laowantong/.gitignore

## .gitignore
.ipynb_checkpoints/*

## covid-19-france.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prerequisites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install COVID19Py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_rows\", 365)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from COVID19Py import COVID19\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context(\"paper\", font_scale=2)\n",
    "sns.set_palette(sns.color_palette(\"Set1\", 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the last data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_code = \"FR\"\n",
    "data = COVID19().getLocationByCountryCode(country_code, timelines=True)\n",
    "for province_data in data:\n",
    "    if province_data[\"province\"] == '':\n",
    "        break\n",
    "timelines = province_data[\"timelines\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cumulative deaths / confirmed cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "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>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05-17</th>\n",
       "      <td>28062</td>\n",
       "      <td>177240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-18</th>\n",
       "      <td>28193</td>\n",
       "      <td>177554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-19</th>\n",
       "      <td>27976</td>\n",
       "      <td>178428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-20</th>\n",
       "      <td>28084</td>\n",
       "      <td>179069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-21</th>\n",
       "      <td>28167</td>\n",
       "      <td>179306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-22</th>\n",
       "      <td>28241</td>\n",
       "      <td>179645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-23</th>\n",
       "      <td>28284</td>\n",
       "      <td>179964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-24</th>\n",
       "      <td>28317</td>\n",
       "      <td>179859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-25</th>\n",
       "      <td>28407</td>\n",
       "      <td>180166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-26</th>\n",
       "      <td>28480</td>\n",
       "      <td>179887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-27</th>\n",
       "      <td>28546</td>\n",
       "      <td>180044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-28</th>\n",
       "      <td>28611</td>\n",
       "      <td>183309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-29</th>\n",
       "      <td>28663</td>\n",
       "      <td>183816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-30</th>\n",
       "      <td>28720</td>\n",
       "      <td>185616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       deaths  confirmed\n",
       "05-17   28062     177240\n",
       "05-18   28193     177554\n",
       "05-19   27976     178428\n",
       "05-20   28084     179069\n",
       "05-21   28167     179306\n",
       "05-22   28241     179645\n",
       "05-23   28284     179964\n",
       "05-24   28317     179859\n",
       "05-25   28407     180166\n",
       "05-26   28480     179887\n",
       "05-27   28546     180044\n",
       "05-28   28611     183309\n",
       "05-29   28663     183816\n",
       "05-30   28720     185616"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "for label in (\"deaths\", \"confirmed\"):\n",
    "    frames.append(pd.DataFrame.from_dict(\n",
    "        timelines[label][\"timeline\"],\n",
    "        orient=\"index\",\n",
    "        columns=[label]\n",
    "    ))\n",
    "df = pd.concat(frames, axis=1)\n",
    "df.index = df.index.map(lambda s: s[5:10])\n",
    "df[-14:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppress manually the deaths in nursing homes that had not been reported previously.\n",
    "if country_code == \"FR\":\n",
    "    df.deaths.loc[\"04-02\"] = 4503\n",
    "    df.deaths.loc[\"04-03\"] = 5091\n",
    "    df.deaths.loc[\"04-04\"] = 5532\n",
    "    df.deaths.loc[\"04-05\"] = 5889\n",
    "    df.deaths.loc[\"04-06\"] = 6494\n",
    "    df.deaths.loc[\"04-07\"] = 7091\n",
    "    df.deaths.loc[\"04-08\"] = 7632\n",
    "    df.deaths.loc[\"04-09\"] = 8044\n",
    "    df.deaths.loc[\"04-10\"] = 8598\n",
    "    df.deaths.loc[\"04-11\"] = 8943\n",
    "    df.deaths.loc[\"04-12\"] = 9253\n",
    "    df.deaths.loc[\"04-13\"] = 9588\n",
    "    df.deaths.loc[\"04-14\"] = 10129\n",
    "    df.deaths.loc[\"04-15\"] = 10643\n",
    "    df.deaths.loc[\"04-16\"] = 11060\n",
    "    df.deaths.loc[\"04-17\"] = 11478\n",
    "    df.deaths.loc[\"04-18\"] = 11842\n",
    "    df.deaths.loc[\"04-19\"] = 12069\n",
    "    df.deaths.loc[\"04-20\"] = 12513\n",
    "    df.deaths.loc[\"04-21\"] = 12900\n",
    "    df.deaths.loc[\"04-22\"] = 13236\n",
    "    df.deaths.loc[\"04-23\"] = 13547\n",
    "    df.deaths.loc[\"04-24\"] = 13852\n",
    "    df.deaths.loc[\"04-25\"] = 14050\n",
    "    df.deaths.loc[\"04-26\"] = 14202\n",
    "    df.deaths.loc[\"04-27\"] = 14497\n",
    "    df.deaths.loc[\"04-28\"] = 14810\n",
    "    df.deaths.loc[\"04-29\"] = 15053\n",
    "    df.deaths.loc[\"04-30\"] = 15244\n",
    "    df.deaths.loc[\"05-01\"] = 15369\n",
    "    df.deaths.loc[\"05-02\"] = 15487\n",
    "    df.deaths.loc[\"05-03\"] = 15583\n",
    "    df.deaths.loc[\"05-04\"] = 15826\n",
    "    df.deaths.loc[\"05-05\"] = 16060\n",
    "    df.deaths.loc[\"05-06\"] = 16237\n",
    "    df.deaths.loc[\"05-07\"] = 16386\n",
    "    df.deaths.loc[\"05-08\"] = 16497\n",
    "    df.deaths.loc[\"05-09\"] = 16573\n",
    "    df.deaths.loc[\"05-10\"] = 16642\n",
    "    df.deaths.loc[\"05-11\"] = 16820\n",
    "    df.deaths.loc[\"05-12\"] = 17003\n",
    "    df.deaths.loc[\"05-13\"] = 17101\n",
    "    df.deaths.loc[\"05-14\"] = 17224\n",
    "    df.deaths.loc[\"05-15\"] = 17342\n",
    "    df.deaths.loc[\"05-16\"] = 17412\n",
    "    df.deaths.loc[\"05-17\"] = 17466\n",
    "    df.deaths.loc[\"05-18\"] = 17589\n",
    "    df.deaths.loc[\"05-19\"] = 17714\n",
    "    df.deaths.loc[\"05-20\"] = 17812\n",
    "    df.deaths.loc[\"05-21\"] = 17870\n",
    "    df.deaths.loc[\"05-22\"] = 17944\n",
    "    df.deaths.loc[\"05-23\"] = 17987\n",
    "    df.deaths.loc[\"05-24\"] = 18022\n",
    "    df.deaths.loc[\"05-25\"] = 18112\n",
    "    df.deaths.loc[\"05-26\"] = 18195\n",
    "    df.deaths.loc[\"05-27\"] = 18260\n",
    "    df.deaths.loc[\"05-28\"] = 18326\n",
    "    df.deaths.loc[\"05-29\"] = 18387\n",
    "    df.deaths.loc[\"05-30\"] = 18444"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = df.plot(\n",
    "    kind=\"bar\",\n",
    "    figsize=(16, 8),\n",
    "    grid=True,\n",
    "    width=1,\n",
    "    logy=True,\n",
    "    fontsize=13,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Smoothed deltas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The derivatives of the previous curves are smoothed by averaging the daily numbers of cases (deaths / confirmed) over a period of 4 days. A linear derivative corresponds to a polynomial increase (not an exponential one)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05-17</th>\n",
       "      <td>123.0</td>\n",
       "      <td>314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-18</th>\n",
       "      <td>125.0</td>\n",
       "      <td>874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-19</th>\n",
       "      <td>98.0</td>\n",
       "      <td>641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-20</th>\n",
       "      <td>58.0</td>\n",
       "      <td>237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-21</th>\n",
       "      <td>74.0</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-22</th>\n",
       "      <td>43.0</td>\n",
       "      <td>319.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-23</th>\n",
       "      <td>35.0</td>\n",
       "      <td>-105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-24</th>\n",
       "      <td>90.0</td>\n",
       "      <td>307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-25</th>\n",
       "      <td>83.0</td>\n",
       "      <td>-279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-26</th>\n",
       "      <td>65.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-27</th>\n",
       "      <td>66.0</td>\n",
       "      <td>3265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-28</th>\n",
       "      <td>61.0</td>\n",
       "      <td>507.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-29</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       deaths  confirmed\n",
       "05-17   123.0      314.0\n",
       "05-18   125.0      874.0\n",
       "05-19    98.0      641.0\n",
       "05-20    58.0      237.0\n",
       "05-21    74.0      339.0\n",
       "05-22    43.0      319.0\n",
       "05-23    35.0     -105.0\n",
       "05-24    90.0      307.0\n",
       "05-25    83.0     -279.0\n",
       "05-26    65.0      157.0\n",
       "05-27    66.0     3265.0\n",
       "05-28    61.0      507.0\n",
       "05-29    57.0     1800.0\n",
       "05-30     NaN        NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltas = df.diff().shift(-1)\n",
    "deltas[-14:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05-17</th>\n",
       "      <td>109.857143</td>\n",
       "      <td>296.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-18</th>\n",
       "      <td>101.571429</td>\n",
       "      <td>317.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-19</th>\n",
       "      <td>101.571429</td>\n",
       "      <td>441.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-20</th>\n",
       "      <td>92.285714</td>\n",
       "      <td>370.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-21</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>332.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-22</th>\n",
       "      <td>82.142857</td>\n",
       "      <td>393.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-23</th>\n",
       "      <td>79.428571</td>\n",
       "      <td>374.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-24</th>\n",
       "      <td>74.714286</td>\n",
       "      <td>373.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-25</th>\n",
       "      <td>68.714286</td>\n",
       "      <td>208.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-26</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>139.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-27</th>\n",
       "      <td>65.142857</td>\n",
       "      <td>571.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-28</th>\n",
       "      <td>63.285714</td>\n",
       "      <td>595.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-29</th>\n",
       "      <td>65.285714</td>\n",
       "      <td>807.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           deaths   confirmed\n",
       "05-17  109.857143  296.428571\n",
       "05-18  101.571429  317.285714\n",
       "05-19  101.571429  441.142857\n",
       "05-20   92.285714  370.571429\n",
       "05-21   86.000000  332.285714\n",
       "05-22   82.142857  393.857143\n",
       "05-23   79.428571  374.142857\n",
       "05-24   74.714286  373.142857\n",
       "05-25   68.714286  208.428571\n",
       "05-26   64.000000  139.285714\n",
       "05-27   65.142857  571.857143\n",
       "05-28   63.285714  595.857143\n",
       "05-29   65.285714  807.428571\n",
       "05-30         NaN         NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 7\n",
    "rolling_deltas = pd.Series.rolling(deltas, window).mean()\n",
    "rolling_deltas[\"03-15\":].plot(figsize=(16, 16),grid=True,fontsize=13, ylim=(0, 800))\n",
    "rolling_deltas[-14:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.4"
  },
  "nbTranslate": {
   "displayLangs": [
    "fr"
   ],
   "hotkey": "alt-t",
   "langInMainMenu": true,
   "sourceLang": "fr",
   "targetLang": "cn",
   "useGoogleTranslate": true
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}