Covid China - Hubei - Shanghai.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieving active cases in Shanghai & China excl.Hubei\n",
    "\n",
    "The purpose of this notebook is to draw an evolution of **active** cases\n",
    "(i.e. *confirmed* - *cured* - *dead*) **relative** to a given reference date in 2 locations:\n",
    "* China excluding Hubei\n",
    "* Shanghai\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Getting Data\n",
    "\n",
    "### 1a. Data extraction and cleaning\n",
    "We first retrieve the data from a github repository with data on China & other countries.\n",
    "\n",
    "We have to perform some cleaning\n",
    "* Extracting data related to China\n",
    "* Only keeping data for country and provinces (i.e. \"city\" information is *null*)\n",
    "* Dropping useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as plticker\n",
    "\n",
    "REF_DATE = \"2020-01-26\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://github.com/canghailan/Wuhan-2019-nCoV/raw/master/Wuhan-2019-nCoV.csv\"\n",
    "whole_df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python37-32\\lib\\site-packages\\pandas\\core\\frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>province</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>suspected</th>\n",
       "      <th>cured</th>\n",
       "      <th>dead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47409</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47417</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47446</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>台湾省</td>\n",
       "      <td>385</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47448</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>香港特别行政区</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>336</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47450</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>澳门特别行政区</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2962 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  province  confirmed  suspected  cured  dead\n",
       "0      2019-12-01     China          1          0      0     0\n",
       "1      2019-12-01       湖北省          1          0      0     0\n",
       "3      2019-12-02     China          1          0      0     0\n",
       "4      2019-12-02       湖北省          1          0      0     0\n",
       "6      2019-12-03     China          1          0      0     0\n",
       "...           ...       ...        ...        ...    ...   ...\n",
       "47409  2020-04-11   宁夏回族自治区         75          0     75     0\n",
       "47417  2020-04-11  新疆维吾尔自治区         76          0     73     3\n",
       "47446  2020-04-11       台湾省        385          0     99     6\n",
       "47448  2020-04-11   香港特别行政区       1000          0    336     4\n",
       "47450  2020-04-11   澳门特别行政区         45          0     10     0\n",
       "\n",
       "[2962 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stats for China country as \"province\" set to \"na\"\n",
    "china = whole_df[whole_df[\"countryCode\"]==\"CN\"].fillna({\"province\":\"China\"})\n",
    "df = china[china[\"city\"].isnull()]\n",
    "df.drop([\"country\", \"countryCode\", \"cityCode\", \"provinceCode\", \"city\"], axis=\"columns\", inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1b. Data selection\n",
    "\n",
    "We can then keep data for:\n",
    "* China\n",
    "* Hubei\n",
    "* Shanghai\n",
    "\n",
    "We can then add the number of active cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>province</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>suspected</th>\n",
       "      <th>cured</th>\n",
       "      <th>dead</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46289</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>555</td>\n",
       "      <td>0</td>\n",
       "      <td>435</td>\n",
       "      <td>7</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46420</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>67803</td>\n",
       "      <td>0</td>\n",
       "      <td>64264</td>\n",
       "      <td>3219</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46913</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>China</td>\n",
       "      <td>83400</td>\n",
       "      <td>44</td>\n",
       "      <td>77980</td>\n",
       "      <td>3349</td>\n",
       "      <td>2071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47036</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>555</td>\n",
       "      <td>0</td>\n",
       "      <td>438</td>\n",
       "      <td>7</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47167</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>67803</td>\n",
       "      <td>0</td>\n",
       "      <td>64264</td>\n",
       "      <td>3219</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>349 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  province  confirmed  suspected  cured  dead  active\n",
       "0      2019-12-01     China          1          0      0     0       1\n",
       "1      2019-12-01     Hubei          1          0      0     0       1\n",
       "3      2019-12-02     China          1          0      0     0       1\n",
       "4      2019-12-02     Hubei          1          0      0     0       1\n",
       "6      2019-12-03     China          1          0      0     0       1\n",
       "...           ...       ...        ...        ...    ...   ...     ...\n",
       "46289  2020-04-10  Shanghai        555          0    435     7     113\n",
       "46420  2020-04-10     Hubei      67803          0  64264  3219     320\n",
       "46913  2020-04-11     China      83400         44  77980  3349    2071\n",
       "47036  2020-04-11  Shanghai        555          0    438     7     110\n",
       "47167  2020-04-11     Hubei      67803          0  64264  3219     320\n",
       "\n",
       "[349 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection = df[(df.province == \"China\") | (df.province == \"湖北省\") | (df.province == \"上海市\")]\n",
    "selection = selection.replace(\"湖北省\", \"Hubei\").replace(\"上海市\", \"Shanghai\")\n",
    "selection[\"active\"] = selection[\"confirmed\"] - selection[\"cured\"] - selection[\"dead\"]\n",
    "# selection.to_csv(\"China_Hubei_Shanghai.csv\")\n",
    "selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1c. Data computation\n",
    "\n",
    "We can then group by provinces and compute nb of active cases relative to a given date.\n",
    "\n",
    "We need to:\n",
    "1. Group data  by provinces\n",
    "2. Compute data for \"China excl. Hubei\"\n",
    "3. Remove data for \"China\" and \"Hubei\"\n",
    "4. Extract the values for reference date (e.g. 2020-01-26)\n",
    "5. Divide dataframe by these reference values to get relative values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>province</th>\n",
       "      <th>Shanghai</th>\n",
       "      <th>China excl. Hubei</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>1.215686</td>\n",
       "      <td>1.360305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.824427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>1.862745</td>\n",
       "      <td>2.353435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2.392157</td>\n",
       "      <td>2.917557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>2.549020</td>\n",
       "      <td>1.359542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>2.509804</td>\n",
       "      <td>1.368702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>2.470588</td>\n",
       "      <td>1.351908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>2.215686</td>\n",
       "      <td>1.347328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>2.156863</td>\n",
       "      <td>1.336641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "province    Shanghai  China excl. Hubei\n",
       "date                                   \n",
       "2020-01-26  1.000000           1.000000\n",
       "2020-01-27  1.215686           1.360305\n",
       "2020-01-28  1.470588           1.824427\n",
       "2020-01-29  1.862745           2.353435\n",
       "2020-01-30  2.392157           2.917557\n",
       "...              ...                ...\n",
       "2020-04-07  2.549020           1.359542\n",
       "2020-04-08  2.509804           1.368702\n",
       "2020-04-09  2.470588           1.351908\n",
       "2020-04-10  2.215686           1.347328\n",
       "2020-04-11  2.156863           1.336641\n",
       "\n",
       "[77 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_plot = selection.pivot(index=\"date\", columns=\"province\", values=\"active\").fillna(0)\n",
    "to_plot[\"China excl. Hubei\"] = to_plot[\"China\"] - to_plot[\"Hubei\"]\n",
    "to_plot.drop([\"China\", \"Hubei\"], axis=\"columns\", inplace=True)\n",
    "\n",
    "to_plot = to_plot / to_plot.loc[REF_DATE]\n",
    "to_plot.loc[REF_DATE:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Drawing a plot\n",
    "\n",
    "We can finally draw a plot of relative active cases along time.\n",
    "\n",
    "We will trim data before the reference date (where values would be too small) so we should start at 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loc = plticker.MultipleLocator(base=7.0) # We want to put a tick every seven days \n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.title(f\"Relative number Active cases related to {REF_DATE}\")\n",
    "plt.ylabel(\"% active cases\")\n",
    "axes = sns.lineplot(data=to_plot.loc[REF_DATE:])\n",
    "axes.xaxis.set_major_locator(loc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "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
}