phobson/covid.ipynb

## covid.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from matplotlib import pyplot\n",
    "import pandas\n",
    "import seaborn\n",
    "seaborn.set(style='ticks', context='paper', palette='deep')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(tspart):\n",
    "    url = (\n",
    "        \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/\"\n",
    "        \"csse_covid_19_data/csse_covid_19_time_series/\"\n",
    "        \"time_series_19-covid-{}.csv\"\n",
    "    ).format(tspart)\n",
    "    return (\n",
    "        pandas.read_csv(url)\n",
    "        .drop(columns=['Lat', 'Long'])\n",
    "        .fillna({'Province/State': 'default'})\n",
    "        .set_index(['Country/Region', 'Province/State'])\n",
    "        .rename(columns=pandas.to_datetime)\n",
    "        .sort_index(axis='columns')\n",
    "        .pipe(lambda df: df.diff(axis='columns')\n",
    "                           .where(lambda x: x.notnull(), df)\n",
    "                           .where(lambda x: x >= 0, 0)\n",
    "        )\n",
    "        .rename_axis(columns=['Date'])\n",
    "        .astype(int)\n",
    "#         .pipe(lambda df: pandas.concat(\n",
    "#             [df, df.cumsum(axis='columns')],\n",
    "#             keys=['new', 'cumulative'],\n",
    "#             axis='columns', names=['Which', 'Date']\n",
    "#         ))\n",
    "        .stack(level='Date')\n",
    "        .to_frame(tspart)\n",
    "    )\n",
    "\n",
    "\n",
    "@pyplot.FuncFormatter\n",
    "def yaxisfmt(tick, pos):\n",
    "    thousands = int(tick // 1000)\n",
    "    return f'{thousands}k'"
   ]
  },
  {
   "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",
       "    .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>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27139</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27140</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27141</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27142</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27143</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>default</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27144 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country/Region Province/State       Date  Confirmed  Recovered  Deaths\n",
       "0        Afghanistan        default 2020-01-22          0          0       0\n",
       "1        Afghanistan        default 2020-01-23          0          0       0\n",
       "2        Afghanistan        default 2020-01-24          0          0       0\n",
       "3        Afghanistan        default 2020-01-25          0          0       0\n",
       "4        Afghanistan        default 2020-01-26          0          0       0\n",
       "...              ...            ...        ...        ...        ...     ...\n",
       "27139         Zambia        default 2020-03-15          0          0       0\n",
       "27140         Zambia        default 2020-03-16          0          0       0\n",
       "27141         Zambia        default 2020-03-17          0          0       0\n",
       "27142         Zambia        default 2020-03-18          2          0       0\n",
       "27143         Zambia        default 2020-03-19          0          0       0\n",
       "\n",
       "[27144 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pandas.concat(\n",
    "    map(read_data, ['Confirmed', 'Recovered', 'Deaths']),\n",
    "    axis='columns'\n",
    ").reset_index()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cumulative plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Metric</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Confirmed</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Recovered</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subset</th>\n",
       "      <th>China</th>\n",
       "      <th>Everywhere\\nElse</th>\n",
       "      <th>US/Canada</th>\n",
       "      <th>China</th>\n",
       "      <th>Everywhere\\nElse</th>\n",
       "      <th>US/Canada</th>\n",
       "      <th>China</th>\n",
       "      <th>Everywhere\\nElse</th>\n",
       "      <th>US/Canada</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>548</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>643</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>920</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>1406</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>2075</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>2877</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>5509</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>101</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>6087</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>120</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>8141</td>\n",
       "      <td>87</td>\n",
       "      <td>7</td>\n",
       "      <td>135</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>9802</td>\n",
       "      <td>116</td>\n",
       "      <td>11</td>\n",
       "      <td>214</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Metric     Confirmed                            Recovered                   \\\n",
       "Subset         China Everywhere\\nElse US/Canada     China Everywhere\\nElse   \n",
       "Date                                                                         \n",
       "2020-01-22       548                6         1        28                0   \n",
       "2020-01-23       643               10         1        30                0   \n",
       "2020-01-24       920               20         2        36                0   \n",
       "2020-01-25      1406               27         2        39                0   \n",
       "2020-01-26      2075               38         6        49                3   \n",
       "2020-01-27      2877               45         6        58                3   \n",
       "2020-01-28      5509               63         7       101                6   \n",
       "2020-01-29      6087               73         7       120                6   \n",
       "2020-01-30      8141               87         7       135                8   \n",
       "2020-01-31      9802              116        11       214                8   \n",
       "\n",
       "Metric               Deaths                             \n",
       "Subset     US/Canada  China Everywhere\\nElse US/Canada  \n",
       "Date                                                    \n",
       "2020-01-22         0     17                0         0  \n",
       "2020-01-23         0     18                0         0  \n",
       "2020-01-24         0     26                0         0  \n",
       "2020-01-25         0     42                0         0  \n",
       "2020-01-26         0     56                0         0  \n",
       "2020-01-27         0     82                0         0  \n",
       "2020-01-28         0    131                0         0  \n",
       "2020-01-29         0    133                0         0  \n",
       "2020-01-30         0    171                0         0  \n",
       "2020-01-31         0    213                0         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumxtab = (\n",
    "    data.assign(Subset=lambda df: numpy.select(\n",
    "            (df['Country/Region'].isin(('US', 'Canada')), df['Country/Region'] == 'China'),\n",
    "            ('US/Canada', 'China'), 'Everywhere\\nElse'\n",
    "        ))\n",
    "        .groupby(by=['Subset', 'Date']).sum()\n",
    "        .unstack(level='Subset')\n",
    "        .cumsum()\n",
    "        .rename_axis(columns=['Metric', 'Subset'])\n",
    ")\n",
    "cumxtab.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases = (\n",
    "    cumxtab\n",
    "        .loc[:, 'Confirmed']\n",
    "        .pipe(lambda df:\n",
    "            df.rename(columns=lambda c: f\"{c} ({df.loc[:, c].iloc[-1]:,d})\")\n",
    "        )   \n",
    ")\n",
    "\n",
    "outcomes = (\n",
    "    cumxtab\n",
    "        .groupby(level='Metric', axis='columns').sum()\n",
    "        .loc[:, ['Deaths', 'Recovered']]\n",
    "        .pipe(lambda df:\n",
    "            df.rename(columns=lambda c: f\"Global {c} ({df.loc[:, c].iloc[-1]:,d})\")\n",
    "        ) \n",
    ")\n",
    "\n",
    "fig1, ax1 = pyplot.subplots(figsize=(8, 5), nrows=1, sharex=True)\n",
    "cases.plot.area(ax=ax1, alpha=0.875, zorder=0, linewidth=0)\n",
    "outcomes.plot.area(ax=ax1, zorder=10, color=['0.75', '0.25'], linewidth=0, alpha=0.5)\n",
    "\n",
    "seaborn.despine(fig=fig1)\n",
    "ax1.set_ylabel('Total Cases')\n",
    "ax1.set_xlabel('')\n",
    "\n",
    "ax1.yaxis.set_major_formatter(yaxisfmt)\n",
    "\n",
    "fig1.savefig('newcases.png', dpi=600, bbox_inches='tight')\n",
    "pyplot.close(fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookback = 15\n",
    "fig2, ax2 = pyplot.subplots(figsize=(8, 5), nrows=1, sharex=True)\n",
    "cases.iloc[-1 * lookback:, :].plot.area(ax=ax2, alpha=0.875, zorder=0, linewidth=0)\n",
    "outcomes.iloc[-1 * lookback:, :].plot.area(ax=ax2, zorder=10, color=['0.75', '0.25'], linewidth=0, alpha=0.5)\n",
    "\n",
    "seaborn.despine(fig=fig2)\n",
    "ax2.set_ylabel('Total Cases')\n",
    "ax2.set_xlabel('')\n",
    "\n",
    "fig2.savefig(f'newcases_prev{lookback}days.png', dpi=600, bbox_inches='tight')\n",
    "pyplot.close(fig2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### new cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "COIs = ['China', 'US', 'Iran', 'Italy', 'Spain', 'France', 'Korea, South']\n",
    "since = (\n",
    "    data.groupby(by=['Country/Region', 'Date'])\n",
    "            .sum()\n",
    "        .groupby(by=['Country/Region'])\n",
    "            .apply(lambda g: g.assign(\n",
    "                cmlcase=g['Confirmed'].cumsum(),\n",
    "                days_since=g['Confirmed'].cumsum().ge(100).cumsum())\n",
    "            )\n",
    "        .rename_axis(columns='Metric')\n",
    "        .loc[lambda df: df['days_since'] >= 1]\n",
    "        .set_index('days_since', append=True)\n",
    "        .reset_index('Date', drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region\n",
       "China      11\n",
       "Iran       16\n",
       "Spain      16\n",
       "Italy      17\n",
       "US         17\n",
       "Germany    18\n",
       "France     20\n",
       "Name: days_since, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    since\n",
    "        .loc[lambda df: df['cmlcase'] > 10000]\n",
    "        .reset_index()\n",
    "        .groupby(by=['Country/Region'])\n",
    "        .first()\n",
    "        .sort_values(by=['days_since'])\n",
    "        .loc[:, 'days_since']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = pyplot.subplots(figsize=(6.5, 6), nrows=2, sharex=True)\n",
    "\n",
    "since['cmlcase'].unstack(level='Country/Region').loc[:, COIs].plot(ax=ax1, legend=False)\n",
    "since['Confirmed'].unstack(level='Country/Region').loc[:, COIs].plot(ax=ax2)\n",
    "\n",
    "seaborn.despine(fig=fig)\n",
    "ax1.yaxis.set_major_formatter(yaxisfmt)\n",
    "ax1.set_ylabel('Cumulative Cases')\n",
    "ax1.set_ylim(bottom=0)\n",
    "\n",
    "ax2.yaxis.set_major_locator(pyplot.MultipleLocator(2000))\n",
    "ax2.yaxis.set_major_formatter(yaxisfmt)\n",
    "ax2.set_ylabel('New Cases')\n",
    "ax2.set_xlabel('Days Since 100th Case')\n",
    "ax2.set_ylim(bottom=0)\n",
    "fig.savefig('Since100thCase - Cases', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = pyplot.subplots(figsize=(6.5, 4), nrows=1, sharex=True)\n",
    "\n",
    "since['cmlcase'].unstack(level='Country/Region').loc[:, COIs].plot(ax=ax1, legend=True)\n",
    "# since['Confirmed'].unstack(level='Country/Region').loc[:, COIs].plot(ax=ax2)\n",
    "ax1.axhline(10000, color='k', zorder=-1)\n",
    "seaborn.despine(fig=fig)\n",
    "ax1.yaxis.set_major_formatter(yaxisfmt)\n",
    "ax1.set_ylabel('Cumulative Cases')\n",
    "ax1.set_ylim(bottom=0)\n",
    "\n",
    "fig.savefig('Since100thCase - Cases - Cumulative', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = pyplot.subplots(figsize=(6.5, 6), nrows=2, sharex=True)\n",
    "\n",
    "since['Deaths'].unstack(level='Country/Region').loc[:, COIs].cumsum().plot(ax=ax1, legend=False)\n",
    "since['Deaths'].unstack(level='Country/Region').loc[:, COIs].plot(ax=ax2)\n",
    "\n",
    "seaborn.despine(fig=fig)\n",
    "# ax1.yaxis.set_major_formatter(yaxisfmt)\n",
    "ax1.set_ylabel('Cumulative Deaths')\n",
    "ax1.set_ylim(bottom=0)\n",
    "\n",
    "# ax2.yaxis.set_major_locator(pyplot.MultipleLocator(2000))\n",
    "# ax2.yaxis.set_major_formatter(yaxisfmt)\n",
    "ax2.set_ylabel('New Deaths')\n",
    "ax2.set_xlabel('Days Since 100th Case')\n",
    "ax2.set_ylim(bottom=0)\n",
    "fig.savefig('Since100thCase - Deaths', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = pyplot.subplots(figsize=(6.5, 6), nrows=2, sharex=True)\n",
    "\n",
    "since['Confirmed'].unstack(level='Country/Region').loc[:, COIs].cumsum().plot(ax=ax1, legend=False)\n",
    "since['Confirmed'].unstack(level='Country/Region').loc[:, COIs].plot(ax=ax2)\n",
    "\n",
    "seaborn.despine(fig=fig)\n",
    "ax1.yaxis.set_major_formatter(yaxisfmt)\n",
    "ax1.set_ylabel('Cumulative Cases')\n",
    "ax1.set_ylim(bottom=0)\n",
    "\n",
    "ax2.yaxis.set_major_locator(pyplot.MultipleLocator(2000))\n",
    "ax2.yaxis.set_major_formatter(yaxisfmt)\n",
    "ax2.set_ylabel('New Cases')\n",
    "ax2.set_xlabel('Days Since 100th Case')\n",
    "ax2.set_ylim(bottom=0)\n",
    "fig.savefig('Since100thCase - Cases.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1086ac9190>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=(6.5, 3))\n",
    "(\n",
    "    data.loc[data['Country/Region'] == 'US']\n",
    "        .groupby(by=['Date']).sum()\n",
    "        .loc[lambda df: df['Confirmed'].cumsum() > 1]    \n",
    "        .pipe(lambda df: df.rename(columns=lambda c: f\"{c} ({df[c].sum():,d})\"))\n",
    "        .cumsum()\n",
    "        \n",
    "        .iloc[:, ::-1]\n",
    "        .plot.area(ax=ax)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region    AfghanistanAfghanistanAfghanistanAfghanistanAf...\n",
       "Province/State    defaultdefaultdefaultdefaultdefaultdefaultdefa...\n",
       "Confirmed                                                    243374\n",
       "Recovered                                                     84943\n",
       "Deaths                                                         9896\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Confirmed  Recovered  Deaths\n",
      "Country/Region                              \n",
      "Italy               41035       4441    3405\n",
      "China               81157      70549    3249\n",
      "Iran                18407       5710    1284\n",
      "Spain               17963       1107     830\n",
      "France              10949         12     243\n",
      "US                  14258         24     222\n",
      "United Kingdom       2716         67     138\n",
      "Korea, South         8565       1557      91\n",
      "Netherlands          2467          2      77\n",
      "Germany             15320        113      44\n",
      "Switzerland          4075         15      41\n",
      "Japan                 959        150      29\n",
      "Indonesia             311         11      25\n",
      "Belgium              1795         31      21\n",
      "Philippines           217          8      19\n",
      "Top 15 Total       220194      83797    9718\n"
     ]
    }
   ],
   "source": [
    "def add_total(df):\n",
    "    df.loc['Top 15 Total'] = df.sum()\n",
    "    return df\n",
    "\n",
    "print(\n",
    "    data.groupby(['Country/Region'])\n",
    "        .sum()\n",
    "        .sort_values(by=['Deaths'], ascending=[False])\n",
    "        .head(15)\n",
    "        .pipe(add_total)\n",
    ")"
   ]
  },
  {
   "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>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province/State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adams, IN</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alachua, FL</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alameda County, CA</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Williamson County, TN</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Worcester, MA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yolo County, CA</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>247 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Confirmed  Recovered  Deaths\n",
       "Province/State                                     \n",
       "Adams, IN                      0          0       0\n",
       "Alabama                       78          0       0\n",
       "Alachua, FL                    0          0       0\n",
       "Alameda County, CA             2          0       0\n",
       "Alaska                         9          0       0\n",
       "...                          ...        ...     ...\n",
       "Williamson County, TN          1          0       0\n",
       "Wisconsin                    159          1       0\n",
       "Worcester, MA                  0          0       0\n",
       "Wyoming                       18          0       0\n",
       "Yolo County, CA                1          0       0\n",
       "\n",
       "[247 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    data.loc[data['Country/Region'] == 'US']\n",
    "        .groupby(by=['Province/State']).sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import probscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=(6.5, 4))\n",
    "(\n",
    "    data.groupby(['Country/Region'])\n",
    "        .sum()\n",
    "        .loc[lambda df: df['Deaths'] > 1, 'Deaths']\n",
    "        .pipe(probscale.probplot, problabel='Non-Exceedance Probability (%)',\n",
    "              datalabel='Deaths', datascale='log', ax=ax, bestfit=True)\n",
    ")\n",
    "ax.set_ylim(bottom=1)\n",
    "seaborn.despine(ax=ax)\n",
    "fig.savefig('Probplot - Deaths.png', dpi=600, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=(6.5, 4))\n",
    "(\n",
    "    data.groupby(['Country/Region'])\n",
    "        .sum()\n",
    "        .loc[lambda df: df['Deaths'] > 1, 'Confirmed']\n",
    "        .pipe(probscale.probplot, problabel='Non-Exceedance Probability (%)',\n",
    "              datalabel='Confirmed Cases', datascale='log', ax=ax,\n",
    "              bestfit=True)\n",
    ")\n",
    "ax.set_ylim(bottom=1)\n",
    "seaborn.despine(ax=ax)\n",
    "fig.savefig('Probplot - Cases.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f1086974510>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hjKkGDgAfA36KtYn3DSJSBHwRay/YauAOEZkb26goNUn94ZMkJ8WzcnHOrI5LT02keEE6h3XPV1s1tfbOqh4fqHqTVbKZyTIHO9wtXL6umMSEuVuqgeCT/IPA//i/TgC+ALxgjPEB24HNwEXAS8aYEaAeWBNiW5WKCLeng/Vl+bMuD4AOvjphqt2gZurqDYtpPTWA59iFP3iHR8d5tX5uz6qZEOxG3j0AInIZ1mbde4Be/8P9WGWbOH/SD7zvDBHZCnw5mPhKhVOdp5PrLl0S1LGlJdm8frDd5hbNX6d7h+npH5nxHPnJcjKT2SiFvLDrGBVLc8/7vN0H2oiPi5vxlNloFvTAq4hsAR4AbsFK8BNJPAPoBgJHmybuO8MYs9UY4wq8BdsWpZzS3TfM4ZZuqmY56DqhtCSbI826sbddGlt7SE6KpzD33F25ZuraTYvZXtt8wQHxmtpmNq8vntGyCdEuqJ9ARCqAbwDvMMa0ALuwau9g9ex3A/tEZLOIJAKVgAm9uUqF195DneRkJgddAy4ryaZ/yNqLVIWuqbWXJQsziYsLvk942bpiRse87DFT/4U1NDzGzgNtc2qz7gsJ9mPqbiAHeEREtmHV3G8XkdeA7caYZuAe4F7gFeBBY4y9O+oqFQZ1HmtpYZcruKSSm5VCbmay1uVt0tTWG3Q9fkJyYjxbKhedd878zgNtJCfGUzXDJSyiXbA1+b+a4u5fTXrOUaxevVJzltvTwXvfUh7Sa0xcFLWlcm7Pt44GjSd6uHxd6Gu6V29azFf+6xX6B0dJn7TTU01tM1dULgpqoD0axcZPoZQDOroGaensn/GiZOejV77aw+fzTbuv60ytL8snOz2Jl+revMzBwNAouw+0zfkLoAJpklfqPNyeDhbmpVE0i8vnp1JWkqNz5W1wqmeI/sHRoMdHAsXFubhm4+JzVqZ8rb6VtNRE1pbGRqkGNMkrdV7uhg5bptCVlmRzqmeYrp4hG1o1fzW29pKanEBBTqotr3ftpiXsPdRJe9fZQfGa2ha2VC4iPoSB3WijSV6pKfh8Puo8nVSVh96jW5iXRlpKgg6+hmjiStdgB8EnW1acRemibP60x+rN9w2Osse0x8QFUIE0ySs1hePtfZzqGWK9DTMs4uJcWpe3wWx2g5qp6k1Wycbn8/HqvhNkZyTNajnpuUCTvFJTqPN0sKwok9zMFFteT5N86GazG9RMXbNxMc3tvRxq7qamtpktVYtCmoMfjTTJKzUFd0OnrZe0l2mSD4k1s8b+nnxeVgpV5QX8tuYwtQc7Yq5UA5rklTrHuNfHXpuTfGlJDidO9tM/OGrba84nHV2DDA6P296TB6jetITndx0jLzsFucB6NnOVJnmlJjnS3M3A8BjryhbY9pqLCzNITIjjSIv25oPR1NZLRmoiuZn2r1i+eX0xyUnxXFlVYtugbjSZm9uPK+Ugt6eD8sU5pKUkTv/kGUqIj2NZcRaHm7tZVxY7c7DDpfFEj60zawKlJifwhdsvoawk2/bXjgaa5JWaxO3poNKGqZOTlena8kFragtuN6iZmssbdU9HyzVKBRgd81J/5JQj64jrDJvgNbb22HKl63ykSV6pAKbxFD6fz5G50qUl2TS19TIyOm77a8cyr9fHsbY+R3vysUyTvFIB3J5OVi/PIynR/n09lxdngc9HY2uP7a8dy9pODTAyOq49+SBpklcqgNtjz3o1U0lJSqCkMENLNrPU2NpDdkYS2Rn2z6yZDzTJK+U3ODzGwaYuW9arOZ/SRTk6+DpLTa3ODrrGOk3ySvnVHz5JclI8KxfnOBajbLEOvs5WowNr1swnIU2hFJH7gOeBl7C2AJzYx/U2rA+Qh/wx7jfGPBxKLKWc5vZ0sK4039EdgUpLsnno6TcY9/piajlbJzW19vK/tqyIdDPmrGA38o4XkZ8A7/bftR74rjGm2n9rBr6ItRdsNXCHiGhBTUU1u5YWvpDSkmyGR8Zp6ehzNE6sGB/3cry9T3vyIQi2yxKP1Uv/sf/79cANIlIjInf777sIeMkYM4LVy18TUkuVclB33zCHW7odG3SdkJmWRGFuqtblZ6ils5+xcS/LdGZN0ILdyHsEeFpENvvvOoTVc98BPCYilwNxxhif//F+ICPwNURkK/DlYOIrZbd9h06Sk5Eclml6ExdFVW9c7Hisua6prZe8rBQy0pIi3ZQ5y65lDWqAAWOMV0Seweq1ewMezwDe1HUxxmwFtgbeJyI+lIoAt6eDypX5YVmgqrQkh/rDnY7HiQVNJ/RK11DZNcJ0H/BW/9dXAXuBfSKyWUQSgUrODsoqFXWs9WqcLdVMmFhb3ufTPs10Gh1es2Y+sCvJ/wvwBRHZDniMMTuBe4B7gVeAB40xwzbFUspWHV2DtHT2Oz7oOqG0JJvegVE6ugbDEm8um9jXVQUvpHKNv+QyoXrSY0eBa0J5faXCwe3pYGFeGkUL0sMSb0F2ClnpSRxq7qYwLy0sMeei0TEvLR19OugaIr0YSs17dQ3OLWUwFZdLN/aeiZaOPsa9Ppbo9MmQaJJX85rP58Pt6aRyZXg38tA9X6fX2NpDQW6qrZu3zEea5NW8dry9j1M9Q45sEnIhVk/+dFhjzjW6Zo09NMmrea3O08GyokxyM1PCGrdscQ6d3UN09+l8hPNpauvVK11toElezWvuhs6w1uMnFC9IJzU5Xks2F9B4oodlxZrkQ6VJXs1b414fexvCX48HiItzsbxY6/LnMzw6TuvJfpYu1HJNqDTJq3nrSHM3A0OjrCsLf5KH6Bl8HR3z8u8/3UXjiejZsep4Wy8+YPHCjGmfqy5Mk7yat9yeDsqX5JKeGpnZG6Ul2VGxUNljz3uoqW3mT68fj3RTzmhq66UoL52UJLtWXpm/NMmreauuoTPss2oClZZk09LZx+DwWMTa0NTaw6PPHmRDeQFuT0fE2jGZXulqH03yal4aHfNSf+QkVSvDP+g6YWlRFvFxLo60RKY3P+71cf+jtVyxvpgPvX0NDcdO0zc4GpG2TNbYqguT2UWTvJqXTOMpvF4fq1bkRawNiQlxLF2YFbG6/JMvHqG5o4+P/tl6Shdlk56ayN6G6Fgd0+rJ66CrHTTJq3nJ7elk9fI8khPjI9qOSC1v0H5qgJ88uZ+/edd6cjKTiYtzUbmygLooKNkMDo/RdmpA16yxiSZ5NS+5PeFdr+Z8IjH46vP5+PYv3axZsYBrN53duKSqooDaKEjyx9p6iYtzUVKgM2vsoElezTuDw2McbOoK29LCF1Jakk1Taw+jY97pn2yTbXuOs//wSe54X9WbNknZUF7A8fY+TnZHdgnkptZeihekkxThv7JihSZ5Ne/UHz5JclI8KxfnRLoprFiUxbjXx7G23rDE6+4b5vu/3scH/9fqc5Y5LlqQRmFuasRn2TS26pWudtIkr+Ydt6eDdaX5xMdH/vRPS0lkUX562BYr+/6v97GoIJ2bt5Se85jL5aKqvIDag5FN8taaNTroapfIn+VKhVmdpzMqSjUTSktywlKX37m/lRfrmvn7WzcQHzf1XrZV5QW4PZ0R3ZpQ93W1V0iXk4nIfcDzwHbgUawNux83xtwnIkuAh/wx7jfGPBxqY5UKVU//CIdbuvls+cZIN+WM0pJsdu5vdTTGwNAo3/llHbdeV3HB5Xsry/M51TPE8fa+iGzW0T84Smf3kM6ssVFQPXkRiReRnwDv9t/1ceCnWJt43yAiRcAXgbuxtgW8Q0SSQ2+uUqHZ29BJTkZyVPUUS0uyOdLSjdfrXO/5J08eIC0lgfddV3HB5+VmprC8OCtiJZtjbb0kxLtYpDNrbBNsuSYeq5f+Y//3lwMvGGN8WL36zcBFwEvGmBGgHlgTYluVCpnb00Hlyvw3zSqJtLKSbAaHxzne7szg6/4jJ3nq5aP8/a0bSEyY/le+KoJLHDS29lBSkEFCFIyXxIqg3kljzIgx5umAu7KAiTO0H6tsE+dP+oH3nSEiW0XEF3gLpi1KzYbb00FlFMyPD5SdkUzlynz+6XsvU9dgb3IdGR3n/kdrefuWFaxaNrOrezdUFLD3UCfj4+Gb1jlBr3S1n10fl72cTeIZQDcQeIZM3HeGMWarMcYVeLOpLUpNqaNrkJbO/qgadJ3wlb/dzHWXLOWfHnyZH/2u3rZ5848+e5CR0XH+8qbVMz5mbekChkfGaTge/u0Jdc0a+9mV5Hdh1d4BrgF2A/tEZLOIJAKVgLEpllJBqWvooDAvjaIF6ZFuyjkS4uP44E2ruedjV/Cn15v5/P3bae7oC+k1j7R089jzHu543wZSk2c+xyI1OQFZlhuRq1+tfV01ydvJriT/beB2EXkN2G6MaQbuAe4FXgEeNMboZpYqotyeDqoisAvUbKwry+f+O6spWpDOp+/bxtOvNAY1nXFihcmrLyph46rCWR9fVV5AnSe8i5X19I/Q1Tus5RqbhTSF0hizNeDbmyY9dhSrV69UxPl8PtyeTj7yjrWRbsq0MtKS+PwHL+a5ncd48Fd17DFtfPKWDWSmJc34NX5bc4j2rgG2fnRzUG2oKi/gF895GBoZC9vGHU2tPSQmxEXlX1pzmQ5hq3nheHsfp3qGIrpJyGy4XC6uv3Qp37yzms7Tg/z9f7ww40HZ1pP9/PSpN/jbP1tPVvrMPxgCybJcEhNc7D9yKqjjg9HU1suSwszzXqilgqNJXs0LdQ2dLC3KJDczJdJNmZVF+Rl89ZNXzXhQ1ufz8e1fuKkqz+eqDSVBx02Ij2NtaX5Ylx5uPNHDUl2zxnaa5NW8EC1LCwdjNoOyz+1swjR18Yn3VoV8LUBVeXiXHrbWrNEkbzdN8irmjXt97G3ojPpB1+lMNyjb1TPEfz1Rz4ffvob8nNSQ41WV53O4uZue/pGQX2s6Pp+PxhO9F1xyQQVHk7yKeUeauxkYGmVd2dxO8nB2UPbj76nkB0/s5d6f7KR3wErCD/5qL8uLs7jx8uW2xFpWlEV2enJYtgQ83TdM78CIzpF3gCZ5FfPqGjooX5JLempipJtiC5fLxXWXLOUbnz07KPuzpw7w2v5WPnlLFXE2DVxaWwLmh6Vk09TaS3JSPIW5adM/Wc2KJnkV89yezjkzq2Y2AgdlH33uIH9xg7C40N6ecFVFeNaxaWztYenCTNs+oNRZmuRVTBsd81J/5CRVK+fmoOt0JgZlf/Clt3LLdeW2v/6G8gJOdPbTfmrA9tcOZK1Zo6UaJ2iSVzHNNJ7C6/WxasXMFueaqwpyUx1ZWbMwL43iBemO9+abWnU3KKdoklcxze3pZPXyPJJ1U+igVVU4O5Wy9WQ/pqmLdWULHIsxn2mSVzGtrqEjJuvx4VRVnk+dg1sCPva8h3WlC6hYmuvI6893muRVzBocHsM0ds3Zi6CixfqyfLr7h2lstX9Tk5Pdgzy38xi3Xn/hHatU8DTJq5hVf/gkyUnxlC/OiXRT5rTsjGRWLMp2ZEvAx7c1ULY4m8o5fqFaNNMkr2KW29PButJ84nUruZBtcGBLwO6+Yf7wciO3Xl8RVdsxxho9+1XMqmuIzfnxkVBVUUD94U7GbNwS8DfbD1FSkM4lqxfa9prqXJrkVUzq6R/hSEu31uNtsmZFHqNjPg42ddnyen2Do/z+xSPccp324p2mSV7FpL0NnWSnJ+tWcjZJSUpg9fI83DbV5X//4mFyM1O4onKRLa+nzs/WLV9EpBE44v/2y8DdWJt4P26Muc/OWEpdiNvTQeXKfO0l2qiqPJ89pp33v21VSK8zNDzGb/50mL9+51rdICQMbOvJi8gy4HljTLUxphq4HPgpcBVwg4gU2RVLqelY8+O1VGOnqooCTGMXg8NjIb3OH15pJDUlgWs2LrapZepC7CzXrAfWi8h2EfkGVpJ/wRjjA7YDwW02qdQsdZ4epLmjnyoddLVV+eIckpPiqT98MujXGB0b51fbGnjvtStJ0FlPYWHnu9wB/F9jzNX+798JTFw90Y9VtjlDRLaKiC/wZmNb1Dzm9nRQmJemG0LbLD4+jvVl+SFNpXx25zHAx/WXLLWvYeqC7KzJ1wF7/F8/DZRiJfZe/79HA59sjNkKbA28TxO9soPb0zHnd4GKVlXlBfzx1cagjh0b9/LY89mvIyIAABQ2SURBVB7+7JqVJOlaQmFjZ0/+M8Bf+7++GngNqPZ/fw2w28ZYSk3J5/Ph9nTq1EmHVJXnc/RED6d7h2d97PbXjzM4NMqNm5fb3zB1XnYm+W8D7xKRbUAu8J/A7SLyGrDdGNNsYyylptTc0cepniG9TN4hSxZmkpeVTF3D7Eo2Xq+PXzzn4Z1Xl5GabOukPjUN295tY0w3cNOkuyd/r5Sj3J5OlhZlkpuVEummxCSXy0VleQG1Bzu4+qKZz455ee8JTnYP8fYtKxxsnZqKDm+rmOL2dGipxmET69jMdOlhn8/Ho88d5OYtK8hIS3K4dWoyTfIqZox7fext6NRBV4dVlRfQ3jVI68mZbQm4+412jrf38a6ryxxumZqKJnkVM460dDMwNMraMk3yTsrPSaWkIGNGUyl9Ph+PPnuQGy9fRk5mchhapybTJK9o7xrg588Y2hzerNlpdZ4OVi7JISM1MdJNiXkbZrgl4L5DJ/Ec6+Ld1SvD0Co1FU3y89jI6DiPPGP4+Fef54+vNvKpr73An/Ycj3SzgqZTJ8NnYktAr/fCdflHnz3IdZcsJT8nNUwtU5PpXKZ5yOfz8Wp9K//1m334gM/dtpHL1xXz5ItH+NbPX2f3G2187D2VpKXMnR7x6JiX+iMneY/2GMNifVk+/YPWcs5l59l562BTF3WHOvnu+64Lc+tUIE3y88zx9l6+/+t97DvUyfveUs573lJOsv/qw5uvLGXdynz+42e7+dTXtvG52zaxanleWNrVNzBCclICiQnB/XF5sKkLr9fHqhXhae98l5GWRNniHNyejvMm+UefPcjVG0ooztflJSJJk/w8MTA0ys+fOcgTNYe4ZE0R3/nH61iYl3bO85YVZfG1T1/Nj5/czxe+vYO/eKtwy3UVji0J29Taw8+fOUiN27pWbkFWCoV5aRTmpp35d2FeKoV5aRTkpJKYMPXl8G5PB6uX5535wFLO21BRgNvTyXuuLT/nsaMneti5v5Vvfe7aCLRMBdIkH+N8Ph/b9hznR7+rJz01kS//zeVsqCi84DFJifF89F3r2SiFfOOR13ndtPPZD2ya8kMhWI3+5P6iu5mLVxfx1TuuIjEhjrauAdpPDdDeNcDRlh5eq2+l7dQAg8NjuFyQm5nCwjMfAqkszEujIDeNXQfauGydrmYdTlXlBfxm+2FGx8bP+fD9xbMHuWxdMcuKsiLUOjVBk3wMO3T8NA/+ai9HT/TwgbcJb7+ydFbLu25atZAHPnct3/z563zqay/wifdWhbwG+NETPTzyjOHluhYuWVPE1/7hGlYG/Lm/csm5f/r7fD76B0dp8yf/tlODtHcN0NTay64DbbSdGmBgaIxPvK8qpLap2Vm9PA98Pt5o7GJ9wLTV5o4+drib+dqnr4lg69QETfIxqKd/hJ89dYCnX22keuNi7v7QJUFf5p+dkcz/+chlIQ/KHmnp5pFnDK/sPcFl64r5+meqKS3JntGxLpeLjLSkM3XgqUzVm1TOSkqMZ82KBbgPdrwpyT/2nIcNFYVTfmCr8NMkH0PGvT6efuUoP3vqAAvz0vjqHVfaMnDqcrmCHpQ90tLNw380vLrvBJevL+Ybn61mxaKZJffZ0AQfGZXl+bxa38pf3rQagPZTA7yw+xj/8vEtEW6ZmqBJfo7z+XwMDI3RcPw0P3yino7Tg3zo5jVcf+ky2wdLJw/K/vkNwq3XlRM/RQno0PHTPPKM4bX6VjavX+RYcleRtaGigJ/94Q36B0dJT03k8W0NrFqex9rSBZFumvLTJB+lhkfHOd07TFfvEF09w5zuHaKrd5iu3klf9wwxMuYlzgU3XbGCe25cRaaDi0BNNSh7521nB2Ubjp/mkT8adu5v5YrKRXzrzmtZVqyDb7GqtCSH1OQE9h3qpGJpLn98tZH//ZHLIt0sFcA105XkwkFEfMaYSDfDNj6fj+HRcfoHR/23MfqHRukbHGVgaPTM/X3+2+mABD4wZG2WHB/nIiczmdzMZHIyU/z/JpObmUJulv/fzGRys1LCvk53d98w3/z569QfPsltb1uF29PJrgOtXFlVwq03VOjMinniX3/0Gvk5qSTGx1F3qJP7Pn01LpczU27V1EQEY8yUb3rM9ORPdg8CkJ6SSHJSvCMn2eiYl+4+f++6d/hMT/t0zzBdfcMByXyUfn8SHxs/+yEaH+ciIy2R9JRE0lPP3jJSE1mYm4YszbUSdmYKOf5knpmWRJxDc9RDdWZQ9iVrHGDTqoU8cNdbWLIwM9JNU2G0oaKAX77QQG//MJ95/yZN8FEmJpL80PAYH//qcwwOjwNWMn1TEp2UVNNTE96caP0fDL39I28uh/QMc7rv7Ne9AyMAuFyQnZ58poedm5VCYW4amSVnX+/svwln4iQnOvPhE0kul4ubt6zgZt0MYt6qKi/gP39Zx9KiTC5bq9cqRBvHkryIJAAPAYuA14wxdzoVKyU5gZ995Sb6Bt9cApkoiZy5f2iMztODNJ4IuH9olL6BUcbGvaSnJpKTkfymMsiy4swzpRHrsRSy05OmHGxUaj5alJ/OsqJM3v+2VVH7V+d85mRP/r1AnTHmz0XkByJyiTFmp1PBkhLjyUuMJy/I+eDj415N3EoFweVy8cBdb4l0M9R5OJnkLwd+4f/6WeBK4EySF5GtwJcdjD8rmuCVUrHIySSfBfT6v+4HMgIfNMZsBbYG3ici0TPVRymlYoCT3ddezib2DKDbwVhKKaWm4GSS3wVU+79+C/Cag7GUUkpNwckk/yiwQUReBsaMMa84GEsppdQUHKvJG2NGgD936vWVUkpNT6eUKKVUDIu6K15FJNJNUEqp2OHz+WLiVlFR4Yvk8dHwM2h8ja/xNf7km5ZrlFIqhmmSV0qpGKZJXimlYlgsJfmvRPh4O0S6DRpf42v8GIsfVTtDKaWUslcs9eSVUkpNokleKaVimCZ5pZSKYXMqyYtIgn9bwWCPj+jeZCISLyJx/q/D/t6LSLqIrAl33ID4GSLyllD+D0OMH9L5Y0N8Pf/0/At77DmT5EXkk8CPgLtEJDOI45cC/yYiOXa3bYbx/wH4L+DTAMYYbwTi/wHYHM64AfE/BOwGvmWMGYtA/JDOHxvi6/mn59+PiMD5NyeSvIiUAn/G2e0CPygiK2f5MsuBjwLV4e7FiMgK4G3AvwLXicgH/PeHpR0isgr4JPB+4GcikheOuP7YSSLyIHA18GHgMRFJDOf/gf/9D/X8CdVy4G+IzPm3nMief0Lkzr9UEflPInv+LSeC51/ULVA2wf/GfBj4OVAObDPGHBKRHwP3AV0i0miMGb3A8dcDfzDGHAeWAr8BrsL6RD8WhvZfDzwJbAB+ZYzxiMjvgItE5JfGmOEwxH8aaAG+C9wPJAF9/sT7olNt8Me/AXgM+Kox5rD/T/U15/s/cyD+xPu/Atgxm/PHhvjZwGrgdf97vBL4FeE7/ybi7waqgN+H+fybiO8GmoEfAN8EUgjP+ZeNda69LCL3GmMa/Z2dcJ1/Ez//HqwP+NfCef4FisqevIjcCDwCJAO3AEuAy0XkCqAVOA0sxDphpjr+av/x5cAXReStwFPGmI8A48DbRCTJwfZPxBfgM0CcMeZ7IpIO3AYUAP/sP+mcjF8B3AVcBwwDB40xNwMPA5divT9Oxi8H7sFKcBhj9gM9IrLOibhTxK/Aev9LgU0ichnQxjTnj03eAdyJ9QsO8DtjzEeBMRw+/wLi34X1cz5ljHlARLKAD+Dw+RcQ/3NAMTAEDACHw3H+BcYXkQpjTCOAMeYNoNvp8y8g/l1Y7/UBrA2UZpS/7BZVST5gYCoH+KEx5m6gCziO1QuoBp4DDmLV9tLO81LjWD2XfwSeAC4DJv5jf+L/3skTbCL+Xf72rheRK4wx/cDVxpgPA+lYP6cTA3IT8T8PPAWUYSX5/wdgjPk11geQUwOBgfGfwPqAvtL/2BuA072XwPjbgETgCLAReJ7pz5+giYhLRBKBG7F+kS8RkRxjTLv/KT/CwfNvUvwCrN+ZLABjTI8xxtHzb1L8QuBKrIrB74AH/e1w7Pyb4ue/VERy/Y9lYiVcx86/KeJfb4xpw/rZr2Zm+ctWUZXkjTETl9/2Ay/4vz4FXGOMeQb4NlZd63GsT8PU87xULjAkIqnAS8BRYKOIJBtj9mH9wr/f/7gTJsc/BFwmImuBKn+PcglW6STw53Yq/gmsX+qlInKjiGzB2lw90ea4U8V/Gev9vtj/WCH+wTcH66KB8V/EOleOYJWOvsj0509I/H+C3w/8B1apaE3AYweAwzh4/k0RXwBEZKWIbHL6/JsUfzlwkTHmEJAsIjf7e7SOnX9T/Pyr/ff3AiU4fP5N/vlF5DJjzG+A7wBbcfj8myyiNXkRKQK+hNW72uevGbqMMb8NeNr1wEP+rwuxTs5vAP+G9Yv8f7B6ix5jzID/eTuxBjrWGmN2iUgD1p/sKVg92u8AFcaYQRva/9EZxD+E9am+FKs+fxPwdWPM9jDFP4L13hUBlcBbgfuNMXvCFL+Bsz3XJ4HPAj8KdYbHLOI3Yv2yF2OVcL4F3GuMOepA/ERgBKsG6/OPDVwkIkeMMSf8h/4nzp1/U8XfICJ1WD33G3D2/Jsq/kYR2YvVc12Ps+ff+d7/Y8aYY8Bvcfb8myr+xf4ckIiVh+7DhvNvpiK2do2IVABfB2qAdqwBqY9M9CpEpNQ/WHc/Vl317/3P+5UxZsA/Ov0A1sASwCuBHw4i8i9AB/B7rDrYfwN/aYwZsqn9s4nfhjXw9MEIx7891MQSZPyJ9/9D/pJVJOL/kPC+/yuNMQ3+8/x24DWsEtJ4BOJ/CHgVa1zAlqmTQcaP1M9/O9YH/5N2DXQG+f//Owj/9NVIXBAx8aerFxgwxtyLVTdu9X/yXSsizwJv9Y9Q3wr8CViFNc92ecDxQ8aYL2HNVFjqf/0rReQprF/w01gfEE8BBvCGWn+c1P6Zxn8Sqw4X6fjjEYo/8f6PRjB+uN7/K0TkeazeKsaYg1jlOp//FpIg4zf4Y4dcAw8xfshCeP+9RPb/H8AV7gQPYezJizU39odYteEarAG4F4wxHWKN8n8duBl4F3DM/2d2JdafVt8EGicd78aaVtntf1OHseq/XcAfjTHGH3cZkG2MqbO5/Rpf458v/h+MMZ6AY+ND7cFq/PkdPxTh7MlXAYeMMTcArwB3GGM6/I/dDNT4P+V+b4zZBWCMqTPGfNgY8/oUx3/GGNPtP/6fgPdifbrGGWOMWKPcccaYxlB/wc/Tfo2v8c8X3+OP7wKw6Rdc48/v+EFzPMkH/HnUAqwTkXRjzB+BBhG52/9YBvCEiHwU+H8ismAGxx8Skc9PPGasQY8UrNoXxhifHX8aaXyNH0J8O8ozGn8ex7eDI0lerGlaC8H6YcValKcZq7b+Xv/T/gmr7h6PNRPi11jzmL+GNe1oJsffJCLVwCf9fzItw6r9hqv9Gl/ja3yNb2t8u9lak/e/Gd/DuvimAWvE+fsBj78FqzTzLWNdZvyvWBcnvQtr9H3HLI/fijUdDaDIGOMOc/s1vsbX+BrflvhOsSXJ+9+ceP/tK8aYu0SkDGvA9BNYU9j+Haj3P+daoAnrApmbsC71Dvb4dxhj+iLYfo2v8TW+xo9aIZdrRORTWOuE/COQCVwpIsuMdYXbDuAjWHPg44CfGWO+C/wU6wrEdwJ/G8rxNvwHh9R+ja/xNb7Gj2Yh9eTFWujnn7GmOf4b1sJDBVjLENwu1jz3J4BPGGPq7T4+VBpf42t8jR+p+OEy6568iCwRkbeLtejPFcB2Y12e+yDwHmPMD4BFIlJtrClGz+BfhVCsaUUhHR/qD6zxNb7G1/iRih8Js1q7RkSuxVok7A9YAxD/DUxM+l+AdcESWGvD3CEi78Ba5vYR//3VoRxvQpySFGr7Nb7G1/gaf66ZUZIXkVuAbKyrvL5mjPmBiHwC2AL0YV3ltQn/HFGs5TR3Y632di/WbjjVwR5vzl40FZRQ26/xNb7G1/hz1QWTvFhz2L+HtXrcUaye+GH/w7/DWldmlYh4sNaszhKRx7HeoH/FWgc+6OONf7H/YIXafo2v8TW+xp/rpqvJ+4Ae4A6syf9ZwNtFpNAY04R1gcA6rD91bsXaxel/jDH/4v/TJtTjQ6XxNb7G1/iRih8VpkvycVg76/Qba4nOdqyFxe70P/40sM4Y0wp83hjzDmPMYzYeHyqNr/E1vsaPVPyoMOMplGItsXmfMeZGEfkS1qffFqxF+Kf95Av1+FBpfI2v8TV+pOJH0mxm1ywEfuufepSC9Sn4sDFmZ5iOD5XG1/gaX+NHKn7EzCbJXwR8AXg38BNjzNOzjBXq8aHS+Bpf42v8SMWPmNkk+TGsFSK/bowZCSJWqMeHSuNrfI2v8SMVP3J8Pt+MbhUVFXEzfa4Tx4d60/gaX+Nr/Pl4i9hG3koppZwX9o28lVJKhY8meaWUimGa5JVSKoZpkldKqRg2q6WGlYpF/hVSH8G65D0OGAe+YIx59TzP/6gJ2PtTqWimPXmlLH8wxlQbY64GPgz8t4iknee5Xwpfs5QKjfbklZrEGNMoIjXAbSLybiAJaz3yvwXeBhSJyL9j7ZXw31irG/YBf22MaY9Qs5WakvbklZpaOzAC/LMx5nrgfuADxph7gVZjzOeBu4FfGmMmdhzaGqnGKnU+2pNXampLgWeBT4nI3wG5WBtPBFoLXC0iH8HqMHWGtYVKzYD25JWaRERWABcD/wD80BjzV8BeYPJGzgZrB6FqrDXKnwhnO5WaCe3JK2W5UUS2Yc2s8QEfBFYD3xGRNuBEwHObRGSiPPMDEbkTSAY+FtYWKzUDunaNUkrFMC3XKKVUDNMkr5RSMUyTvFJKxTBN8kopFcM0ySulVAzTJK+UUjFMk7xSSsWw/w9i6pIYCdzYrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    data.loc[\n",
    "        data['Province/State'].str.endswith(', WA') | \n",
    "        data['Province/State'].eq('Washington')\n",
    "    ]\n",
    "    .groupby('Date').sum()['Confirmed']\n",
    "    .loc[lambda x: x > 1]\n",
    "    .plot(subplots=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Adams, IN',\n",
       " 'Alabama',\n",
       " 'Alachua, FL',\n",
       " 'Alameda County, CA',\n",
       " 'Alaska',\n",
       " 'Anoka, MN',\n",
       " 'Arapahoe, CO',\n",
       " 'Arizona',\n",
       " 'Arkansas',\n",
       " 'Arlington, VA',\n",
       " 'Beadle, SD',\n",
       " 'Bennington County, VT',\n",
       " 'Bergen County, NJ',\n",
       " 'Berkshire County, MA',\n",
       " 'Bernalillo, NM',\n",
       " 'Bon Homme, SD',\n",
       " 'Boone, IN',\n",
       " 'Broward County, FL',\n",
       " 'Bucks, PA',\n",
       " 'Burlington, NJ',\n",
       " 'Calaveras, CA',\n",
       " 'California',\n",
       " 'Camden, NC',\n",
       " 'Camden, NJ',\n",
       " 'Carver County, MN',\n",
       " 'Charles Mix, SD',\n",
       " 'Charleston County, SC',\n",
       " 'Charlotte County, FL',\n",
       " 'Charlton, GA',\n",
       " 'Chatham County, NC',\n",
       " 'Cherokee County, GA',\n",
       " 'Clark County, NV',\n",
       " 'Clark County, WA',\n",
       " 'Cobb County, GA',\n",
       " 'Collier, FL',\n",
       " 'Collin County, TX',\n",
       " 'Colorado',\n",
       " 'Connecticut',\n",
       " 'Contra Costa County, CA',\n",
       " 'Cook County, IL',\n",
       " 'Cuyahoga, OH',\n",
       " 'Dallas, TX',\n",
       " 'Dane, WI',\n",
       " 'Davidson County, TN',\n",
       " 'Davis County, UT',\n",
       " 'Davison, SD',\n",
       " 'DeKalb, GA',\n",
       " 'Delaware',\n",
       " 'Delaware County, PA',\n",
       " 'Denver County, CO',\n",
       " 'Deschutes, OR',\n",
       " 'Diamond Princess',\n",
       " 'District of Columbia',\n",
       " 'Douglas County, CO',\n",
       " 'Douglas County, NE',\n",
       " 'Douglas County, OR',\n",
       " 'Eagle, CO',\n",
       " 'El Paso County, CO',\n",
       " 'Essex, MA',\n",
       " 'Fairfax County, VA',\n",
       " 'Fairfield, CT',\n",
       " 'Fayette County, KY',\n",
       " 'Fayette, GA',\n",
       " 'Florida',\n",
       " 'Floyd, GA',\n",
       " 'Fort Bend County, TX',\n",
       " 'Fresno County, CA',\n",
       " 'Fulton County, GA',\n",
       " 'Georgia',\n",
       " 'Grafton County, NH',\n",
       " 'Grand Princess',\n",
       " 'Grant County, WA',\n",
       " 'Gregg, TX',\n",
       " 'Guam',\n",
       " 'Gunnison, CO',\n",
       " 'Gwinnett, GA',\n",
       " 'Hanover, VA',\n",
       " 'Harford County, MD',\n",
       " 'Harris County, TX',\n",
       " 'Harrison County, KY',\n",
       " 'Hawaii',\n",
       " 'Hendricks County, IN',\n",
       " 'Hillsborough, FL',\n",
       " 'Honolulu County, HI',\n",
       " 'Howard, IN',\n",
       " 'Hudson County, NJ',\n",
       " 'Humboldt County, CA',\n",
       " 'Idaho',\n",
       " 'Illinois',\n",
       " 'Indiana',\n",
       " 'Iowa',\n",
       " 'Island, WA',\n",
       " 'Jackson County, OR ',\n",
       " 'Jefferson County, KY',\n",
       " 'Jefferson County, WA',\n",
       " 'Jefferson Parish, LA',\n",
       " 'Jefferson, CO',\n",
       " 'Johnson County, IA',\n",
       " 'Johnson County, KS',\n",
       " 'Johnson, IN',\n",
       " 'Kane, IL',\n",
       " 'Kansas',\n",
       " 'Kentucky',\n",
       " 'Kershaw County, SC',\n",
       " 'King County, WA',\n",
       " 'Kitsap, WA',\n",
       " 'Kittitas County, WA',\n",
       " 'Klamath County, OR',\n",
       " 'Knox, NE',\n",
       " 'Lake, IL',\n",
       " 'Lancaster, SC',\n",
       " 'Larimer, CO',\n",
       " 'Lee County, FL',\n",
       " 'Litchfield, CT',\n",
       " 'Los Angeles, CA',\n",
       " 'Loudoun, VA',\n",
       " 'Louisiana',\n",
       " 'Madera County, CA',\n",
       " 'Maine',\n",
       " 'Manatee County, FL',\n",
       " 'Maricopa County, AZ',\n",
       " 'Marin, CA',\n",
       " 'Marion County, IN',\n",
       " 'Marion County, OR',\n",
       " 'Maryland',\n",
       " 'Massachusetts',\n",
       " 'McHenry, IL',\n",
       " 'Michigan',\n",
       " 'Middlesex County, MA',\n",
       " 'Middlesex, NJ',\n",
       " 'Minnehaha, SD',\n",
       " 'Minnesota',\n",
       " 'Mississippi',\n",
       " 'Missouri',\n",
       " 'Monmouth, NJ',\n",
       " 'Monroe, PA',\n",
       " 'Montana',\n",
       " 'Montgomery County, MD',\n",
       " 'Montgomery County, PA',\n",
       " 'Montgomery, TX',\n",
       " 'Multnomah, OR',\n",
       " 'Napa, CA',\n",
       " 'Nassau County, NY',\n",
       " 'Nassau, FL',\n",
       " 'Nebraska',\n",
       " 'Nevada',\n",
       " 'New Castle, DE',\n",
       " 'New Hampshire',\n",
       " 'New Jersey',\n",
       " 'New Mexico',\n",
       " 'New York',\n",
       " 'New York County, NY',\n",
       " 'Noble, IN',\n",
       " 'Norfolk County, MA',\n",
       " 'Norfolk, VA',\n",
       " 'North Carolina',\n",
       " 'North Dakota',\n",
       " 'Oakland, MI',\n",
       " 'Ohio',\n",
       " 'Okaloosa County, FL',\n",
       " 'Oklahoma',\n",
       " 'Olmsted, MN',\n",
       " 'Orange County, CA',\n",
       " 'Oregon',\n",
       " 'Orleans, LA',\n",
       " 'Pasco, FL',\n",
       " 'Passaic, NJ',\n",
       " 'Pennington, SD',\n",
       " 'Pennsylvania',\n",
       " 'Philadelphia, PA',\n",
       " 'Pierce County, WA',\n",
       " 'Pierce, WI',\n",
       " 'Pima, AZ',\n",
       " 'Pinal County, AZ',\n",
       " 'Pinellas, FL',\n",
       " 'Placer County, CA',\n",
       " 'Polk County, GA',\n",
       " 'Polk, OR',\n",
       " 'Pottawattamie, IA',\n",
       " \"Prince George's, MD\",\n",
       " 'Providence County, RI',\n",
       " 'Puerto Rico',\n",
       " 'Ramsey County, MN',\n",
       " 'Rhode Island',\n",
       " 'Riverside County, CA',\n",
       " 'Rockingham County, NH',\n",
       " 'Rockland County, NY',\n",
       " 'Sacramento County, CA',\n",
       " 'San Benito, CA',\n",
       " 'San Diego County, CA',\n",
       " 'San Francisco County, CA',\n",
       " 'San Joaquin, CA',\n",
       " 'San Mateo, CA',\n",
       " 'Santa Clara County, CA',\n",
       " 'Santa Cruz, CA',\n",
       " 'Santa Rosa County, FL',\n",
       " 'Saratoga County, NY',\n",
       " 'Shasta County, CA',\n",
       " 'Shelby County, TN',\n",
       " 'Skagit, WA',\n",
       " 'Snohomish County, WA',\n",
       " 'Socorro, NM',\n",
       " 'Solano, CA',\n",
       " 'Sonoma County, CA',\n",
       " 'South Carolina',\n",
       " 'South Dakota',\n",
       " 'Spartanburg County, SC',\n",
       " 'Spotsylvania, VA',\n",
       " 'St. Joseph, IN',\n",
       " 'St. Louis County, MO',\n",
       " 'Stanislaus, CA',\n",
       " 'Stark, OH',\n",
       " 'Suffolk County, MA',\n",
       " 'Suffolk County, NY',\n",
       " 'Sullivan, TN',\n",
       " 'Summit County, CO',\n",
       " 'Summit, UT',\n",
       " 'Tarrant, TX',\n",
       " 'Tennessee',\n",
       " 'Texas',\n",
       " 'Thurston, WA',\n",
       " 'Tulsa County, OK',\n",
       " 'Ulster County, NY',\n",
       " 'Umatilla, OR',\n",
       " 'Union, NJ',\n",
       " 'Utah',\n",
       " 'Ventura, CA',\n",
       " 'Vermont',\n",
       " 'Virgin Islands',\n",
       " 'Virginia',\n",
       " 'Volusia County, FL',\n",
       " 'Wake County, NC',\n",
       " 'Washington',\n",
       " 'Washington County, OR',\n",
       " 'Washington, D.C.',\n",
       " 'Washoe County, NV',\n",
       " 'Wayne County, PA',\n",
       " 'Wayne, MI',\n",
       " 'Weber, UT',\n",
       " 'West Virginia',\n",
       " 'Westchester County, NY',\n",
       " 'Whatcom, WA',\n",
       " 'Williamson County, TN',\n",
       " 'Wisconsin',\n",
       " 'Worcester, MA',\n",
       " 'Wyoming',\n",
       " 'Yolo County, CA']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(data.loc[data['Country/Region'] == 'US', 'Province/State'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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