corona.ipynb

{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.5-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python37564bitcoronapipenveb1ea60fb4664f9da282b082967fd3b5",
   "display_name": "Python 3.7.5 64-bit ('corona': pipenv)"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from datetime import date, timedelta\n",
    "\n",
    "def daterange(start_date, end_date):\n",
    "    for n in range(int((end_date - start_date).days)):\n",
    "        yield start_date + timedelta(n)\n",
    "\n",
    "api_netloc = \"https://covid19.mathdro.id\"\n",
    "start_date = date(2020, 2, 25)\n",
    "end_date = date.today()\n",
    "\n",
    "# Fetch data from API\n",
    "data = {}\n",
    "for single_date in daterange(start_date, end_date):\n",
    "    r = requests.get(\n",
    "        api_netloc\n",
    "        + f\"/api/daily/{single_date.month}-{single_date.day}-{single_date.year}\"\n",
    "    )\n",
    "\n",
    "    data[single_date] = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "countryRegion           lastUpdate  confirmed  deaths  recovered  \\\n2020-02-25       Germany  2020-02-25T21:33:02         17       0          0   \n2020-02-26       Germany  2020-02-26T23:43:03         27       0          0   \n2020-02-27       Germany  2020-02-27T23:13:04         46       0          0   \n2020-02-28       Germany  2020-02-28T00:13:18         48       0          0   \n2020-02-29       Germany  2020-02-29T14:43:03         79       0          0   \n\n            day  \n2020-02-25   56  \n2020-02-26   57  \n2020-02-27   58  \n2020-02-28   59  \n2020-02-29   60  \n"
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "# Filter out for Germany\n",
    "data_germany = {}\n",
    "for single_day in data:\n",
    "    data_germany_single = filter(\n",
    "        lambda elem: elem[\"countryRegion\"] == \"Germany\", data[single_day]\n",
    "    )\n",
    "    data_germany[single_day] = list(data_germany_single)[0]\n",
    "\n",
    "# create a dataframe\n",
    "df_germany = pandas.DataFrame.from_dict(data_germany, orient=\"index\")\n",
    "df_germany['day'] = pandas.to_datetime(df_germany.index).dayofyear\n",
    "df_germany = df_germany.drop(\n",
    "    columns=[\"provinceState\", \"latitude\", \"longitude\", \"\\ufeffprovinceState\"]\n",
    ")\n",
    "\n",
    "df_germany.confirmed = df_germany.confirmed.astype(int)\n",
    "df_germany.deaths = df_germany.deaths.astype(int)\n",
    "df_germany.recovered = df_germany.deaths.astype(int)\n",
    "\n",
    "\n",
    "print(df_germany.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x13c6555d0>"
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# curve fit\n",
    "from scipy.optimize import curve_fit\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def func(x, a, b, c):\n",
    "    return a*np.exp(b*x - c)\n",
    "\n",
    "popt, pcov = curve_fit(func, df_germany.day, df_germany.confirmed)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.lineplot(x=df_germany.day, y=func(df_germany.day, *popt), ax=ax)\n",
    "sns.lineplot(x=df_germany.day, y=\"confirmed\", data=df_germany, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x13876ff10>"
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# extrapolate\n",
    "from datetime import date, timedelta\n",
    "time_travel = timedelta(days=7)\n",
    "\n",
    "new_x_data = [(date+time_travel).timetuple().tm_yday for date in df_germany.index]\n",
    "new_y_data = [func(data_point, *popt) for data_point in new_x_data]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.lineplot(x=df_germany.day, y=\"confirmed\", data=df_germany, ax=ax)\n",
    "sns.lineplot(x=new_x_data, y=new_y_data)"
   ]
  }
 ]
}