visualise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisations and descriptive statistics\n",
    "\n",
    "This notebook contains some basic visualisations and descriptive statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "figsize = (12, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/thead/miniconda/envs/sk-playground/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dataframes = []\n",
    "\n",
    "for f in glob.glob(\"fallzahlen_kanton_total_csv/COVID19_Fallzahlen_Kanton_*total.csv\"):\n",
    "    dataframes.append(pd.read_csv(f))\n",
    "\n",
    "df = pd.concat(dataframes)\n",
    "\n",
    "df['date'] = pd.to_datetime(df['date'], dayfirst=True)"
   ]
  },
  {
   "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 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>TotalCured</th>\n",
       "      <th>TotalPosTests1</th>\n",
       "      <th>abbreviation_canton_and_fl</th>\n",
       "      <th>date</th>\n",
       "      <th>ncumul_ICU</th>\n",
       "      <th>ncumul_conf</th>\n",
       "      <th>ncumul_deceased</th>\n",
       "      <th>ncumul_hosp</th>\n",
       "      <th>ncumul_released</th>\n",
       "      <th>ncumul_tested</th>\n",
       "      <th>ncumul_vent</th>\n",
       "      <th>source</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AG</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ag.ch/media/kanton_aargau/themen_1...</td>\n",
       "      <td>15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AG</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ag.ch/media/kanton_aargau/themen_1...</td>\n",
       "      <td>18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AG</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ag.ch/media/kanton_aargau/themen_1...</td>\n",
       "      <td>15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AG</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ag.ch/media/kanton_aargau/themen_1...</td>\n",
       "      <td>15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AG</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ag.ch/media/kanton_aargau/themen_1...</td>\n",
       "      <td>15:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TotalCured  TotalPosTests1 abbreviation_canton_and_fl       date  \\\n",
       "0         NaN             NaN                         AG 2020-02-28   \n",
       "1         NaN             NaN                         AG 2020-03-02   \n",
       "2         NaN             NaN                         AG 2020-03-03   \n",
       "3         NaN             NaN                         AG 2020-03-04   \n",
       "4         NaN             NaN                         AG 2020-03-05   \n",
       "\n",
       "   ncumul_ICU  ncumul_conf  ncumul_deceased  ncumul_hosp  ncumul_released  \\\n",
       "0         NaN          1.0              NaN          NaN              NaN   \n",
       "1         NaN          2.0              NaN          NaN              NaN   \n",
       "2         NaN          6.0              NaN          NaN              NaN   \n",
       "3         NaN          7.0              NaN          NaN              NaN   \n",
       "4         NaN          9.0              NaN          NaN              NaN   \n",
       "\n",
       "   ncumul_tested  ncumul_vent  \\\n",
       "0            NaN          NaN   \n",
       "1            NaN          NaN   \n",
       "2            NaN          NaN   \n",
       "3            NaN          NaN   \n",
       "4            NaN          NaN   \n",
       "\n",
       "                                              source   time  \n",
       "0  https://www.ag.ch/media/kanton_aargau/themen_1...  15:00  \n",
       "1  https://www.ag.ch/media/kanton_aargau/themen_1...  18:00  \n",
       "2  https://www.ag.ch/media/kanton_aargau/themen_1...  15:00  \n",
       "3  https://www.ag.ch/media/kanton_aargau/themen_1...  15:00  \n",
       "4  https://www.ag.ch/media/kanton_aargau/themen_1...  15:00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_df(df, column, pretty_name):\n",
    "    \"\"\"Pick out one column of interest\"\"\"\n",
    "    df = (df\n",
    "          .rename(columns={\"abbreviation_canton_and_fl\": \"Canton\",\n",
    "                           column: pretty_name,\n",
    "                           \"date\": \"Date\"})\n",
    "          .pivot_table(index=\"Date\",\n",
    "                       values=[pretty_name],\n",
    "                       columns=['Canton'])\n",
    "         )\n",
    "    # Create a row for every day\n",
    "    all_days = pd.date_range(df.index.min(), df.index.max(), freq='D')\n",
    "    df = df.reindex(all_days)\n",
    "    # Fill missing values with previous day's number\n",
    "    df.fillna(method='pad', inplace=True)\n",
    "    # Now there are only missing values at the start\n",
    "    # of the series, so set them to zero\n",
    "    df.fillna(value=0, inplace=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "def time_to_double(series):\n",
    "    \"\"\"Compute how long it took for the most recent doubling\"\"\"\n",
    "    last_day = series.index[-1]\n",
    "    current = series[last_day]\n",
    "    half = current / 2\n",
    "    \n",
    "    half_day = series.where(series < half).dropna().index[-1]\n",
    "    return last_day - half_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deaths\n",
    "\n",
    "Confirmed deaths is a quantity that is known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "deaths_per_canton = transform_df(df, \"ncumul_deceased\", \"Deaths\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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></th>\n",
       "      <th>2020-03-17 00:00:00</th>\n",
       "      <th>2020-03-18 00:00:00</th>\n",
       "      <th>2020-03-19 00:00:00</th>\n",
       "      <th>2020-03-20 00:00:00</th>\n",
       "      <th>2020-03-21 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Canton</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 rowspan=\"12\" valign=\"top\">Deaths</th>\n",
       "      <th>AG</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BL</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BS</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TI</th>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VD</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VS</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZH</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               2020-03-17  2020-03-18  2020-03-19  2020-03-20  2020-03-21\n",
       "       Canton                                                            \n",
       "Deaths AG             0.0         0.0         0.0         1.0         1.0\n",
       "       BE             1.0         1.0         1.0         2.0         2.0\n",
       "       BL             2.0         2.0         2.0         3.0         3.0\n",
       "       BS             4.0         4.0         4.0         4.0         5.0\n",
       "       FR             0.0         1.0         1.0         1.0         1.0\n",
       "       GE             0.0         0.0         0.0         7.0         7.0\n",
       "       GR             0.0         1.0         1.0         3.0         3.0\n",
       "       NE             0.0         0.0         0.0         2.0         2.0\n",
       "       TI            10.0        14.0        15.0        22.0        28.0\n",
       "       VD             5.0         5.0         7.0        12.0        12.0\n",
       "       VS             3.0         3.0         4.0         5.0         5.0\n",
       "       ZH             1.0         1.0         3.0         3.0         3.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double check\n",
    "deaths_per_canton.tail().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch = deaths_per_canton.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It took 3 days for the number of deaths to double.\n"
     ]
    }
   ],
   "source": [
    "print(\"It took {} days for the number of deaths to double.\".format(time_to_double(ch).days))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = ch.plot(legend=None, title=\"Deaths across Switzerland\")\n",
    "ax.set_ylabel(\"Deaths\");\n",
    "ax.set_yscale('log', nonposy='clip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confirmed cases\n",
    "\n",
    "Read https://ourworldindata.org/coronavirus#cases-of-covid-19 for caveats when it comes to interpreting these numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_per_canton = transform_df(df, \"ncumul_conf\", \"Cases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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></th>\n",
       "      <th>2020-03-17 00:00:00</th>\n",
       "      <th>2020-03-18 00:00:00</th>\n",
       "      <th>2020-03-19 00:00:00</th>\n",
       "      <th>2020-03-20 00:00:00</th>\n",
       "      <th>2020-03-21 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Canton</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 rowspan=\"25\" valign=\"top\">Cases</th>\n",
       "      <th>AG</th>\n",
       "      <td>67.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>123.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BL</th>\n",
       "      <td>89.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BS</th>\n",
       "      <td>165.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>873.0</td>\n",
       "      <td>873.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GL</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR</th>\n",
       "      <td>0.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JU</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NW</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OW</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG</th>\n",
       "      <td>47.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SH</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SZ</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TG</th>\n",
       "      <td>23.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TI</th>\n",
       "      <td>422.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UR</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VD</th>\n",
       "      <td>608.0</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>1432.0</td>\n",
       "      <td>1432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VS</th>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZG</th>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZH</th>\n",
       "      <td>294.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>773.0</td>\n",
       "      <td>773.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              2020-03-17  2020-03-18  2020-03-19  2020-03-20  2020-03-21\n",
       "      Canton                                                            \n",
       "Cases AG            67.0       101.0       118.0       165.0       165.0\n",
       "      AI             5.0         5.0         6.0         6.0         6.0\n",
       "      AR             5.0        11.0        11.0        11.0        11.0\n",
       "      BE           123.0       193.0       282.0       377.0       377.0\n",
       "      BL            89.0       116.0       134.0       184.0       282.0\n",
       "      BS           165.0       182.0       222.0       272.0       299.0\n",
       "      FR            11.0        11.0        11.0        11.0        11.0\n",
       "      GE             0.0         0.0         0.0       873.0       873.0\n",
       "      GL            10.0        10.0        17.0        17.0        17.0\n",
       "      GR             0.0       116.0       145.0       213.0       213.0\n",
       "      JU             0.0         0.0        27.0        29.0        29.0\n",
       "      LU             0.0        65.0        65.0        92.0       109.0\n",
       "      NE             0.0         0.0         0.0       159.0       159.0\n",
       "      NW             4.0         4.0        25.0        28.0        28.0\n",
       "      OW             1.0         1.0         1.0         1.0         1.0\n",
       "      SG            47.0        61.0        85.0        98.0        98.0\n",
       "      SH             0.0         0.0         0.0        14.0        14.0\n",
       "      SZ            13.0        13.0        13.0        13.0        13.0\n",
       "      TG            23.0        32.0        36.0        49.0        56.0\n",
       "      TI           422.0       511.0       638.0       834.0       918.0\n",
       "      UR             2.0         5.0         7.0         7.0        12.0\n",
       "      VD           608.0       796.0      1212.0      1432.0      1432.0\n",
       "      VS            95.0        95.0       232.0       282.0       282.0\n",
       "      ZG            24.0        24.0        24.0        48.0        48.0\n",
       "      ZH           294.0       424.0       526.0       773.0       773.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the last few days to double check data\n",
    "confirmed_per_canton.tail().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is a pretty messy plot, but it can serve as a base\n",
    "# for picking out a few cantons or some such\n",
    "ax = confirmed_per_canton.plot(legend=None, title=\"Confirmed cases per canton\")\n",
    "ax.set_yscale('log', nonposy='clip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Switzerland wide cases\n",
    "ch = confirmed_per_canton.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It took 3 days for the number of confirmed cases to double.\n"
     ]
    }
   ],
   "source": [
    "print(\"It took {} days for the number of confirmed cases to double.\".format(time_to_double(ch).days))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = ch.plot(legend=None, title=\"Confirmed cases across Switzerland\")\n",
    "ax.set_ylabel(\"Cases\");\n",
    "ax.set_yscale('log', nonposy='clip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created by Tim Head <betatim@gmail.com> licensed under CC-BY-4."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}