load_data_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data for specific Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "country = 'Chile'\n",
    "projection = pd.read_json('projections/'+country+'/'+country+'_pred_26_5_2020.json')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_cases_pred</th>\n",
       "      <th>Daily_cases_pred</th>\n",
       "      <th>Total_deaths_pred</th>\n",
       "      <th>Daily_deaths_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-18</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-02</th>\n",
       "      <td>230072.390494</td>\n",
       "      <td>3118.287504</td>\n",
       "      <td>2274.718787</td>\n",
       "      <td>24.682799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-03</th>\n",
       "      <td>233135.034798</td>\n",
       "      <td>3062.644303</td>\n",
       "      <td>2298.590952</td>\n",
       "      <td>23.872165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-04</th>\n",
       "      <td>236142.805208</td>\n",
       "      <td>3007.770410</td>\n",
       "      <td>2321.676008</td>\n",
       "      <td>23.085056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-05</th>\n",
       "      <td>239096.483933</td>\n",
       "      <td>2953.678725</td>\n",
       "      <td>2343.997551</td>\n",
       "      <td>22.321543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-06</th>\n",
       "      <td>241996.862148</td>\n",
       "      <td>2900.378215</td>\n",
       "      <td>2365.579119</td>\n",
       "      <td>21.581568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-07</th>\n",
       "      <td>244844.736730</td>\n",
       "      <td>2847.874582</td>\n",
       "      <td>2386.444083</td>\n",
       "      <td>20.864964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-08</th>\n",
       "      <td>247640.907549</td>\n",
       "      <td>2796.170819</td>\n",
       "      <td>2406.615560</td>\n",
       "      <td>20.171476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-09</th>\n",
       "      <td>250386.175211</td>\n",
       "      <td>2745.267662</td>\n",
       "      <td>2426.116331</td>\n",
       "      <td>19.500771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-10</th>\n",
       "      <td>253081.339161</td>\n",
       "      <td>2695.163949</td>\n",
       "      <td>2444.968788</td>\n",
       "      <td>18.852457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>255727.196068</td>\n",
       "      <td>2645.856907</td>\n",
       "      <td>2463.194881</td>\n",
       "      <td>18.226093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-12</th>\n",
       "      <td>258324.538445</td>\n",
       "      <td>2597.342377</td>\n",
       "      <td>2480.816077</td>\n",
       "      <td>17.621196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13</th>\n",
       "      <td>260874.153442</td>\n",
       "      <td>2549.614997</td>\n",
       "      <td>2497.853332</td>\n",
       "      <td>17.037255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-14</th>\n",
       "      <td>263376.821798</td>\n",
       "      <td>2502.668356</td>\n",
       "      <td>2514.327069</td>\n",
       "      <td>16.473737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-15</th>\n",
       "      <td>265833.316913</td>\n",
       "      <td>2456.495115</td>\n",
       "      <td>2530.257156</td>\n",
       "      <td>15.930087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-16</th>\n",
       "      <td>268244.404037</td>\n",
       "      <td>2411.087124</td>\n",
       "      <td>2545.662898</td>\n",
       "      <td>15.405742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-17</th>\n",
       "      <td>270610.839555</td>\n",
       "      <td>2366.435518</td>\n",
       "      <td>2560.563028</td>\n",
       "      <td>14.900130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-18</th>\n",
       "      <td>272933.370373</td>\n",
       "      <td>2322.530818</td>\n",
       "      <td>2574.975702</td>\n",
       "      <td>14.412674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-19</th>\n",
       "      <td>275212.733388</td>\n",
       "      <td>2279.363015</td>\n",
       "      <td>2588.918500</td>\n",
       "      <td>13.942798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20</th>\n",
       "      <td>277449.655045</td>\n",
       "      <td>2236.921657</td>\n",
       "      <td>2602.408425</td>\n",
       "      <td>13.489925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-21</th>\n",
       "      <td>279644.850976</td>\n",
       "      <td>2195.195931</td>\n",
       "      <td>2615.461909</td>\n",
       "      <td>13.053485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-22</th>\n",
       "      <td>281799.025711</td>\n",
       "      <td>2154.174735</td>\n",
       "      <td>2628.094823</td>\n",
       "      <td>12.632914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>283912.872464</td>\n",
       "      <td>2113.846754</td>\n",
       "      <td>2640.322477</td>\n",
       "      <td>12.227654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-24</th>\n",
       "      <td>285987.072988</td>\n",
       "      <td>2074.200524</td>\n",
       "      <td>2652.159637</td>\n",
       "      <td>11.837160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-25</th>\n",
       "      <td>288022.297477</td>\n",
       "      <td>2035.224489</td>\n",
       "      <td>2663.620533</td>\n",
       "      <td>11.460896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-26</th>\n",
       "      <td>290019.204536</td>\n",
       "      <td>1996.907059</td>\n",
       "      <td>2674.718869</td>\n",
       "      <td>11.098336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-27</th>\n",
       "      <td>291978.441187</td>\n",
       "      <td>1959.236651</td>\n",
       "      <td>2685.467840</td>\n",
       "      <td>10.748971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-28</th>\n",
       "      <td>293900.642923</td>\n",
       "      <td>1922.201736</td>\n",
       "      <td>2695.880143</td>\n",
       "      <td>10.412302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
       "      <td>295786.433794</td>\n",
       "      <td>1885.790871</td>\n",
       "      <td>2705.967989</td>\n",
       "      <td>10.087846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-30</th>\n",
       "      <td>297636.426521</td>\n",
       "      <td>1849.992727</td>\n",
       "      <td>2715.743123</td>\n",
       "      <td>9.775134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>299451.222638</td>\n",
       "      <td>1814.796117</td>\n",
       "      <td>2725.216832</td>\n",
       "      <td>9.473709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total_cases_pred  Daily_cases_pred  Total_deaths_pred  \\\n",
       "2020-01-01          0.000000          0.000000           0.000000   \n",
       "2020-01-02          0.000000          0.000000           0.000000   \n",
       "2020-01-03          0.000000          0.000000           0.000000   \n",
       "2020-01-04          0.000000          0.000000           0.000000   \n",
       "2020-01-05          0.000000          0.000000           0.000000   \n",
       "2020-01-06          0.000000          0.000000           0.000000   \n",
       "2020-01-07          0.000000          0.000000           0.000000   \n",
       "2020-01-08          0.000000          0.000000           0.000000   \n",
       "2020-01-09          0.000000          0.000000           0.000000   \n",
       "2020-01-10          0.000000          0.000000           0.000000   \n",
       "2020-01-11          0.000000          0.000000           0.000000   \n",
       "2020-01-12          0.000000          0.000000           0.000000   \n",
       "2020-01-13          0.000000          0.000000           0.000000   \n",
       "2020-01-14          0.000000          0.000000           0.000000   \n",
       "2020-01-15          0.000000          0.000000           0.000000   \n",
       "2020-01-16          0.000000          0.000000           0.000000   \n",
       "2020-01-17          0.000000          0.000000           0.000000   \n",
       "2020-01-18          0.000000          0.000000           0.000000   \n",
       "2020-01-19          0.000000          0.000000           0.000000   \n",
       "2020-01-20          0.000000          0.000000           0.000000   \n",
       "2020-01-21          0.000000          0.000000           0.000000   \n",
       "2020-01-22          0.000000          0.000000           0.000000   \n",
       "2020-01-23          0.000000          0.000000           0.000000   \n",
       "2020-01-24          0.000000          0.000000           0.000000   \n",
       "2020-01-25          0.000000          0.000000           0.000000   \n",
       "2020-01-26          0.000000          0.000000           0.000000   \n",
       "2020-01-27          0.000000          0.000000           0.000000   \n",
       "2020-01-28          0.000000          0.000000           0.000000   \n",
       "2020-01-29          0.000000          0.000000           0.000000   \n",
       "2020-01-30          0.000000          0.000000           0.000000   \n",
       "...                      ...               ...                ...   \n",
       "2020-07-02     230072.390494       3118.287504        2274.718787   \n",
       "2020-07-03     233135.034798       3062.644303        2298.590952   \n",
       "2020-07-04     236142.805208       3007.770410        2321.676008   \n",
       "2020-07-05     239096.483933       2953.678725        2343.997551   \n",
       "2020-07-06     241996.862148       2900.378215        2365.579119   \n",
       "2020-07-07     244844.736730       2847.874582        2386.444083   \n",
       "2020-07-08     247640.907549       2796.170819        2406.615560   \n",
       "2020-07-09     250386.175211       2745.267662        2426.116331   \n",
       "2020-07-10     253081.339161       2695.163949        2444.968788   \n",
       "2020-07-11     255727.196068       2645.856907        2463.194881   \n",
       "2020-07-12     258324.538445       2597.342377        2480.816077   \n",
       "2020-07-13     260874.153442       2549.614997        2497.853332   \n",
       "2020-07-14     263376.821798       2502.668356        2514.327069   \n",
       "2020-07-15     265833.316913       2456.495115        2530.257156   \n",
       "2020-07-16     268244.404037       2411.087124        2545.662898   \n",
       "2020-07-17     270610.839555       2366.435518        2560.563028   \n",
       "2020-07-18     272933.370373       2322.530818        2574.975702   \n",
       "2020-07-19     275212.733388       2279.363015        2588.918500   \n",
       "2020-07-20     277449.655045       2236.921657        2602.408425   \n",
       "2020-07-21     279644.850976       2195.195931        2615.461909   \n",
       "2020-07-22     281799.025711       2154.174735        2628.094823   \n",
       "2020-07-23     283912.872464       2113.846754        2640.322477   \n",
       "2020-07-24     285987.072988       2074.200524        2652.159637   \n",
       "2020-07-25     288022.297477       2035.224489        2663.620533   \n",
       "2020-07-26     290019.204536       1996.907059        2674.718869   \n",
       "2020-07-27     291978.441187       1959.236651        2685.467840   \n",
       "2020-07-28     293900.642923       1922.201736        2695.880143   \n",
       "2020-07-29     295786.433794       1885.790871        2705.967989   \n",
       "2020-07-30     297636.426521       1849.992727        2715.743123   \n",
       "2020-07-31     299451.222638       1814.796117        2725.216832   \n",
       "\n",
       "            Daily_deaths_pred  \n",
       "2020-01-01           0.000000  \n",
       "2020-01-02           0.000000  \n",
       "2020-01-03           0.000000  \n",
       "2020-01-04           0.000000  \n",
       "2020-01-05           0.000000  \n",
       "2020-01-06           0.000000  \n",
       "2020-01-07           0.000000  \n",
       "2020-01-08           0.000000  \n",
       "2020-01-09           0.000000  \n",
       "2020-01-10           0.000000  \n",
       "2020-01-11           0.000000  \n",
       "2020-01-12           0.000000  \n",
       "2020-01-13           0.000000  \n",
       "2020-01-14           0.000000  \n",
       "2020-01-15           0.000000  \n",
       "2020-01-16           0.000000  \n",
       "2020-01-17           0.000000  \n",
       "2020-01-18           0.000000  \n",
       "2020-01-19           0.000000  \n",
       "2020-01-20           0.000000  \n",
       "2020-01-21           0.000000  \n",
       "2020-01-22           0.000000  \n",
       "2020-01-23           0.000000  \n",
       "2020-01-24           0.000000  \n",
       "2020-01-25           0.000000  \n",
       "2020-01-26           0.000000  \n",
       "2020-01-27           0.000000  \n",
       "2020-01-28           0.000000  \n",
       "2020-01-29           0.000000  \n",
       "2020-01-30           0.000000  \n",
       "...                       ...  \n",
       "2020-07-02          24.682799  \n",
       "2020-07-03          23.872165  \n",
       "2020-07-04          23.085056  \n",
       "2020-07-05          22.321543  \n",
       "2020-07-06          21.581568  \n",
       "2020-07-07          20.864964  \n",
       "2020-07-08          20.171476  \n",
       "2020-07-09          19.500771  \n",
       "2020-07-10          18.852457  \n",
       "2020-07-11          18.226093  \n",
       "2020-07-12          17.621196  \n",
       "2020-07-13          17.037255  \n",
       "2020-07-14          16.473737  \n",
       "2020-07-15          15.930087  \n",
       "2020-07-16          15.405742  \n",
       "2020-07-17          14.900130  \n",
       "2020-07-18          14.412674  \n",
       "2020-07-19          13.942798  \n",
       "2020-07-20          13.489925  \n",
       "2020-07-21          13.053485  \n",
       "2020-07-22          12.632914  \n",
       "2020-07-23          12.227654  \n",
       "2020-07-24          11.837160  \n",
       "2020-07-25          11.460896  \n",
       "2020-07-26          11.098336  \n",
       "2020-07-27          10.748971  \n",
       "2020-07-28          10.412302  \n",
       "2020-07-29          10.087846  \n",
       "2020-07-30           9.775134  \n",
       "2020-07-31           9.473709  \n",
       "\n",
       "[213 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cummulative Projected Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22acdc2c2e8>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [14, 6]\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(projection['Total_cases_pred'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily Projected Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22acd88da58>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [14, 6]\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(projection['Daily_cases_pred'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cummulative Projected Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22acd8aff60>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [14, 6]\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(projection['Total_deaths_pred'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily Projected Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22acda2d4e0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [14, 6]\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(projection['Daily_deaths_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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