{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import collections\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import gmean\n",
    "import datetime\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>cases</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>1035</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>665</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>967</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>1427</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>1452</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>2013</td>\n",
       "      <td>2416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>1887</td>\n",
       "      <td>2312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>2095</td>\n",
       "      <td>2142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>2445</td>\n",
       "      <td>2068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>1936</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cases  average\n",
       "date                      \n",
       "2020-03-21   1035      NaN\n",
       "2020-03-22    665      NaN\n",
       "2020-03-23    967      NaN\n",
       "2020-03-24   1427      NaN\n",
       "2020-03-25   1452      NaN\n",
       "...           ...      ...\n",
       "2020-05-27   2013   2416.0\n",
       "2020-05-28   1887   2312.0\n",
       "2020-05-29   2095   2142.0\n",
       "2020-05-30   2445   2068.0\n",
       "2020-05-31   1936   2001.0\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases = pd.read_excel(  '2020-05-31_COVID-19_Press_Conference_Data.xlsx'\n",
    "                      , sheet_name='New Cases (UK)'\n",
    "                      , skiprows=[0,1,2]\n",
    "                      , skipfooter=11\n",
    "                      , header=1\n",
    "                      )\n",
    "cases.columns=['date', 'cases', 'average']\n",
    "cases.set_index('date', inplace=True)\n",
    "cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3a46b4ad0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WRW1RLud6JdQzXxGPP41wuj3jOfwQaNYGkRu1TRZ+pZSRAnlB+Jv7RlDqgogGU5SXxcevXDIh42dxyYXS/uk42x0QjkMt0Q3w7hoKtIyI1eOvKIivx3/K2NhdUZ7P1Q3lQGIH0zhHPBSG8cBNyuzZs87qCUxtI2yoBwLhHqnenb+I8KcR5vQtk2h78ofKE68smJj73tLroqrARpY1ureEuQF4NswG4BnjsSOtA9P2/w/GFJq6kuiqdk2yrBZK87PjVr3b2BnY2F1enk9dSS71JbkJjfMHQj2Rv3yX2bPpmmUev1kAFm5zFwJ/33M9roSGuITkIcKfJmitx+ftmhRE2Zo5lPBXF+ZM8vhd1MbgaedlWymzZ4fN/DjbPUxOZgaDo97xD4FwmKmcsXr8EN+UzsaOIcrs2eNe+NUNZbx2uichVbxa66hj/GX2bAZHvbNqQW1W7YZL54TA32Bw1EtflE0AhfRChD9NGB7z4deMj1yEII9/BsI/uYiruXck5MZuOBaHCQcMj3rpHBwdH5pyOIpwz7meYSwKakKEmyIxOXQ1Gxo7h1hRfiGef1VDGW6Pn31n++Jy/mCGRgOb89EKP0D3LMI9vWHaNQRzIZQncf75SFTCr5Q6q5Q6rJQ6oJTaZ6wVK6V2KaUajd9FxrpSSn1XKXVKKXVIKbUx6Dw7jOMblVI7EvOS5ifBnTlNZuPxVwUVcY16fXQMukNu7IZjcUnetMJvhoBuWFOBLdMSVZz/XK+LKkdO1OGmYALVuyOzDk1oHcjoCRb+y5aWkJVh4YWTnbM6dyjGO3NOM30rmPG2DbPY4B1vyRxFqAeQZm3zlFj+h12rtd6gtd5k3P88sFtrvQLYbdwHuBlYYfzcA3wPAh8UwJeArcAW4Evmh4UQGbNCdzYx/uAPjarx3Hc3bf1utI6tTQJAvTGtKVTo4YyxsbusLJ911Y6oKn+bel0xVewGU11oY3jMF3Wb6ulod7oZGvWyvOJCL/q8bCub6hPTotr820Sb1QPQNYtc/l6jJXO4rB64EG6TDd75yWxCPbcBjxi3HwFuD1p/VAd4HShUSlUBNwK7tNa9Wus+YBdw0yyuv6AwPf7grJ68rAwsiohtGwZcYxTYrGQEVYYG576bXTknV+1GYnFpIBwQyis0M3rqS3O5qNbB0TYnXl/4MY6h+vBHS2XQB9lsaOy4kNETzNUNZZzoGIx7M7hoWjKblBfMvnq3zzVGttVCTmboBm0mtswMKgqyRfjnKdEKvwZ+r5Tar5S6x1ir0FqfBzB+lxvrNUBz0HNbjLXp1ieglLpHKbVPKbWvqys5w75TkcmdOSGQlhlN24aBEc+UPP/gIq7mvtgbo0Egxg+h48BnugNTtHKzrFxc62DE4+OdrunjxUOjXrqHxmK2waTa/CCbpTCfNFo1TBb+q4wW1S/F2euPZgiLSUleNhY1S+EfDrRriKYyenFxnuTyz1OiFf4rtNYbCYRx7lVKXRXm2FDvKB1mfeKC1vdrrTdprTeVlcVWyDOfMcM5k0MC0XToDJU1ElzE1dw7QmaGGs+Hj5b6MLn8Z3uGxx+/qKYQgINhwj3NM2jOFkxV4dT+QzPhVOcQxXlZlBhhFZNVlXbK7dlxz+ePpiWzSYZFUZw3u1z+PtdYxIwek7oSyeWfr0Ql/FrrNuN3J/DfBGL0HUYIB+O3ufPVAiwKenot0BZmXYgCZwiPH6LryR9K+IOLuFr6XFQX5kwIBUWDIzeTwtzMkLn8Z7uHWWKEgpaW5pGfbQ2b2TObVE6ACnvAG55tKKaxc4jl5VMrdJVSXNVQxsunuiOGrGIhFuGHwAbvbHry9w6Hb9AWzOLiXDoHR2eVPiqkJhGFXymVp5Sym7eB7cARYCdgZubsAJ4ybu8EPmJk91wGDBihoGeA7UqpImNTd7uxJkTB5OlbJtFM4ZouT7yywEb7gJvmvpGYM3pMQmX2ON0eeobHxoXfYlGsqyngUGsY4TfOsTjKkYuTsWZYKLfbZlW9q7WmsWOQhorQrRmubihjYMTDgeb4tajuH/Fgtahph6JMpnyW1bt9Lk/EjB6TOsnsmbdE4/FXAC8rpQ4CbwC/1lr/Dvg6cINSqhG4wbgP8BvgNHAK+AHwFwBa617gK8Be4+fLxpoQBYNuL1kZFrInpToW5Fij8Pi9IYU/MHt3hJZeF4uKY8+dB3Na00SP/8LG7gURv7i2kGPnnYx5Q3vL53pdFNisYXsORaLSYaPdOXOPv2twFKfby4pye8jHr1pRRoZF8ezx+KV1mu2yo+1GWmafXb+eXmMISzRILv/8JWKduNb6NLA+xHoPcH2IdQ3cO825HgIeit1MIVC1a50iEIEY//RZPVprnNM0AasqzOH8ofP4/HpCH55YqC/J5elDbYx5/eP592Yq55Ig4b+oxsGY18/JjsGQTd0CqZwz8/ZNqgttHD8/OOPnN3aGzugxceRmcuniIp493slnb1o14+sEMzDN32Y6yuzZdA+N4vfrsP37Q+H1+RkYCT+EJRhz8148/vmHVO6mCc5JfXpMIsX4J7dkDqbKYRtv6Rxr1a7J4pI8/PpCd08ICL9SE+P162sDG7zTFXLNJpXTpMqRQ9vAzIu4zOEry6cJ9QBct6qc4+2DtPXHJ61zwBVduwaTsvxsPD49PqA9pmsZz4k2xl+Ym4ndZpUN3nmICH+aMLkXv0lBTiZjXv+0fWTCbR6aRVwQeyqnyYVpTRfE4Wz3MNWOHGxBueKLinNw5GRyuHVqfNzn17T0ucZjyjOlymHD7fHTP8P+Mo2dQzhyMscLpUJx3apA1vJzJ+IT7olmCEswy4xvIzMZhRlt1a6JUorFJTJ4fT4iwp8mTO7MaVIQoXq3fyTwn306j99kNpu7MDEOfKbHRX3pxPMppbi41sHB5qke//mBETw+PWuP32wpPdNcfrNHT7h4+4ryfGoKc3guTnH+aBu0mWxdUky21TKjtFKzajfcEJbJLC7O45zE+OcdIvxpgnPEMyWjBy705J8us8esDA3VC6bSEP6czAxK86MXg2BK87PIy8qYMK3pbPeFHP5gLqpxcLJjcMq3k/EB67MUfvP1zDSX/1TnECvChHkg8AF2/epyXjkVn26dsQq/LTODy5aW8MKJmQi/6fFHf726klxa+kbimsIqJB8R/jRhOo/fMd6oLfQGb7hQT0leFlkZFmqLcmKacRtMIByQN+7x9w2PMTDimbCxa3JxbSFev+bYeeeEdTOVc6bhJhNz9u55Z+zC3z00Su/wGMunyegJ5tpV5Yx4fLx+enZD2P1+jdMdm/BDIK30dPdwzDNxx0M9MXn8uXj9Om6dT4XUQIQ/hXina4hb/u0l9jdNbf/rnNSL32Q81DOdxx9G+JVS1BTlzDrEsjiowtPsux9a+APZPIcn5fOf63VhtaiQ079iocyejdWiaOqOPTQxXY+eUGxbWoIt0zLrcM+g24vW4IhBiAGuMUZTvtAYm9c/E+GvC7GHI6Q/IvwpQuegmx0PvcGRVifPThpO7vX5cY35QoZ6InXojFQZ+i93XMwX3jO71MTFJXk097nw+TVnuqbm8JtUOWyU5mdNifM39bqoLYq9cngyGRbFtmUlPPN2e8yZPaeMqVuRQj0QCLdcsayU3cc7Z9UGOtaqXZMlpXksKs7hhRg3mPuGx8jJzCAnymIxuNCWI5pBOkL6IMPWU4DhUS93P7yPnqExyu3ZHGmdGAoZGg3drgEuVPJOl9LpHPGgVOjnAly6uHg2pgOBXH6PT9PWP8JZY5hKqM1ipRQX1Th44WQX9/3kLTqdbjoHR2npc7FtWems7QC4bUMNf/PEQd5q7mdjXfRdvxs7h8jPtlIZZb+ia1eVs/t4p7EvEDk8FIqZCr9SiqsbyvjFm62Men1kW6MT8t5hT9SpnCaVBTbysjLGU12F+YF4/HOE1+cf32idvP7JH7/J0bYB/t+HLuHqhjKOtA5M8CRD9eI3MSdyhQv12LOtMRf7xELw4PUz3cPUFuVOO0zlhjWVjHp8HGrpx681a6oK+PDWxXzmhoa42HLj2gqyrBaeeqs16uf4/ZqDLQMsj5DRE8y1RlrnbKp4w2VcReKahnJcY76YpoL1ucZi2tiFQLuNhko7x9tF+OcT4vHPEd97/h2+ueskm+uLuOXiam6+qJKy/Gy++NRRnjvRxVffv47rV1fQ0jfCE/tbaHe6x/Psp+vMCZBtzcCWaZnW4w/VkjneBA9eP9szHDLMY/KhrXV8aGtdwmyx2zJ59+pynj50ni/esgZrRnjfxu/X/N1/H+Zgcz9fvGVN1NepKcxhVaWdZ4938omrl83I1lhaMk9m2zJzKlgXVyyP7ttSoF1D7Nlbqyrt/OZwIHw20yQAIbUQj3+OONk5hN1mxTni5Us7j3LZP+/m5u+8xE/eOMe91y7jw1sXA7CupgBgQrgnVC/+YAKN2qbP6pmJRxkLlQU2sqwWmnqGOdvtYsksC7Fmy63ra+gZHuOVd8Jn3Zii//jeZu67bjkfv6I+putct6qcfU19EXslTcdMQz0QmAq2eUlRTGmdsbRkDmZlhZ2BEQ8ds+gKKqQWIvxzRIfTzerKAp75q6v4/V9dxSevXY7Pr/nglkX8zfaV48etrirAoiZmvjhDTN8KJlzbhv45EH6LRbG4OJf9TX0MjXpDZvTMJdesLMNus/LUgenDPZNF/zM3NMTszV63qhyfX/NSjNk1Jv0xTN8KhTkVLNr2EX0xtGQOZmVlwBk5IXH+eYMI/xzRNTg6PjqvocLOZ7avZNdnruZrf3DxBMHJzbKyrCyfo0HCb3r80wp/mGEsc+HxQyDOb7YrDhfqmQtsmRncvK6SZ460hyyyiofoA1xSV0RhbibPHptZnN854iHbapnQ2iIWrm4I7DO8GEUVr8fnx+n2zjjUA3Ci3RnhSCFdEOGfA7TWdDjdUU+4Wlfj4EhbkMc/Piw9TKhnGuF3jnhwhKjajTeLS3Ix+r0l3eOHQHbP8JiP3ZNEWWvNP/zq6KxFHwLpozetrWTnwTZebox9JONsP5QbKvKpctiiat9gfrsojnFzFwK9fcrt2bLBO48Q4Z8Dhka9uMZ8lNunb/4VzLoaBx3OUToHA9WSkWL8BbbQPfm11nPm8dcbcX2rRVEzy0KseHDZ0hLK7dlTwj3ff/E0j77WxP/3riWzEn2Tv3vvapaV5fPn/7WfEzEK42z/NmZa58uN3XgitFSItUHbZFZW2mN+fULqIsI/B3QagzOi9virAzHVo8YG76DbQ25WxrQZKtNt7o54fHh8es5CPRBoxRwpk2YuyLAo3re+mudPdI2n0T51oJWv//Y4t1xcxRduXh2XDJUCWyYPfWwztqwMPv7wXjpjaBfRH2NL5lBcs7KMwVEvb50L363zdFegMjnaOoXJrKq009g5JD175gnJ/x+6AOgwxMCM8UdiTbWZ2RMI9ziNISzTYcb4/f6JVaSzyRqJFbPCM9nx/WBu21DNmM/Pb4+c57V3evjbJw6xZUkx3/yj9XGta6gpzOGhHZvpHR7j7kf24RqbfjBOMOb0rdlw+fJSrBbF8xGqeH//dgeOnEw2LCqc0XVWVhYw5vVPaMYnpC8i/HOAORy73B6dt2W3ZbK0NG88zj/o9k67sQsBYdcahiYJzlwKf3WhDVumJapeN3PFRTUOlpTm8cNXznLPY/uoK8nlB3dtirrSNaZr1Tr4tw9ewtG2Af7yJwfGB9yEI9bpW6EosGWy0ZgKNh0en5/dxzq5fnX5jL+NXdjglXDPfECEfw4wY/UVUXr8AGtrHOO5/NN15jQZb9swqTJ4YJbpgrFgzbDw5J9dzl9cszzh14oWpRS3rq/mRMcgtswMHv7Y5oQWs717TQX/+5Y1/M+xDr7/4jsRj4/X/st71lVyvH1wStdTk71nehkY8bB9TeWMr7G8PB+Lksye+YII/xzQ4RwlNyuD/OzoC6XXVRfQ2j9C7/AYzmmmb5lMN4xlLj1+CGxKJ7pKOFbu3LKI61eV88OPbp7xXOFY+OgVS7i6oYwfvnJ22sHyEGjVMTTqDTknIVZu3VBDZobi5/tbQj7++7c7sGVauLqhbMbXsGVmUF+SJ7n88wQR/jmgw+mm3J4d02aiOZD8aNuA4cnfImIAACAASURBVPGHE/7AB8rkzJ7+ORb+VKTKkcODH90ccsB7ovjo5fV0DY7yzNH2aY9xGplajpzZd00pzsviulXl/PJA65TsHq01vz/azrtWlMXUlTMUktkzfxDhnwM6B0cpjzGbYl31hd71zpEIm7s2syf/xBi/U4Q/KVzdUEZdcS6PvnZ22mPGv43F6RvSBy5dRPfQ2JRiriOtTtoG3GxfUzHra6ystNPU64p681pIXUT454DOGIq3TBy5mSwqzuFoqzOqzV0IHeoJ15JZSAwWi+Ij2xaz92wfb7eFjon3u2bemTMU16wsoyQviycnhXt+/3Y7FgXXr5698K+qtKP1haE1Qvoiwp9gAlW7o1EXbwWzrtrB/qY+xnz+iOmcMLU184AxpzeRLZmF0Nxx6SJsmRYee/1syMcv7L/Ep6o6M8PCbRtq2H2skz5jti7AM0fb2bKkeEY9eiYz3rNHwj1pjwh/ghka9TLi8cWU0WOyrsZBu1EDUBBG+O3ZVjIsitZJzbrmqmpXmIojN5PbN9Twy7faQs5hSMTG+x9eWsOYz8+vDrUBcKZ7mJMdQ7PK5gmmrjgXW6ZFWjfMA0T4E4zZyjbWUA8wYUMyXFaPxaK4YXUFT+xrmeDtifAnl7u2LWbE4+OJ/c1THkvE/svaagerqwrGs3t2vR3YXL4hDvF9CFRDN1TYOdEhKZ3pjgh/gjFL+MtmFOopGL8dKU7/me0NDI95+c+g/HER/uSyttrBpsVFPPZ604Sqaq017xizieP99/nApbUcbBmgsWOQ3x/tYG11AYuK45fGurJCMnvmA1ELv1IqQyn1llLqaeP+EqXUHqVUo1Lqp0qpLGM927h/yni8PugcXzDWTyilboz3i0lFYu3TE0xJfjbVjsDzwm3uQqDV823rq3nk1bPjBWMi/MnnI5fX09Tj4kWjZ//Z7mE++sO9PPzqWbYtLZl2ROVMuW1DNVaL4j9fOM3+c31xC/OYrKy00z00RveQDGVJZ2J5130KOBZ0/xvAt7TWK4A+4G5j/W6gT2u9HPiWcRxKqTXAncBa4CbgP5RS8a+dTzHMPj0zEX4IVPACYfP4TT797gY8Ps1/PBfw+p1xaAkgzI6b1lZSZs/mwZfP8K1dJ9n+7RfZ39THF29Zw2N3b4n79Urzs7lmZTk/f7MFrWH72viEeUxWGRu8J8XrT2uiEn6lVC3wXuAB474CrgOeNA55BLjduH2bcR/j8euN428DHtdaj2qtzwCngPi/81OMmVTtBmPm8xdEUehTX5rHHZfW8uM952jtH4lL90dhdmRZLXxwSx0vNXbznd2N3LS2kt1/fTV3X7kkYV1MP3BpDRDYjDV77MSLlcb5ZIM3vYlWjb4NfBYw30UlQL/W2qzkaAFqjNs1QDOA1tqrlBowjq8BXg86Z/BzxlFK3QPcA1BXl7ih3HNF52DsOfzBfGhrHXabNep2uvddv4JfvNnKN357HK9/bloyC+H56OX1tPaN8Icba7g8ysHos+G6VRVUO2zcvqE67sPRS/OzKM7Lkjh/mhNR+JVStwCdWuv9SqlrzOUQh+oIj4V7zoUFre8H7gfYtGlT5BaHKU7nDHP4Tcrs2Xz8yiVRH19TmMOHttbx8KtnAanaTQWK87L45h+tn7PrZVktPPs315CVgG8USilWVtg5Pqlnz4DLg1/rGQ96EeaWaDz+K4BblVLvAWxAAYFvAIVKKavh9dcCbcbxLcAioEUpZQUcQG/Quknwc+YtHYNuLq6dWQ/0mfIX1y7j8b3ncHv8IvwLlJnO8Y2GlZV2frq3mR+8eJpDrQMcaumnqceFRcEVy0u5dX01N66rjJiQICSPiC6B1voLWutarXU9gc3ZZ7XWHwaeAz5gHLYDeMq4vdO4j/H4s1prbazfaWT9LAFWAG/E7ZWkIFprOp2jVMzC458J5XYbH7088C1htoM+BGEyF9c6GPH4+OpvjvFmUx+rKwv47E0r+fNrltHU4+JvnzzEpn/6Hz7x2D7Odg8n21whBLNp4vI54HGl1D8BbwEPGusPAo8ppU4R8PTvBNBaH1VK/Qx4G/AC92qtfbO4fsozOF61O/MY/0y599pl2G1WNtUXzfm1hfnN+9ZXU1ecS31pHqX5E52av9m+kgPN/ew82MbP9jbzDXWc7/3JpUmyVJiOmIRfa/088Lxx+zQhsnK01m7gjmme/1Xgq7Eama50xjhyMZ7YbZnce23qDEUR5g+ZGRY21ReHfEwpxSV1RVxSV4Rr1MdvjpzH6/OnxBxm4QLy10ggsY5cFIT5xFUNZQy6vRxsCT8IXph7RPgTSMcMRi4KwnzhyuWlWBS8cKIr8sHCnCLCn0DMBm2xDmERhPmAIzeTDYsKeaGxO9mmCJMQ4U8gnc5R8mZRtSsI6c5VDWUcaumf0DVWSD4i/AmkY5ZVu4KQ7lzVUIbW8NIp8fpTCRH+BNLpdM+oHbMgzBfW1xbiyMmcMgtYSC4i/Amkc3BUPH5hQZNhUVy5opQXT3YRqOMUUgER/gQRmLXrloweYcFz9YoyOgdHpaNnCiHCnyCcbi9uj19y+IUFz1UNZQCzCve0D7jx+eUbQ7wQ4U8QXYPJq9oVhFSi0mFjZYV9fApZrDz08hm2fX03H3loD/2u6bOD+l1j49XyQnhE+BPEbIasC8J846qGUvae6cM15o18sIHPr/mHnUf58tNvc2ldEXvP9HH7v7/Cqc6hCcf5/Zr/er2Jd/2f5/jwA3vibfq8RIQ/QZgjF2fTi18Q5gtXNZQx5vPz+umeqI4fHvVyz6P7ePjVs/zplUv46Se28ZN7tjI06uX9//4Kz53oBOBkxyB3fP81/v6XR7AoxamuIdyeed37MS6I8CcIc8i6VO0KAmyuL8aWaeHFk5Hz+Tucbv74/td47kQnX7ltLX9/yxoyLIpLFxfz1CevpLY4l7sf3su9P3qT9373JU53DfHNO9bzT7evQ2s4I62gIyLCnyA6nG7ys61StSsIBAbDXLa0JOIG76jXx10P7uF01zAP7NjEXdvqJzxeU5jDz/98GzeureTXh8/zvvXV7P7ra/jDS2tZVpYPwDtdQyHOLAQjqpQgOp2jsrErCEFctaKML594m7Pdw9SX5oU85t+fPcXJjiF++NHNXLuqPOQxuVlW/uPDG+lwjlLpuPCNeklpHkrB6S7x+CMhHn+C6HC6Jb4vCEFsX1uBLdPC3zxxkDGvf8rjx847+Y/n3+EPLqmZVvRNlFITRB8gJyuDmsIc8fijQIQ/QUjVriBMpLYol//7gfXsa+rji788MqGS1+vz87mfH8KRk8kXb1kz42ssK8sX4Y8CEf4EcKFqV4RfEIJ53/pqPnntcn66r5lHXj07vv7DV85yqGWAf7h1LUV5WTM+/9KyPE53DUt7iAiI8CcA54iXUa9fQj2CEILP3NDADWsq+Mqvj/FyYzdNPcN8c9cJ3r26glsurprVuZeV5eMa89GegEKuZ462M+DyxP28yUCEPwF0jlftiscvCJOxWBTf+uMNLC/L594fv8mnHj9ApsXCP92+DqXUrM49ntnTGd8N3tb+ET7x2H6+uetEXM+bLET4E0Br/wgAFeLxC0JI8rOtPLBjExYFB5r7+cJ7Vk/ZrJ0Jy8oD2ULxjvMfNuYG//ebrTFVH6cqIvwJ4GDzAErB6uqCZJsiCCnLouJcHv7YFv72xpXcuXlRXM5Zlp+NPdsad+E/0uoEYHDUy9MHz8f13MlA8vgTwP5zfayssFNgy0y2KYKQ0qxfVMj6RYVxO59SiqXl8c/sOdI2wKpKO16/5kdvnOOP4vRBlSzE448zfr/mrXN9bFxclGxTBGFBsszI7IkXWmuOtA6wrsbBh7bUcbC5n6NtA3E7fzIQ4Y8zp7qGGHR72Vgnwi8IyWBZWT7nB9wMjcYnFt/hHKV7aIx11QX84cZasq0WfrznXFzOnSxE+OPM/qY+AC4Vj18QksKyssAG75k4ef1HWgPe/boaB47cTN57cRW/fKs1bh8sySCi8CulbEqpN5RSB5VSR5VS/2isL1FK7VFKNSqlfqqUyjLWs437p4zH64PO9QVj/YRS6sZEvahksr+pj+K8LOpLcpNtiiAsSOLdrO1wayBZY42RrPHhrXUMj/nYeaAtLudPBtF4/KPAdVrr9cAG4Cal1GXAN4Bvaa1XAH3A3cbxdwN9WuvlwLeM41BKrQHuBNYCNwH/oZTKiOeLSQXebOpjY13RrPORBUGYGXUluWRYVNyE/2jbAMvK8snNCuTCbKwrYmWFnR+/0RSX8yeDiMKvA5j/gpnGjwauA5401h8Bbjdu32bcx3j8ehVQwduAx7XWo1rrM8ApYEtcXkWK0Ds8xunuYTYujl+WgiAIsZFtzaCuODduG7xHWp2sC0rNVkrxoa11HGl1csjI7083oorxK6UylFIHgE5gF/AO0K+1NoNcLUCNcbsGaAYwHh8ASoLXQzxnXvDWOSO+Lxu7gpBUlpbmxcXj7xocpd3pZl2NY8L67ZfUYMtM303eqIRfa+3TWm8Aagl46atDHWb8DhXj0GHWJ6CUukcptU8pta+ra2bDmZPF/qY+rBbFxbXi8QtCMllWns/p7mF8/tk1azvSdmFjNxhHTibvu7ianQfb0nKTN6asHq11P/A8cBlQqJQyC8BqAXOnowVYBGA87gB6g9dDPCf4GvdrrTdprTeVlZXFYl7S2d/Ux9rqAnKy5t3WhSCkFcvK8hjz+mntG5nVeY4aGT1rQlThv/+SGlxjPvae6Z3VNZJBNFk9ZUqpQuN2DvBu4BjwHPAB47AdwFPG7Z3GfYzHn9WBHqk7gTuNrJ8lwArgjXi9kHhwsmMQ/ww9BI/Pz8GWfincEoQUYDyzp3t24Z7DrQMsKc0LWYV/SV0RmRmKPfNR+IEq4Dml1CFgL7BLa/008DngM0qpUwRi+A8axz8IlBjrnwE+D6C1Pgr8DHgb+B1wr9baF88XMxvO9bi48dsv8uhrZ2f0/OPnB3F7/FK4JQgpwIUunbMT/iOtTtZO03MrJyuDi2oc7D2bfsIfsVeP1voQcEmI9dOEyMrRWruBO6Y511eBr8ZuZuI52jaA1vCjPefYcXl9zOmY+5sCf3wp3BKE5FOUl0VRbibvzCKzp294jNb+Ee7atnjaYzYvKeahl8/g9viwZaZPiFcqdw2Otw8C0Ng5NF59Gwv7z/VT5bBRXZgTb9MEQZgBsx3DOL6xW+2Y9pitS4rx+DRvnUuvtE4RfoOTHYNUOWzkZ1v5yRvNkZ8wiTebpDGbIKQSy8ryOT0b4TdaMa+rmb69+qWLi1GKtAv3iPAbnGgf5OJaB+9bX82vD7cxMBL9iLX2ATet/SOSvy8IKcSy8jy6h8ZmPC7xSNsAtUU5FOZOPwPYkZPJqsoC3kizDV4RfsDt8XG2Z5iVFXY+uGURbo+fnQdao37+m0bhlnj8gpA6LC2dXWbPkdaBsGEeky31Rbx5rg+Pzz+j6yQDEX7gVOcQfg0rKwu4qMbB2uoCfvxGM4Es1Mjsb+oj22phTZVM3BKEVGFZ+cwzewZGPDT1uLioNgrhX1KCa8zH0TZnzNdJFiL8BMI8ACsr81FKceeWOo6dd3KoJbphC/ub+lhfW0iWVf45BSFVWFSUQ2aG4tj5wZif+7Yh4tOlcgazeUngm346FXKJUhHY2M3KsFBfEujjfduGanIyM3h8b+Q+HPubejncOjD+xxcEITWwZli4dmU5T+5vZtAdW5zf7MG/NopQT7ndxpLSvLQq5BLhJ5DKuaw8H2tG4J+jwBYYtrDzQBvDYfpwnB8Y4ROPvcmiohzuedeyuTJXEIQo+eR1y3G6vTz2emwtlA+3DlBZYKPMnh3V8Zvri9jX1Dvjyv+5RoSfgMe/qtI+Ye2DWxYxPObjVwdDD1twe3x84rH9jIx5uf8jm3DkymB1QUg1Lq4t5OqGMh546Qyuseiaqb1wsovfHjnPtmUlUV9ny5IS+l0eGmdZKTxXLHjhH3B5OD/gpqFiovBvrCuioSKfH+05x6h3YmcJrTV/94vDHGoZ4Ft/vGHKcwVBSB3uu245vcNjUbVQ3t/Ux589tp/l5Xb+8ba1UV9jS30xAG+kST7/ghf+k50XNnaDUUqx4/J6DrcOsO1rz/K13x7jXI8LgAdfPsMv3mrlr97dwPa1lXNusyAI0bOpvphtS0u4/8XTuD3Ttwc70T7Ixx/eS0VBNo9+fEvIxmzTsag4h8oCW9rk80fs1TPfuZDRM3X3/kNb6lhUlMuP9jTxwEtnuP/F01y2pIQ9Z3q4cW0F9123fK7NFQRhBtx33XI+9MAentjfwl2XTe2909zr4q4H92DLtPDY3Vujju2bKKXYvKSYvWd60Vqn/OjVBe/xn2gfxJ5tpdphm/KYUoqrGsr4/l2bePlz1/KX163gdPcQKysL+OYfbcBiSe0/riAIAbYtK2FjXSH/+fw7jHknFlo197r4kwf3MOr18+jHt7KoOHdG19iypJh2p5vm3tnNAJgLRPg7BmmotEf8hK5y5PBXNzTw2uev5+n7riQ/e8F/WRKEtEEpxX3Xr6C1f4RfvhWoyn+7zclf/fQA1/7L83Q6R3noo5tZWTnz/bpkxPnHvH5eOBn7pMIFLfxaa060D8a0OWuxKDLE0xeEtOOahjLW1RTw3WcbuevBPbznuy/xzNF2PrKtnl2fuWrWLdVXlOdTmJvJG2d64mRxZB597Sw7HnpjfN53tCxot7VzcJSBEc+UVE5BEOYfSinuu24Fn3hsP2NeP5+7aRUf2lIXt1Rsi0WxaXEx+2bQ1n0maK352b5AJ+FXTnVzSQxNIhe08Js9+CUdUxAWBjeureTp+65kRUU+2db4D05ZW13As8c75mQwy+HWAU52DKEUvPpOD5+8bkXUz13QoZ6T4xk9IvyCsFBYV+NIiOhDwIn060Djx0TzxL4Wsq0W7ri0ln1NfWFTVSezoIX/ePsgZfZsivOm77ctCIIQLQ0VgXqgxs7YG8PFgtvjY+fBNm5cW8lN6yoZ8/p5M4YQ04IW/pMdg6yUMI8gCHGivjSPzAzFyY7Eevz/c6yDgREPd2yqZXN9MRkWxavvRL+pvGCF3+fXNHYOSphHEIS4kZlhYUlpHo0difX4n9zfQrXDxuXLSrHbMllf6+CVd7qjfv6CFf5zvS7cHr94/IIgxJUVFfaEevztA25ePNnFH2ysHU8tv3xZKYdaBqJuP71ghf+EbOwKgpAAGsrtNPe5GBmLfrM1Fn7xVgt+DR+4tHZ87fLlJfj8OupeQQta+JWCFRX5kQ8WBEGIkoaKfHSCMnu01jy5v4XN9UXUl+aNr2+sKyLLaok6zr9ghf9kxyB1xbnkZi3oUgZBEOJMgxFFOJmAOP+b5/o53TXMHZcumrBuy8xg0+IiEf5IHG93SuGWIAhxZ3FxLlkZloQI/5P7W8jJzOA9F1dNeeyK5aUcO++kZ2g04nkWpPC7xryc7h6OapCyIAhCLFgzLCwty4u78A+4PDx9sI2bL6oM2STSnBj2+unIcf6Iwq+UWqSUek4pdUwpdVQp9SljvVgptUsp1Wj8LjLWlVLqu0qpU0qpQ0qpjUHn2mEc36iU2hH1K44zx8470RrWRTFIWRAEIVYaEpDZ87XfHsPl8XH3lUtCPn5xjYP8bGtUaZ3RePxe4K+11quBy4B7lVJrgM8Du7XWK4Ddxn2Am4EVxs89wPcg8EEBfAnYCmwBvmR+WMw1R1qdQKB0WxAEId40VOTT2j/C8Gh0c34j8frpHh7f28yfXrmEtdM4rNYMC1uXFPNaFHH+iDubWuvzwHnj9qBS6hhQA9wGXGMc9gjwPPA5Y/1RrbUGXldKFSqlqoxjd2mtewGUUruAm4CfRLQyzhxpHaAkL4uKgtim7AiCIETDCmP/sLFziA2LCic8Njzq5StPv835ATf9Ix6cIx4GRjwU5Wby/bsuZXn5xL1Ht8fH3/3iMIuKc/j0uxvCXnfbshJ2H++MaF9MMX6lVD1wCbAHqDA+FMwPh3LjsBqgOehpLcbadOuTr3GPUmqfUmpfV1fsAwai4Uibk7U1jpQfjyYIQnpiJo6EivM/faiNx/c20z00SoHNytrqAm5eV8nAiJc//v7rHG0bmHD8vz93itPdw/zz+y8iJyt8c7krlpdGZV/UuYxKqXzg58CntdbOMKIZ6gEdZn3igtb3A/cDbNq0acrjs8Xt8dHYMci1K8vifWpBEAQA6opzybZaQrZu2HmwjfqSXJ6+78oJzuefvmspH/7B63zw/td5+ONb2FhXxIn2Qb73/Dv8wSU1vGtFZM1aWWGnOC+LpgjHReXxK6UyCYj+j7TWvzCWO4wQDsZv8/tFCxCcZFoLtIVZn1NOdgzi9WuJ7wuCkDAyLIplZflTNng7nW5efaeHW9dXT4k4LCnN42d/to2ivCzuemAPr57q5gu/OITdZuV/vXd1VNe1WBTblpZEPi7SASpg3YPAMa31vwY9tBMwM3N2AE8FrX/EyO65DBgwQkHPANuVUkXGpu52Y21OGd/YlYweQRASyMpK+5RQz9OHzqM13LqhOuRzaotyeeIT26guzOHDD+7hzXP9fPGWNZTkR78f+efXLIt4TDQe/xXAXcB1SqkDxs97gK8DNyilGoEbjPsAvwFOA6eAHwB/AWBs6n4F2Gv8fNnc6J1LjrQNYLdZWVScM9eXFgRhAbGiIp/zA26cQY3Tdh5sY01VwZQN3GDKC2z89BPbuGRRIdvXVPD+S6ZshYYlmmhGNFk9LxM6Pg9wfYjjNXDvNOd6CHgoolUJ5GjrAOuqZWNXEITE0mCIe2PHEJcuLuJcj4sDzf18/uZVEZ9bnJfFz//8coCEaNWCqtz1+Pwcax9kXY1U7AqCkFjMzB5zg/dXhwJbmu9bHzrMMxmlVMIc1AUl/Kc6hxjz+mVjVxCEhFNblENOZsb4Bu9TB1rZtLiImsLkh5kXlPAfaQ3kx05X+SYIghAvLBbF8vJ8GjsHOd7u5GTHELdNs6k71ywo4T/a5iQ3K4MlQX2sBUEQEsWKinxOdgyy80AbGRbFey6a2lUzGSww4R9gTVXB+LgyQRCERNJQYafDOcoT+1u4YnlpTGmZiWTBCL/frzna5pT4viAIc4Y507trcJRbo9zUnQsWjPCf6RnGNeaTHvyCIMwZ5mjXLKuFG9dWJNmaCyyYuYPmxq54/IIgzBU1hTkU2KxcuaIUuy0z2eaMs2CE/2ibkyyrheXlMlxdEIS5QSnFj/70MiodtmSbMoEFI/xHWgdYXWknM2PBRLcEQUgBLqpNvSjDglBBrTVHWgdYK2EeQRCEhSH8LX0jON1e6cgpCILAAhH+Cxu7ktEjCIIwL2P8AyMejrYN8Habk7fbnOw504vVosabJgmCICxk5p3wv3Wujw/852v4/IGpjRUF2aypKuDea5djyww/r1IQBGEhMO+E/3dH2rEoeOBjm1lX7aDMnhol0oIgCKnCvBP+5050srm+mGtXlifbFEEQhJRkXm3utvaPcLJjSERfEAQhDPNK+J8/0QnANSvLkmyJIAhC6jLPhL+LmsIcacsgCIIQhnkj/KNeH6+e6uaalWUySF0QBCEM80b4953tY3jMJ/F9QRCECMwb4X/+RCdZGRYuX16SbFMEQRBSmnkj/M+d6GLr0mJys+ZdhqogCEJcmRfC39zr4lTnEFc3SDaPIAhCJOaF8D9/sguAa1dJfF8QBCES80L4XzjRyaLiHJaW5iXbFEEQhJQnovArpR5SSnUqpY4ErRUrpXYppRqN30XGulJKfVcpdUopdUgptTHoOTuM4xuVUjuiMc7j0xGPcXt8vHKqh2tXlksapyAIQhRE4/E/DNw0ae3zwG6t9Qpgt3Ef4GZghfFzD/A9CHxQAF8CtgJbgC+ZHxbhONHupN81FvaYvWd7GfH4pFpXEAQhSiIKv9b6RaB30vJtwCPG7UeA24PWH9UBXgcKlVJVwI3ALq11r9a6D9jF1A+TqdcGfn34fNhjnj/RRZbVwralpZFOJwiCIDDzGH+F1vo8gPHb3FWtAZqDjmsx1qZbn4JS6h6l1D6l1D6r0jz1Vtu0Rmitee5EJ9uWlpCTJb32BUEQoiHem7uhguw6zPrURa3v11pv0lpvKrHn8MbZXlr6XCEv9ua5fk53DbN9bcXMLRYEQVhgzFT4O4wQDsbvTmO9BVgUdFwt0BZmPSyFOZkAPHUg9KEPvXKGApuV918S8suDIAiCEIKZCv9OwMzM2QE8FbT+ESO75zJgwAgFPQNsV0oVGZu62421sGRZLWyuL+KXb7Wi9cQvCK39I/zuSDsf3Fon1bqCIAgxEE0650+A14CVSqkWpdTdwNeBG5RSjcANxn2A3wCngVPAD4C/ANBa9wJfAfYaP1821iJy24YaGjuHePu8c8L6o6+dBeAj2+qjOY0gCIJgENFV1lp/cJqHrg9xrAbuneY8DwEPxWQd8N6LqvjHXx3lqQNtrK12AOAa8/KTPee4aV0lNYU5sZ5SEARhQZPylbtFeVlc3VDOUwda8fkD4Z6fv9mK0+3l41fUJ9c4QRCENCTlhR/g9kuq6XCOsud0D36/5ocvn2F9rYONdRFrwARBEIRJpMWu6LtXV5CfbeWXB1oZ9fo53T3Md+7cIC0aBEEQZkBaCL8tM4Ob1lXy28PtnO1xUVGQzc3rqpJtliAIQlqSFqEegPdfUsPgqJc3zvTykW31ZFnTxnRBEISUIm3U87KlJZTbs8m2WvjglrpkmyMIgpC2pEWoByDDovjybWsZdHspzstKtjmCIAhpS9oIP8BNEtcXBEGYNWkT6hEEQRDigwi/IAjCAkOEXxAEYYEhwi8IgrDAEOEXBEFYYIjwC4IgLDBE+AVBEBYYIvyCIAgLDDV5pGEqoZQaBE4k244ZUgp0J9uIWZDO9ovtyUFsTw6hbF+stS6b7gmpXrl7Qmu9KdlGzASlYHVZ3QAABEFJREFU1L50tR3S236xPTmI7clhJrZLqEcQBGGBIcIvCIKwwEh14b8/2QbMgnS2HdLbfrE9OYjtySFm21N6c1cQBEGIP6nu8QuCIAhxRoRfEARhgZGywq+UukkpdUIpdUop9flk2xMOpdRDSqlOpdSRoLVipdQupVSj8bsomTZOh1JqkVLqOaXUMaXUUaXUp4z1lLdfKWVTSr2hlDpo2P6PxvoSpdQew/afKqVSdmSbUipDKfWWUupp43462X5WKXVYKXVAKbXPWEv59w2AUqpQKfWkUuq48d7flg62K6VWGv/e5o9TKfXpWG1PSeFXSmUA/w7cDKwBPqiUWpNcq8LyMHDTpLXPA7u11iuA3cb9VMQL/LXWejVwGXCv8W+dDvaPAtdprdcDG4CblFKXAd8AvmXY3gfcnUQbI/Ep4FjQ/XSyHeBarfWGoDzydHjfAHwH+J3WehWwnsDfIOVt11qfMP69NwCXAi7gv4nVdq11yv0A24Bngu5/AfhCsu2KYHM9cCTo/gmgyrhdRaAYLel2RvE6ngJuSDf7gVzgTWArgSpGa6j3Uir9ALXGf9LrgKcBlS62G/adBUonraX8+wYoAM5gJLekk+2T7N0OvDIT21PS4wdqgOag+y3GWjpRobU+D2D8Lk+yPRFRStUDlwB7SBP7jVDJAaAT2AW8A/Rrrb3GIan83vk28FnAb9wvIX1sB9DA75VS+5VS9xhr6fC+WQp0AT80wmwPKKXySA/bg7kT+IlxOybbU1X4VYg1yTtNIEqpfODnwKe11s5k2xMtWmufDnztrQW2AKtDHTa3VkVGKXUL0Km13h+8HOLQlLM9iCu01hsJhGTvVUpdlWyDosQKbAS+p7W+BBgmBcM64TD2fm4FnpjJ81NV+FuARUH3a4G2JNkyUzqUUlUAxu/OJNszLUqpTAKi/yOt9S+M5bSxH0Br3Q88T2CfolApZfahStX3zhXArUqps8DjBMI93yY9bAdAa91m/O4kEGfeQnq8b1qAFq31HuP+kwQ+CNLBdpObgTe11h3G/ZhsT1Xh3wusMDIcsgh8pdmZZJtiZSeww7i9g0DsPOVQSingQeCY1vpfgx5KefuVUmVKqULjdg7wbgKbdM8BHzAOS0nbtdZf0FrXaq3rCby/n9Vaf5g0sB1AKZWnlLKbtwnEm4+QBu8brXU70KyUWmksXQ+8TRrYHsQHuRDmgVhtT/YGRZiNi/cAJwnEbP9Xsu2JYOtPgPOAh4A3cTeBeO1uoNH4XZxsO6ex/UoC4YRDwAHj5z3pYD9wMfCWYfsR4H8b60uBN4BTBL4KZyfb1giv4xrg6XSy3bDzoPFz1Pw/mg7vG8PODcA+473zS6AojWzPBXoAR9BaTLZLywZBEIQFRqqGegRBEIQEIcIvCIKwwBDhFwRBWGCI8AuCICwwRPgFQRAWGCL8giAICwwRfkEQhAXG/w8eRjfeklGwjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cases.cases.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>tests</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>11896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>11947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>13623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>14629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>16080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>117013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>119587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>131458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>127722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>115725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tests\n",
       "date              \n",
       "2020-04-01   11896\n",
       "2020-04-02   11947\n",
       "2020-04-03   13623\n",
       "2020-04-04   14629\n",
       "2020-04-05   16080\n",
       "...            ...\n",
       "2020-05-27  117013\n",
       "2020-05-28  119587\n",
       "2020-05-29  131458\n",
       "2020-05-30  127722\n",
       "2020-05-31  115725\n",
       "\n",
       "[61 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tests = pd.read_excel(  '2020-05-31_COVID-19_Press_Conference_Data.xlsx'\n",
    "                      , sheet_name='Daily Tests (UK)'\n",
    "                      , skiprows=[0,1,2]\n",
    "                      , skipfooter=8\n",
    "                      , header=1\n",
    "                      )\n",
    "tests.columns=['date', 'tests']\n",
    "tests.set_index('date', inplace=True)\n",
    "tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3a4096d10>"
      ]
     },
     "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": [
    "tests.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>region</th>\n",
       "      <th>admissions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>England</td>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>England</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>England</td>\n",
       "      <td>806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>England</td>\n",
       "      <td>893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>England</td>\n",
       "      <td>1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>England</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>England</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>England</td>\n",
       "      <td>552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>England</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>England</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             region  admissions\n",
       "date                           \n",
       "2020-03-20  England         608\n",
       "2020-03-21  England         720\n",
       "2020-03-22  England         806\n",
       "2020-03-23  England         893\n",
       "2020-03-24  England        1137\n",
       "...             ...         ...\n",
       "2020-05-25  England         472\n",
       "2020-05-26  England         475\n",
       "2020-05-27  England         552\n",
       "2020-05-28  England         562\n",
       "2020-05-29  England         545\n",
       "\n",
       "[71 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admissions = pd.read_excel(  '2020-05-31_COVID-19_Press_Conference_Data.xlsx'\n",
    "                      , sheet_name='Hospital Admissions (England)'\n",
    "                      , skiprows=[0,1]\n",
    "                      , skipfooter=3\n",
    "                      , header=1\n",
    "                      )\n",
    "admissions.columns=['date', 'region', 'admissions']\n",
    "admissions.set_index('date', inplace=True)\n",
    "admissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3a3ca7b90>"
      ]
     },
     "execution_count": 39,
     "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": [
    "admissions.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3a3e25b50>"
      ]
     },
     "execution_count": 33,
     "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": [
    "cases.merge(tests, how='outer', left_index=True, right_index=True).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>cases</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>1035</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>665</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>967</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>1427</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>1452</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>2013</td>\n",
       "      <td>2416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>1887</td>\n",
       "      <td>2312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>2095</td>\n",
       "      <td>2142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>2445</td>\n",
       "      <td>2068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>1936</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cases  average\n",
       "date                      \n",
       "2020-03-21   1035      NaN\n",
       "2020-03-22    665      NaN\n",
       "2020-03-23    967      NaN\n",
       "2020-03-24   1427      NaN\n",
       "2020-03-25   1452      NaN\n",
       "...           ...      ...\n",
       "2020-05-27   2013   2416.0\n",
       "2020-05-28   1887   2312.0\n",
       "2020-05-29   2095   2142.0\n",
       "2020-05-30   2445   2068.0\n",
       "2020-05-31   1936   2001.0\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}