCE5kdLHYBWdTZa1XloaS/YCsYAxn/wS9z7EwlHmTZmNtGIta7epDQ/mjP+a+10Oy3XfAHFngdOt8tSMAYg3B3B0dDkQAWERYmVskAIH47URzih+zSanjcDd6WXy00wBkaJQaPT6tO03QklDsbAqG9t1qlJiYIxwy8vWsrDB30XXlbBmEJhg7LcsowCD0opmwDtgLuEEE2BAcAkKWVjYFL+fwOcBzTO/7kVeL8MfTti5OYkFRmxYxANC3KzrQcC8ycuRtfjA7xV89cQDlnbQmrfq6kxF68IQkTpelUTy75Vb1jVdD0lIxlPogVOncWMW3JWO7J+mHDw9uLBxGJ6gTTBv6V9r9aY1fQ4HBrdrzvDsm85e82yisa2ZSDX2jajEAJR8SujDED4AQ94zkNUGo8QPsu+KRQKxbFEmQVkUsptUso/8/89G1gO1AR6A5/m/2+fAvuHs/UGPpMGs4EKQojqZeXfkaLlWRVMa169fp223axnoTx+88BGczpwmGRGDkWSZwbPDd9CcloUf3IMf0oMX1KMR9/dQuVKUy371vfFa+L88/g93Phsn8R3XAaLkS4ITbesLbcfGVmKnvUCeubTyPAftpsXWp3TAk2Lf3/qn1IHr//fTzcAY4biiz88SkpGMv5UH/5UH94kD/0/vZtq9apY9s2bbP7QoDk0nCaF5odDaMloac+gVV2IVu0vtPQ3EQ7rfu0nkBvkh6ETGdT3Pb58bRxZu7Nt21IoFEcHMrISPWsgeuZTyNDMctE4VhocEdkLIUQ94DRgDlBVSrkNjKBNCLH/alwT2HTQyzbnr207Ej6WFXUabuT8a3YzYVRGwQBprz9G225ZNGu76bCvL8r5fdsx9s2fCQcPBF8ut84Zl51iXdZAz+KUdjmMXryUJXOSiUWh2em5eHwSbIg6trugFY99fh8fPjKcrWu2U7FGOjc804dzbzzLsq1Sx7R+DAxpkyhWvwp6zgeQ8x6GgLBEBr8B7wWQ+rzl4PO2165n0ZSlBXWBTrcTl9vJ/UNvt2RnP806NeHLbR+yZPoKIuEozc9oYmls1cFccGt3Rr86rlBhv8vtpOPFbRM+UmjPP3u5q+0AcvbmEswN4fG5GPnC17w+7TnqN6uTUN8UCkXZoOd+BtmvYVx7dWTwO/B0hbRBiX/wLyFlHpAJIZKBr4H7pJRZh3jDzIcDxtu7FWNLkzp1joaLro/bn91K225Z/DI6g2hU0O2SvbQ7NwtjB9ca1/Rbyrr5OSyYnozTKYnFoOHJQe563o7qfwfI1nC64NROOQcd8CM81tvpATr0bkOH3tbV6sscd3sI/U7ceClnY+sF/bEtkPMuhbo2ZQAC48F3KbitlT9Wq1eFT5a/yfcf/MKK2auo16w2ve7qUaLaHqfLyalnNbP9+v1c9dglrFm8gXk/L8LpcqDHdOo1q0O/928tse2S8uEjI9j7zx5i+QnOUCBCKBBhUN93eWfOwMQ6p1AoSh0Z2wXZr1L42psHwUngmwmeYmqFjxLKNCAThorq18DnUsqx+cvbhRDV87Nj1YH9leCbgYMHAdYCtha1KaUcCgwFo8uyzJwvLbRUhIBWXXJo1SUn7phV3GIKz36Wx8ZVHtav8FKzfoiGzYKAhpRhS+OThLMR0nexMSpKBvIXfeBuB+7Eq+tLGULmDIXAV0AMvD0RyXdbF3IFRMoAZHhe/u8ZYb/oqkh91rpjoamYPz8EkcGJCIsBGRgF1tc+cZl1X8oYl9vFM2P7s2nlFtb9tZEaDavR6DRr3bxlxaxxMwuCsYNZ/edaArlBy00HCnuEQxG+fPU7fvpoEtFwlM6XteP6p68gNSMl0a4pjjXC0wGznaAAMjgBoQIyc4SRCvsYWC6lHHzQoXHADcDL+X9+d9D63UKIL4DTgcz9W5tHNYcKkGx09+0XbK3TOESdxgcXg2vYKQkUqU+D50xkYAzICMLXG7znJTz1K6VE7ukLkUUUPA3ljUSGpkCl763P7XTUBW9PCIzGeJ90o/PSZT1LaWgumb3XDpufafmn9ok1qX1iMY0RCcLlCmP6OQiJ5kjsKKbjiSd7vcyS6SsKOoV/GPorf/y4gI+WDMbtTXAzj+LYQngMXaa4VIx2TFx7y7LLsiNwHdBVCLEw/+d8jEDsbCHEKuDs/P8G+BFYC6wGPgTuLEPfjhjCWZcCxf9CeBGuRtYN+i6isCApgBM83WypugshEN6uaOnvoWV8iPBdgBDWi7VLncifEP2LwmKuYYhth+BEy+Zk3vB8zTD9wE94GjL7Veu+ebsSt/UJgAPhu9C6PYUtzumzD7en8OfgcOq0Pisbt1sFZEeCv+evYcmMlYVkW6LhKHt3ZDLly1kJ9ExxTOI+A/NpH26E7+Ij7U2pU2YZMinldMz3dQC6mfz/ErirrPxJGJ4zQUsDPciBm7gwonmvdSVrkfIAMrIEIvlK+kKAoyYizcbWWz7bN+xk6pjZxKIxOvRuQ52TykEmJLK0mBl5ecjIQoSvpzV7uR8DgSKLQcgbjUwZYCkIFVoFqPAmct99IDSMYcoxSHnUmC2qOCJcO6A+KxduYeUCH1KCpkGlGhEeeF0HYW+cmMIaf89bi+lQ9pwgS2as4Ozr7dWiKhRmCC0JKryL3HcXRnghQUYh5T6Erd2O8oUaLl7GCOGCiqORmY9AeC4gwXUKIu1lhGZdqFMIH2SMhMhiiK4AZ31wtbG9xfjjR7/y7r3DjC1CXTL8mS+5csDFXPdkYsfF4KgBwgWyqKCsz9h+tEqxXaMRjG4dazpYwnsWVJmR3ygQBveZ5UpE9HjAW3UAd75wDR/8N4PVSzxUrx3hlqd2kd5wcMK33I8XqtarjGbS3e32uanZOPEaf4pjD+HpCJXzr70yBJ7OCIf1EWLlERWQHQlkEPRMjIheAz3LWLOJEALcLYyfErB7217evXdY4dFJkRijB35Lx4va0qC5jcCntPCcCSIlvwj/oO0n4bS3LehuAeHZ8euOWrZFSYWWXG7n9e3etpcZ3/xBNBKl/YWtqd7AXLT3aGb1Yo0HejYiFAgjdcje6+KJaxvy2Eg3HXon2rvjg5bdTyGtYgqhvBB67MD31OlycM4NXRLnmOKYRmhJYHWX5ChADRcvY6QMI/dcBdHlFGRjYmuRe65F6pkJ9W3md3MRJoKkkXCUqV8ltv5DCBd6hc/5cVRz7u5xArd3P5GvhpxM2PcpQkuzbi9lQL46/P5TXgBeo6nhGGPSyGlc3/Buhj78GR8/+jk3N7ufkS9+nWi3Sp2h/YcTzDWCsf2EAhHe7TfsmBGKLO84HA4GT32WUzo3wel24nQ7qdesDoMmP0OFyta/pwrF8YzKkJU1od/ys2FFbhAyCoHvIenahLgFlPttnReuHs3cCW5CecZ7N/wVN9MnjOSN6c9ZFsEVrqZQcSwy5wOI/AXOBojkOxCukmt1lSf27cxk8M3vxw2MH/nCWNpd0DqxWc9SZuXcNabru7ftJS87QFKqWTONorSpXKsir/32NDn7colGoioQUyhsojJkZU1sG0izGZNBQ2A0gbTv1RppMhfT5XZy5hXtE+DRAVb9uZa5ExYQyjvQZRkKhNmwdBN//LjAlk3hbIBW4RW0yj+hpb97zAVjALO/n4/miP9aR8JRJn8xPQEelR0VKpvr0TldDssjpxQlJ7lCkgrGFIoSoAKyssbVAsw6+IQf4T7tyPtzEBWrp3P3231xe124PC6cLgdur4s+j1xE/VMSm0lZOmMleiw+WAzkBFn0+5IEeHR0cHAdz8FIKYs9drRy5YCL8BQJvDw+Nz1v6Y7DWQ6kWxQKhcICasuyrHGdBq5TIbwA2F/I7wFHHWP+VoI5r283Wp3dnGlfzyEaidGhd+tyIQCaXq0CTreDSKhwdtHtdVGxRkaCvCpbpJQE80J4fG40zd6z0ukXtOLdfsPi1t1eF2dc3qGkLpYrzuvbjd3b9jJ64HdoDkE0EqPbtZ255ZXrEu1aAVJKozFFeBFCPf8qFIriEUdz8Wvr1q3lvHnzEu3GYZEyjMz9BAJjDL0qXy9E0i1Gp4jClHAwzFW1bydrd3ahdW+Sl8/WvEN6lWNra+TnTycz5KHh5OzNweN3c83jl9Kn/0W26vzGvX4XQx7/Bz0Gui5wuSW9+rq45c2Rx2RQEMwLsX3DTirVSCcprfx8p/S87yDnFdD3GA0lSbcgkm4r97WbCoWi7BBCzJdStjY9pgIyc2TsH0PdPbwYXCcg/DcgnEfDMPNjh/VLN/H0Ja+ya8sehBAkpfl44ov7adapSaJdK1Umj5rOS9e9VaieTwjBNf+9lBue7mPJloyuRu66hK3rdaZ+X4FoRND+3EwaNtMQFd5AeLqUsvcKM2RwInLfgxzIimPMiU26HS35joT5dbwipURKaTvzrFCUFiogs4iMrkXuvtwQnSNMwSDq9E8RJdT+UlhDSsmWVduIRmLUaVLzmLygXlL5P2Tvzolbd7g0fgyMsvQ7y7zPkVkvU3jkVD7+G9BSHy+Bp4p/i77rAoj+HX9AJCOqzC0f48mOA7L35vDOPR8zdcxs9JhO8zObct8Ht1KzUfVEu6Y4TjlUQHbs3d1KAZn1IsgcjGAMIAoyD5n1dAK9Oj4RQlDrhBrUO7n2MRmMAabBGEAsopO9N9eaMVEBTGeaukFTkwSOGMV1UMsQSIufqcIWUkoeOutppo6ZTTQcRY/pLPp9Kfe2f4ycfeozUJQ/js07XEkJ/4HpANPocmTcKB+FomSYifPux+11WTPm7Yr511pD+C6yZkthH2cj83WRbPwoypzFU5exbe12ouFowZrUJaFAmInDpyTQM4XCHBWQmSGKE5R0oRpTFaVN8zPMh+JWrVcZX5LXki0hfIiM/4FW2TiPRTKIVET62wiHmi14pBDJDwFFPzsvpDxwTDZWlEc2r9yKrsdLvYTywqxdvCEBHikUh0ZdGczwX038xdQDvt7qYnoUI0NT0XddgP5PM/Sd3dHzvk+0SwAMGH4PyelJBzJlwtDTevbbR2zZE65TEJWnGTWP6R8iqsxCeM4sRY8Vh0N4TkekDwVnM8ALjnqQ9iKa31qThsI+9ZrVMS1z8CZ5OKFlgwR4pFAcGpXuMUEk34GMrYXgryA8htK+uzVCFUQftcjQNOTeuynoeotthKzH0Qmg+a9IqG+ValZkxNp3+eXT31k+exX1mtXmvL7dSK9awbZNIbQSD5/fT152gF+HT2X57L+p27QWPfp2VYrs/wLhaYfwjE20G8ctTdufQJ2mtVi7aD2RkLFtqWkCX7KXbteekWDvFIp4VJflIZCxLRBdDY66CGe9Mvt7FGWPvqt3/oD3ImgVEZVnKm2oYti1dQ93tRlAXmYewbxQwVSH14NSV0QAAAmrSURBVKc+a3uag67rrFm4nkg4ygmtGuB0qedCRdmQlx3go0dGMOnzaUQjUdqcdxp3vvEfqtSulGjXFMcpSvZCcdyj/9OcQppQBTgQVeYjNDWI2oyXrnuLKaNnEIsWrsU5sU0j3pnzkmV7qxes47+9XiY3Mw+hCTSHxmOf96NNj8SOEVMoFIojgZK9UCgcxYyDEskgrBXOH0/M/n5+XDAGsHrBWoJ5JlpnhyAUCNG/+zPs2rKHQE6QvKwAOXtzeeayQezcvLu0XFYoFIqjEhWQKY4LRMr9xDdq+CD5DtWocQjcHvPtRCGM7JYVZo//k6hJcKfHYvzy2e923FMoFIpjBnUnUhwXCO85kPo8aFUBYQioptyP8P8n0a6Va869qWucFprT5eD0nq1we6xppGXtykKPxuLWI6Eoe//JLJGfCoVCcbSjqmkVxw2avxf4e+WL+7pUIf+/4PqnLufveWtYNutvhDAyY1XrVeb+obdZttW8y8mm675kL63PUSPJFArF8Y0KyBTHHUK4E+3CUYPb6+aViU+y6s+1rF28gRoNq9Gs00m2gtm6TWrR5cqOTPlyJsFco/7M4/fQuGUD2px3amm7rlAoFEcVqstSoVAcMaSUTPlyJj9++CvhUJRu13Smx01n4XJbHBGlUCgURyGH6rJUGTKFQnHEEELQpU9HuvTpmGhXFAqFolyhivoVCoVCoVAoEowKyBQKhUKh+JdIGUPGduU3BykUpYfaslQoFAqF4l+g542G7NdABgEN6b8WkfIAQjgS7ZriGEAFZAqFQqFQHAYZ/BmyXqDQCLa8EUhApD6cKLcUxxBqy1KhUCgUisMgc94mfh5uAAIjkDKSCJcUxxgqIFMoFAqF4nDEtpmvyxjInCPri+KYRAVkCoVCoVAcDmcT83WRBCLtyPqiOCZRAZlCoVAoFIdBpDwMeIuseiGlP0KoW6mi5KizSKFQKBSKwyDcLRAZw8HdzsiIOZsgKgxG81+aaNcUxwiqy1KhUCgUin+BEZR9lmg3FMcoKkOmUCgUCoVCkWBUQKZQKBQKhUKRYFRAplAoFAqFQpFgVECmUCgUCoVCkWBUQKY4blg8dRn9Oj5O7wrXc9upDzFz3NxEu6RQKBQKBaACMsVxwqLfl/LYeS+wbNbf5GUFWLt4Ay9e/QaTRk5LtGsKhUKhUKiATHF8MLT/cEKBcKG1UF6YoQ8PR0qZIK8UCoVCoTBQAZniuGDDsk2m65k7MwnmhY6wNwqFQqFQFEYFZIrjgko1K5iue5PceHzuI+yNQqFQKBSFUQGZ4rjg2v7g8emF1jy+GFfcHUHT1NdAoVAoFIlFjU5SHBd0vWA2gd0u/jewOnm5Gi635PI7dtDnzt1IGUYIlSVTKBQKReJQAZniOEHQ8/o9nHftHnIyHSSlxHA4ARyJdkyhUCgUCrVlqThO8F4IuNA0SE3/f3t3F7J3Xcdx/P259yBss3zIRoEPzIMFKtiUZE5EEgQ7KK0F7UjCyAM7EDpIOhJiUFEQCkm5jBVUpNEDEj4cSBSB4cMsNU2qmTM3HSPDTkb67eD6L++5678D8f7/7uv/f79gXE/3DV/24bv7w/933buWlbH1O7w6JklqzkKmScipX4S150M2AGshG2FpM3nv7tajSZLkkaWmIUub4MxfwNHfw3+fgzXnwClXkaxrPZokSRYyTUeyBKdcMfsjSdIq4pGlJElSYxYySZKkxixkkiRJjVnIJEmSGrOQSZIkNWYhkyRJasxCJkmS1JiFTJIkqTELmSRJUmMWMkmSpMZSVa1neMeSvAq8MOel9wGHBx5HJzKH9sxgdTCH9sxgdZh6DudW1VnzXljoQtYnyaNVdWnrOabOHNozg9XBHNozg9XBHPp5ZClJktSYhUySJKmxsRay77YeQIA5rAZmsDqYQ3tmsDqYQ49RvodMkiRpkYz1CpkkSdLCGEUhS7I/yZ+S7EvyaPfcGUkeSvJ8d3t66znHrCeD25K81D23L8nHWs85dklOS3JvkmeT/DnJdndhWD0ZuAsDSrJ12d/1viT/TnKLuzCck2TgLvQYxZFlkv3ApVV1eNlzXweOVNVXk9wKnF5VX2o149j1ZHAb8HpVfaPVXFOTZC/w26rak2Q9sAH4Mu7CYHoyuAV3oYkka4CXgMuAm3EXBve2DD6LuzDXKK6Q9fgEsLe7vxe4ruEs0opL8h7gSuB7AFV1tKr+hbswmJNkoHauBv5aVS/gLrSyPAP1GEshK+DBJI8l+Xz33Oaqehmgu31/s+mmYV4GAF9I8sckd3s8sOK2AK8C30/yRJI9STbiLgypLwNwF1r5DPDj7r670MbyDMBdmGsshWxHVW0DrgVuTnJl64EmaF4GdwLnAxcDLwPfbDjfFKwFtgF3VtWHgf8At7YdaXL6MnAXGuiOjD8O3NN6lqmak4G70GMUhayq/tndvgL8HPgIcCjJBwC621faTTh+8zKoqkNV9UZVvQncxSwXrZwDwIGqeqR7fC+zcuAuDGduBu5CM9cCj1fVoe6xuzC84zJwF/otfCFLsjHJqcfuA9cATwG/Am7ovuwG4JdtJhy/vgyO/cPXuZ5ZLlohVXUQeDHJ1u6pq4FncBcG05eBu9DMLo4/KnMXhndcBu5Cv4X/LcskW5hdkYHZccGPqmp3kjOBnwLnAP8APl1VRxqNOWonyeCHzC5LF7AfuOnY+ze0MpJcDOwB1gN/Y/YbTUu4C4PpyeB23IVBJdkAvAhsqarXuuf8uTCgngz8udBj4QuZJEnSolv4I0tJkqRFZyGTJElqzEImSZLUmIVMkiSpMQuZJElSYxYySZOT5PokleRDrWeRJLCQSZqmXcDvmH3GniQ1ZyGTNClJNgE7gBvpClmSpSTfTvJ0kvuS/DrJzu61S5L8JsljSR542/80LknvCguZpKm5Dri/qv4CHEmyDfgkcB5wEfA5YDtAknXAHcDOqroEuBvY3WJoSeO2tvUAkjSwXcC3uvs/6R6vA+7pPvD4YJKHu9e3AhcCDyUBWAP4MS+S3nUWMkmT0X2W4UeBC5MUs4JVvPVZrCd8C/B0VW0faERJE+WRpaQp2Qn8oKrOrarzqups4O/AYeBT3XvJNgNXdV//HHBWkv8fYSa5oMXgksbNQiZpSnZx4tWwnwEfBA4ATwHfAR4BXquqo8xK3NeSPAnsAy4fblxJU5Gqaj2DJDWXZFNVvd4da/4B2FFVB1vPJWkafA+ZJM3cl+Q0YD3wFcuYpCF5hUySJKkx30MmSZLUmIVMkiSpMQuZJElSYxYySZKkxixkkiRJjVnIJEmSGvsfNyY7/EpMxxgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replicate the above plot in whichever way you see fit\n",
    "\n",
    "# Note: The method below is only one way of doing it, yours might be\n",
    "# slightly different\n",
    "\n",
    "# Create DataFrame with patients over 50 years old\n",
    "over_50 = heart_disease[heart_disease[\"age\"] > 50]\n",
    "\n",
    "# Create the plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plot the data\n",
    "scatter = ax.scatter(over_50[\"age\"], \n",
    "                     over_50[\"chol\"], \n",
    "                     c=over_50[\"target\"])\n",
    "\n",
    "# Customize the plot\n",
    "ax.set(title=\"Heart Disease and Cholesterol Levels\",\n",
    "       xlabel=\"Age\",\n",
    "       ylabel=\"Cholesterol\");\n",
    "ax.legend(*scatter.legend_elements(), title=\"Target\")\n",
    "\n",
    "# Add a meanline\n",
    "ax.axhline(over_50[\"chol\"].mean(),\n",
    "           linestyle=\"--\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beatiful, now you've created a plot of two different variables, let's change the style."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-ticks',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-whitegrid',\n",
       " 'classic',\n",
       " '_classic_test',\n",
       " 'fast',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-pastel',\n",
       " 'grayscale',\n",
       " 'seaborn-notebook',\n",
       " 'ggplot',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-muted',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'seaborn-paper',\n",
       " 'bmh',\n",
       " 'tableau-colorblind10',\n",
       " 'seaborn-white',\n",
       " 'dark_background',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-deep']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check what styles are available under plt\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the style to use \"seaborn-whitegrid\"\n",
    "plt.style.use(\"seaborn-whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the style has been changed, we'll replot the same figure from above and see what it looks like.\n",
    "\n",
    "If you've changed the style correctly, it should look like the following:\n",
    "<img src=\"../images/matplotlib-heart-disease-chol-age-plot-seaborn-whitegrid.png\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reproduce the same figure as above with the \"seaborn-whitegrid\" style\n",
    "\n",
    "# Create the plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plot the data\n",
    "scatter = ax.scatter(over_50[\"age\"], \n",
    "                     over_50[\"chol\"], \n",
    "                     c=over_50[\"target\"])\n",
    "\n",
    "# Customize the plot\n",
    "ax.set(title=\"Heart Disease and Cholesterol Levels\",\n",
    "       xlabel=\"Age\",\n",
    "       ylabel=\"Cholesterol\");\n",
    "ax.legend(*scatter.legend_elements(), title=\"Target\")\n",
    "\n",
    "# Add a meanline\n",
    "ax.axhline(over_50[\"chol\"].mean(),\n",
    "           linestyle=\"--\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful, you've changed the style of the plots and the figure is looking different but the dots aren't a very good colour.\n",
    "\n",
    "Let's change the `cmap` parameter of `scatter()` as well as the `color` parameter of `axhline()` to fix it.\n",
    "\n",
    "Completing this step correctly should result in a figure which looks like this:\n",
    "<img src=\"../images/matplotlib-heart-disease-chol-age-plot-cmap-change.png\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replot the same figure as above except change the \"cmap\" parameter\n",
    "# of scatter() to \"winter\"\n",
    "# Also change the \"color\" parameter of axhline() to \"red\"\n",
    "\n",
    "# Create the plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plot the data\n",
    "scatter = ax.scatter(over_50[\"age\"], \n",
    "                     over_50[\"chol\"], \n",
    "                     c=over_50[\"target\"],\n",
    "                     cmap=\"winter\") # changed cmap parameter\n",
    "\n",
    "# Customize the plot\n",
    "ax.set(title=\"Heart Disease and Cholesterol Levels\",\n",
    "       xlabel=\"Age\",\n",
    "       ylabel=\"Cholesterol\");\n",
    "ax.legend(*scatter.legend_elements(), title=\"Target\")\n",
    "\n",
    "# Add a meanline\n",
    "ax.axhline(over_50[\"chol\"].mean(),\n",
    "           linestyle=\"--\",\n",
    "           color=\"red\"); # changed color parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beautiful! Now our figure has an upgraded color scheme let's save it to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the current figure using savefig(), the file name can be anything you want\n",
    "fig.savefig(\"../images/matplotlib-heart-disease-chol-age-plot-saved.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reset the figure by calling plt.subplots()\n",
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extensions\n",
    "\n",
    "For more exercises, check out the [Matplotlib tutorials page](https://matplotlib.org/3.1.1/tutorials/index.html). A good practice would be to read through it and for the parts you find interesting, add them into the end of this notebook.\n",
    "\n",
    "The next place you could go is the [Stack Overflow page for the top questions and answers for Matplotlib](https://stackoverflow.com/questions/tagged/matplotlib?sort=MostVotes&edited=true). Often, you'll find some of the most common and useful Matplotlib functions here. Don't forget to play around with the Stack Overflow filters! You'll likely find something helpful here.\n",
    "\n",
    "Finally, as always, remember, the best way to learn something new is to try it. And try it relentlessly. Always be asking yourself, \"is there a better way this data could be visualized so it's easier to understand?\""
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}