index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "import covid_alberta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Covid Alberta\n",
    "\n",
    "> This is a small package that I have developed to look at some of the alberta specific covid data.\n",
    "> There are two parts to the package. the webscraper and the data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file will become your README and also the index of your documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pip install covid_alberta`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web Scraper\n",
    "\n",
    "The `albertaC19` is a class that scrapes the updated stats off of the [alberta Covid-19 website](https://covid19stats.alberta.ca/).\n",
    "\n",
    "example of using the webscraper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-cea1377c44fe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mabC19scaper\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcovid_alberta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malbertaC19\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputfolder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# I don't plan on writing out the data in this example thus the keywords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mab_totals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mab_regions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mab_testing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mabC19scaper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscrape_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfltypes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_dataframes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Repositories_C\\covid_alberta\\covid_alberta\\webscraper.py\u001b[0m in \u001b[0;36mscrape_all\u001b[1;34m(self, totalfl, regionsfl, testfl, fltypes, combine_dataframes, return_dataframes)\u001b[0m\n\u001b[0;32m    335\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    336\u001b[0m         '''\n\u001b[1;32m--> 337\u001b[1;33m         \u001b[0mtotals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscrape_albertaTotals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_filename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtotalfl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfltypes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfltypes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_dataframe\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_dataframes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    338\u001b[0m         \u001b[0mregions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscrape_albertaRegions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_filename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mregionsfl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfltypes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfltypes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_dataframe\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_dataframes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m         \u001b[0mtesting\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscrape_albertaTesting\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_filename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtestfl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfltypes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfltypes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_dataframe\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_dataframes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Repositories_C\\covid_alberta\\covid_alberta\\webscraper.py\u001b[0m in \u001b[0;36mscrape_albertaTotals\u001b[1;34m(self, output_filename, fltypes, update_figure_order, return_dataframe)\u001b[0m\n\u001b[0;32m    177\u001b[0m         \u001b[1;31m# Scrape the data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    178\u001b[0m         \u001b[0mab_cumulative\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtotals_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfig_order\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'cum_cases'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 179\u001b[1;33m         \u001b[0mab_daily_cases\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtotals_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfig_order\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'daily_cases'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    180\u001b[0m         \u001b[0mab_case_status\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtotals_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfig_order\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'case_status'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "abC19scaper = covid_alberta.albertaC19(outputfolder=\"\")\n",
    "# I don't plan on writing out the data in this example thus the keywords\n",
    "ab_totals, ab_regions, ab_testing = abC19scaper.scrape_all(fltypes=None, return_dataframes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can show the dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>cum_cases</th>\n",
       "      <th>Confirmed_count</th>\n",
       "      <th>Probable_count</th>\n",
       "      <th>Daily_count</th>\n",
       "      <th>Active_cum</th>\n",
       "      <th>Died_cum</th>\n",
       "      <th>Recovered_cum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>1250</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>57</td>\n",
       "      <td>618</td>\n",
       "      <td>23</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>1308</td>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>58</td>\n",
       "      <td>676</td>\n",
       "      <td>24</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>1344</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "      <td>712</td>\n",
       "      <td>27</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>1409</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>65</td>\n",
       "      <td>776</td>\n",
       "      <td>27</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>1423</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>876</td>\n",
       "      <td>29</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cum_cases  Confirmed_count  Probable_count  Daily_count  \\\n",
       "2020-04-04       1250               38              19           57   \n",
       "2020-04-05       1308               35              23           58   \n",
       "2020-04-06       1344               20              16           36   \n",
       "2020-04-07       1409               39              26           65   \n",
       "2020-04-08       1423                9               5           14   \n",
       "\n",
       "            Active_cum  Died_cum  Recovered_cum  \n",
       "2020-04-04         618        23            322  \n",
       "2020-04-05         676        24            382  \n",
       "2020-04-06         712        27            449  \n",
       "2020-04-07         776        27            518  \n",
       "2020-04-08         876        29            518  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_totals.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Calgary_cumulative</th>\n",
       "      <th>Central_cumulative</th>\n",
       "      <th>Edmont_cumulative</th>\n",
       "      <th>North_cumulative</th>\n",
       "      <th>South_cumulative</th>\n",
       "      <th>Unknown_cumulative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>778</td>\n",
       "      <td>61</td>\n",
       "      <td>315</td>\n",
       "      <td>75</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>801</td>\n",
       "      <td>65</td>\n",
       "      <td>340</td>\n",
       "      <td>79</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>821</td>\n",
       "      <td>65</td>\n",
       "      <td>348</td>\n",
       "      <td>86</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>854</td>\n",
       "      <td>72</td>\n",
       "      <td>364</td>\n",
       "      <td>94</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>860</td>\n",
       "      <td>72</td>\n",
       "      <td>368</td>\n",
       "      <td>95</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Calgary_cumulative  Central_cumulative  Edmont_cumulative  \\\n",
       "2020-04-04                 778                  61                315   \n",
       "2020-04-05                 801                  65                340   \n",
       "2020-04-06                 821                  65                348   \n",
       "2020-04-07                 854                  72                364   \n",
       "2020-04-08                 860                  72                368   \n",
       "\n",
       "            North_cumulative  South_cumulative  Unknown_cumulative  \n",
       "2020-04-04                75                19                   2  \n",
       "2020-04-05                79                21                   2  \n",
       "2020-04-06                86                22                   2  \n",
       "2020-04-07                94                23                   2  \n",
       "2020-04-08                95                26                   2  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_regions.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>test_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>1737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>1129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>1319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            test_count\n",
       "2020-04-04        1737\n",
       "2020-04-05        1112\n",
       "2020-04-06        1129\n",
       "2020-04-07        1319\n",
       "2020-04-08         459"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_testing.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all pandas DataFrames. For more info on using pandas check out the pandas [cookbook](https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analysis\n",
    "\n",
    "> these are functions that I have started working on for some quick analyses of the data. The main one being doubling rates\n",
    "\n",
    "### Doubling times\n",
    "\n",
    "the `calculate_doublingtimes` function returns 2 columns.\n",
    "\n",
    "> `dtime` is how many days our count has been doubling from the first reported case to get to todays case count\n",
    "\n",
    "> `dtime_rw` is a rolling window calcualtion. So if you window is 6 days it looks at what our doubling rate, starting from the case count 6 days ago, would have to be to get to todays case count.\n",
    "\n",
    "I started off looking at the rolling window calculation. However the more I look into it the more I'm not happy with using the rolling window. Our information about Covid-19 cases are changing so rapidly, that the rolling window calculation tends to be too noisy and too optimistic to be useful. We can calculate both below and see what they look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>dtime</th>\n",
       "      <th>dtime_rw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>2.818897</td>\n",
       "      <td>7.119992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>2.897670</td>\n",
       "      <td>7.353586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>2.982973</td>\n",
       "      <td>9.613334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>3.059140</td>\n",
       "      <td>11.617191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>3.150442</td>\n",
       "      <td>17.176893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               dtime   dtime_rw\n",
       "2020-04-04  2.818897   7.119992\n",
       "2020-04-05  2.897670   7.353586\n",
       "2020-04-06  2.982973   9.613334\n",
       "2020-04-07  3.059140  11.617191\n",
       "2020-04-08  3.150442  17.176893"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals_dt = covid_alberta.calculate_doublingtimes(ab_totals, col_suffix=\"cum_cases\", combine_df=False)\n",
    "regions_dt = covid_alberta.calculate_doublingtimes(ab_regions, col_suffix=\"cumulative\", combine_df=False)\n",
    "totals_dt.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Calgary_dtime</th>\n",
       "      <th>Calgary_dtime_rw</th>\n",
       "      <th>Central_dtime</th>\n",
       "      <th>Central_dtime_rw</th>\n",
       "      <th>Edmont_dtime</th>\n",
       "      <th>Edmont_dtime_rw</th>\n",
       "      <th>North_dtime</th>\n",
       "      <th>North_dtime_rw</th>\n",
       "      <th>South_dtime</th>\n",
       "      <th>South_dtime_rw</th>\n",
       "      <th>Unknown_dtime</th>\n",
       "      <th>Unknown_dtime_rw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>3.019693</td>\n",
       "      <td>7.296903</td>\n",
       "      <td>4.046714</td>\n",
       "      <td>14.735665</td>\n",
       "      <td>3.872364</td>\n",
       "      <td>5.864623</td>\n",
       "      <td>3.692514</td>\n",
       "      <td>8.141493</td>\n",
       "      <td>4.472769</td>\n",
       "      <td>7.609425</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>3.110208</td>\n",
       "      <td>7.587349</td>\n",
       "      <td>4.151191</td>\n",
       "      <td>12.826571</td>\n",
       "      <td>3.956375</td>\n",
       "      <td>6.261873</td>\n",
       "      <td>3.807239</td>\n",
       "      <td>8.008629</td>\n",
       "      <td>4.553405</td>\n",
       "      <td>6.431655</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>3.202070</td>\n",
       "      <td>9.970858</td>\n",
       "      <td>4.317239</td>\n",
       "      <td>18.637702</td>\n",
       "      <td>4.082834</td>\n",
       "      <td>8.636192</td>\n",
       "      <td>3.890285</td>\n",
       "      <td>7.959255</td>\n",
       "      <td>4.709120</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>3.286065</td>\n",
       "      <td>12.181763</td>\n",
       "      <td>4.376066</td>\n",
       "      <td>15.441420</td>\n",
       "      <td>4.189037</td>\n",
       "      <td>11.309771</td>\n",
       "      <td>3.966687</td>\n",
       "      <td>8.029614</td>\n",
       "      <td>4.863424</td>\n",
       "      <td>7.289318</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>3.385243</td>\n",
       "      <td>19.656061</td>\n",
       "      <td>4.538143</td>\n",
       "      <td>20.885405</td>\n",
       "      <td>4.323639</td>\n",
       "      <td>15.835158</td>\n",
       "      <td>4.109679</td>\n",
       "      <td>9.387934</td>\n",
       "      <td>4.893159</td>\n",
       "      <td>8.566048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Calgary_dtime  Calgary_dtime_rw  Central_dtime  Central_dtime_rw  \\\n",
       "2020-04-04       3.019693          7.296903       4.046714         14.735665   \n",
       "2020-04-05       3.110208          7.587349       4.151191         12.826571   \n",
       "2020-04-06       3.202070          9.970858       4.317239         18.637702   \n",
       "2020-04-07       3.286065         12.181763       4.376066         15.441420   \n",
       "2020-04-08       3.385243         19.656061       4.538143         20.885405   \n",
       "\n",
       "            Edmont_dtime  Edmont_dtime_rw  North_dtime  North_dtime_rw  \\\n",
       "2020-04-04      3.872364         5.864623     3.692514        8.141493   \n",
       "2020-04-05      3.956375         6.261873     3.807239        8.008629   \n",
       "2020-04-06      4.082834         8.636192     3.890285        7.959255   \n",
       "2020-04-07      4.189037        11.309771     3.966687        8.029614   \n",
       "2020-04-08      4.323639        15.835158     4.109679        9.387934   \n",
       "\n",
       "            South_dtime  South_dtime_rw  Unknown_dtime  Unknown_dtime_rw  \n",
       "2020-04-04     4.472769        7.609425              0                 0  \n",
       "2020-04-05     4.553405        6.431655              0                 0  \n",
       "2020-04-06     4.709120        6.000000              0                 0  \n",
       "2020-04-07     4.863424        7.289318              0                 0  \n",
       "2020-04-08     4.893159        8.566048              0                 0  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions_dt.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots\n",
    "\n",
    "Here is some of the plots I've used for looking at the data. For this example I'm using matplotlib. Plotly creates nice plots but is a little harder to include in this documentation since it's hosted on github pages. If you head over to [my website](www.tyleracorn.com) I'll post the plotly code and example of the interactive plots there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set defaults and settings\n",
    "days_to_trim = 1\n",
    "date_fmt = \"%B %d\"\n",
    "\n",
    "# Grab the data we want for the plots and trim the last day off\n",
    "plt_totals = ab_totals[:-days_to_trim]\n",
    "plt_total_dt = totals_dt[:-days_to_trim]\n",
    "plt_regions = ab_regions[:-days_to_trim]\n",
    "plt_regions_dt = regions_dt[:-days_to_trim]\n",
    "\n",
    "# use a format dictionary so I only have to set them in one location\n",
    "\n",
    "fmt = {'alb': {'x_data': plt_totals['cum_cases'],\n",
    "               'y_data': plt_total_dt['dtime'],\n",
    "               'last_date': plt_totals.index.strftime(date_fmt)[-1],\n",
    "               'annot_x': plt_totals['cum_cases'][-1],\n",
    "               'annot_y': plt_total_dt['dtime'][-1],\n",
    "               'color': 'green',\n",
    "               'label': 'Alberta'},\n",
    "       'cal': {'x_data': plt_regions['Calgary_cumulative'],\n",
    "               'y_data': plt_regions_dt['Calgary_dtime'],\n",
    "               'last_date': plt_regions.index.strftime(date_fmt)[-1],\n",
    "               'annot_x': plt_regions['Calgary_cumulative'][-1],\n",
    "               'annot_y': plt_regions_dt['Calgary_dtime'][-1],\n",
    "               'color': 'orange',\n",
    "               'label': 'Calgary'},\n",
    "       'edm': {'x_data': plt_regions['Edmont_cumulative'],\n",
    "               'y_data': plt_regions_dt['Edmont_dtime'],\n",
    "               'last_date': plt_regions.index.strftime(date_fmt)[-1],\n",
    "               'annot_x': plt_regions['Edmont_cumulative'][-1],\n",
    "               'annot_y': plt_regions_dt['Edmont_dtime'][-1],\n",
    "               'color': 'blue', \n",
    "               'label': 'Edmonton'},\n",
    "      }\n",
    "\n",
    "# Setup the plot\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "# Create the scatter plots using a loop and the dictionary above\n",
    "for rgn in ['alb', 'cal', 'edm']:\n",
    "    ax.plot(fmt[rgn]['x_data'], fmt[rgn]['y_data'], \n",
    "            c=fmt[rgn]['color'], label=fmt[rgn]['label'])\n",
    "\n",
    "# add an annotation to the last point\n",
    "for rgn in ['alb', 'cal', 'edm']:\n",
    "    ax.plot(fmt[rgn]['annot_x'], fmt[rgn]['annot_y'], 'o', c=fmt[rgn]['color'])\n",
    "    ax.text(fmt[rgn]['annot_x'] - 60, fmt[rgn]['annot_y'] + 0.08, fmt[rgn]['last_date'], \n",
    "            fontdict={'color': fmt[rgn]['color'], 'size': 8, 'weight': 'bold'})\n",
    "# fancy up the plot\n",
    "ax.grid(which='both', linestyle=(0, (5, 3)), lw=0.5)\n",
    "ax.legend(frameon=True, fancybox=True, shadow=True)\n",
    "ax.set_ylabel('Doubling Time (Days)', fontdict={'size': 9, 'family': 'sans-serif', 'style':'italic'})\n",
    "ax.set_xlabel('Cumulative Case Count', fontdict={'size': 9, 'family': 'sans-serif', 'style':'italic'})\n",
    "title = ax.set_title(\"Alberta: Doubling Time by Cumulative Cases\",\n",
    "                     fontdict={'fontsize': 10, 'family': 'sans-serif', 'fontweight': 'bold'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted 00_webscraper.ipynb.\n",
      "Converted 01_analysis.ipynb.\n",
      "Converted index.ipynb.\n"
     ]
    }
   ],
   "source": [
    "#hide\n",
    "from nbdev.export import notebook2script\n",
    "notebook2script()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}